Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception.

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.

That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.

By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).

At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.

Finding 1: Orchestration runs on model-provider platforms

Anthropic’s Claude leads; open frameworks are marginal

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.

The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.

Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.

Finding 2: Model gravity drives platform selection

The base model, not the tooling, decides the platform

We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.

Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.

Finding 3: The job is reliable multi-step execution

Enterprises just orchestration by whether it completes the work

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.

Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.

The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.

Finding 4: Consolidate, productionize, and build in-house

Three strategic moves are nearly tied for the year ahead

We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.

The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.

Finding 5: Nearly seven in 10 plan to switch — and the biggest group of movers has no shortlist 

The strategic change enterprises anticipate (previous finding) comes with vendor motion attached. Asked whether they plan to adopt a new, additional, or replacement agent orchestration platform in the next twelve months, more respondents are moving here than in any other layer we track.

Asked which platforms they are considering, the most common answer among those in motion is none yet: 29% of all respondents are evaluating without a shortlist, the largest single response after "not considering a change." Among named candidates, OpenAI leads at 16%, followed by LangChain/LangGraph at 12% and Anthropic at 7% — and notably, the independent frameworks draw roughly double their current usage footprint in forward consideration, the same pattern our security tracker found for specialist vendors. Read with this report's concentration and lock-in findings, the picture completes itself: the major model-platform providers hold roughly four-fifths of today's primary usage, vendor lock-in has become the leading fear, 96% anticipate a strategic change — and now the purchase intent to act on all of it, with the largest bloc of buyers still undecided. The most concentrated layer of the agentic stack is also, as of June, the least settled.

Finding 6: Investment flows to workflow tooling

Tooling and permissions lead the spend; monitoring trails

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

Finding 7: The control plane will be hybrid — and lock-in is why

Enterprises expect to split control between providers and their own layer

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.

Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.

The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.

Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

Finding 8: The chatbot trap — most “agents” aren’t agents yet

Enterprises admit most deployments are still chatbot wrappers

We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.

This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.

Finding 9: Fiscal control is still reactive

Only a minority can stop a runaway agent before the bill arrives

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.

More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.

It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.

The bottom line: The layer is real; most of the agents aren't yet

Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing — for now — on model-provider platforms, which collectively hold roughly four-fifths of primary usage, chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the standardization is provisional: 68% plan to adopt a new, additional, or replacement orchestration platform within twelve months — the highest switching intent of any layer we track — and the largest group of those movers has not yet shortlisted a candidate. Today's concentration describes where enterprises are, and visibly does not describe where they intend to stay.

But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed "agents" are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The questions for subsequent waves are whether the deployed reality closes the gap on the ambition — and, with nearly seven in ten buyers in motion and most of them undecided, which platforms the settled stack finally lands on.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.

The enterprise AI industry has a math problem. Cisco data shows 85% of enterprises are piloting AI agents, but only 5% have shipped them to production. At VB Transform 2026 on Tuesday, Bryan Silverthorn, Director of AGI Autonomy at Amazon, explained why that gap persists — and why the answer isn't better benchmarks.

Silverthorn, who joined Amazon through its acquisition of Adept AI and now leads multimodal agent training inside the company's AGI lab, argued that reliability must be broken into four distinct dimensions: consistency, robustness, predictability, and safety — a framework he credits to research from Princeton.

"It unpacks different factors that I see tangled together in almost every eval I've ever seen," he said.

Why AI agents pass internal evals but fail real customers in production

The framework matters because agents routinely ace internal evaluations and then collapse in the wild. Silverthorn described a customer that deployed an agent for software QA involving serial number extraction from screens. It worked flawlessly for two months — then began intermittently reading wrong numbers. The culprit: the underlying vision encoder behaved differently depending on where the serial number appeared on screen, and a software change imperceptible to humans triggered the failure.

The lesson, Silverthorn said, is about measurement, not just models. "The models have to be better. Obviously, we're working hard on making the models better," he said. But the deeper takeaway, he added, is that teams need to identify their dimensions of variability and match measurement rigor to the stakes of the application. VentureBeat's own proprietary research, presented before the session, reinforces the point: half of surveyed companies shipped agents that passed internal evals but failed real customers, and enterprises overwhelmingly track uptime while ignoring accuracy — checking the pulse without checking the diagnosis. A related finding underscored how few guardrails exist: most enterprises default to the model makers' own evaluations and little else, leaving their testing strategy, as I described it on stage, a coin flip between trusting the vendor and trusting nothing.

Inside Amazon's 'intern' framework for managing autonomous AI agents

Silverthorn's most memorable prescription was cultural, not technical. Inside Amazon's AGI lab, researchers literally call their agents "interns" — as in, "I'll have my intern talk to your intern." The joke carries a serious operational philosophy. Agents, like interns, are powerful but occasionally clueless, capable of amazing work and spectacular derailment.

Managing them, he argued, requires management skills rather than software skills: asking what could go wrong, adding backups and undo capabilities, and consciously deciding what risk you can accept. "You can ask the intern, 'Hey, what might you do wrong here? How might you mitigate your negative outcomes?'" he said. Amazon's lab has embraced that trade-off, accepting agents occasionally running the wrong experiment in exchange for research velocity — including one agent running experiments around the clock on its own high-level research plan.

What enterprise leaders should do before deploying agents at scale

Silverthorn was candid about the limits of today's technology. Self-improving AI remains "a loaded term," he said — Amazon uses AI to improve its models constantly, but fully autonomous self-improvement is distant. Computer use remains a core focus of his lab, with a commercial trucking customer already using browser automation to stitch together warranty claims across fragmented systems**, though he stressed that no future agent will rely on computer use alone — it will work alongside MCP, APIs, and other tools to complete end-to-end workflows**. And LLM-as-judge techniques, while promising, are just one of several strategies for aligning agent capability with acceptable risk.

For enterprises stuck in pilot purgatory, the path forward starts with a mindset shift: stop asking whether your agent can do something impressive once, and start asking whether it can do it correctly a thousand times in a row.

In other words, the enterprises that escape the 85% ceiling won't be the ones with the smartest agents. They'll be the ones with the best managers.

Enterprises looking to move more of their agentic AI workloads to open weights models they can customize, control and run on-premises or in virtual private clouds have a strong new contender to consider.

Today, Thinking Machines—the highly capitalized American AI startup founded by former OpenAI CTO Mira Murati—released Inkling, its first major language model under an enterprise-friendly Apache 2.0 open source license, and it boasts high, if sub state-of-the-art, performance for open weights models on third-party benchmarks, specifically software engineering (77.6% on SWE-bench Verified, where it beats fellow U.S. open rival Nvidia Nemotron 3's 71.9%) and voice understanding (91.4% on VoiceBench compared to 94.4% for Gemini 3.1 Pro on high reasoning effort).

Another differentiator: Thinking Machines notes that Inkling was designed "to answer directly on topics that may be subject to censorship," offering enterprises concerned about factual outputs, irrespective of controversy or sensitivity, a more trustworthy option.

Coming in at 975 billion total parameters, Inkling is a natively multimodal, open-weights Mixture-of-Experts (MoE) system capable of reasoning across text, images, and audio. The weights are already available on Hugging Face and the company's own model training application programming interface (API), Tinker.

Designed to balance cost against performance through a novel "controllable thinking effort" mechanism, the model represents a significant departure from the black-box scaling strategies of frontier competitors.

Alongside the flagship model, Thinking Machines also announced a preview of Inkling-Small, a lighter 276-billion-parameter alternative optimized for workloads where low latency and cost are paramount.

Benchmarks Show a Powerful, High-End, Sub State-of-the-Art Model

While Inkling is a formidable multimodal engine, it lands in a fiercely competitive 2026 open-weight landscape characterized by highly specialized MoE architectures. Rather than attempting to dominate every leaderboard, Thinking Machines explicitly designed Inkling—with 975 billion total and 41 billion active parameters—as a broad, balanced generalist.

For example, it comes in near the middle high-end of benchmark performance 1257 on Design Arena’s Agentic Web Dev leaderboard measuring human scores of frontend web design.

But China’s leading AI labs have produced models with elite reasoning and coding capabilities, posing a stiff challenge to Inkling's generalist approach and ultimately outperforming it on general and coding benchmarks.

• GLM 5.2: Widely considered the top open-weight reasoning model available in the benchmark set, GLM 5.2 outperforms Inkling on pure coding, agentic, and complex reasoning tasks. It scores 62.1% on SWEBench Pro (Public) compared to Inkling’s 54.3%, and a massive 82.7 on Terminal Bench 2.1 against Inkling’s 63.8. GLM 5.2 also holds the edge in text-only reasoning, scoring 40.1% on HLE (text only) versus Inkling's 30.0%.

• DeepSeek V4 Pro: DeepSeek maintains an edge in several strict coding and factuality domains, beating Inkling on SWEBench Verified (80.6% vs. 77.6%) and SimpleQA Verified (57.0% vs. 43.9%). However, Inkling successfully overtakes DeepSeek V4 Pro in mathematical problem-solving, achieving 97.1% on AIME 2026 compared to DeepSeek's 96.7%.

• Kimi K2.6: This model outpaces Inkling across multiple technical benchmarks, delivering higher scores on GPQA Diamond (91.1% vs. 87.9%), BrowseComp (83.2% vs. 77.1%), and HLE with tools (54.0% vs. 46.0%). Yet Inkling proves more resilient on general chat instruction following, scoring 79.8% on IFBench compared to Kimi K2.6's 76.0%.

Against its primary U.S.-based open-weight competition, Inkling demonstrates strong parity and frequent superiority.

• Nemotron 3 Ultra: Inkling consistently outperforms this U.S. rival across reasoning and coding. Inkling posts 97.1% on AIME 2026 and 77.6% on SWEBench Verified, beating Nemotron's 94.2% and 70.7%, respectively. Furthermore, Inkling significantly leads in agentic workflows, scoring 74.1% on MCP Atlas against Nemotron's 44.7%.

When compared to closed-source juggernauts like Claude Fable 5, GPT 5.6 Sol, and Gemini 3.1 Pro, Inkling trails in peak reasoning and software engineering autonomy, but remains highly competitive in multimodality.

• Coding and Reasoning: Closed models maintain a commanding lead. Claude Fable 5 (max) hits 95.0% on SWEBench Verified and 53.3% on HLE (text only), far outpacing Inkling's 77.6% and 30.0%. GPT 5.6 Sol dominates Terminal Bench 2.1 with an 89.5, easily clearing Inkling's 63.8.

• Native Multimodality: Inkling's native visual and audio capabilities hold their own. On the MMMU Pro (Standard 10) vision benchmark, Inkling's 73.3% is competitive, though trailing Claude Fable 5's 84.2% and GPT 5.6 Sol's 83.0%. In audio processing, Inkling scores a highly respectable 77.2% on MMAU, keeping it within striking distance of Gemini 3.1 Pro's 82.5%.

If an enterprise workflow demands elite software engineering autonomy or the highest bounds of text-only reasoning, models like GLM 5.2 or proprietary systems like Claude Fable 5 maintain the edge.

However, Inkling carves out a unique and highly defensible position: it is the most capable open-weight foundation model that natively fuses text, vision, and audio, while simultaneously offering developers direct programmatic control over the cost-to-performance ratio.

The Shift from Static Reasoning to Controllable Thinking

Rather than attempting to build a singular "god model" optimized strictly for state-of-the-art benchmark domination, Thinking Machines engineered Inkling for adaptability and efficiency in real-world workflows.

The standout feature of this release is Inkling's "controllable thinking effort." Developers can programmatically adjust the model's reasoning budget—scaling from 0.2 to 0.99—to dictate how hard the AI should "think" before generating an output.

As the company noted, "Inkling's continuous thinking effort lets you pick your point on the cost/performance curve—reaching the same score with a fraction of the tokens".

In practical terms, this allows enterprises to deploy Inkling with lower token expenditure for simpler tasks, while cranking up the compute overhead for complex, multi-step reasoning challenges. However, by keeping the thinking effort lower and generating fewer tokens, the cost-conscious enterprise can achieve high quality results and performance on simple tasks while spending less money, or, in the case of those running models locally, less costs on energy and compute resources.

During the model’s large-scale reinforcement learning (RL) training over 30 million rollouts, researchers observed an emergent phenomenon they called "chain of thought condensation". Over time, Inkling naturally learned to compress its internal reasoning steps—dropping grammatical overhead and connectives—while reaching the same accurate conclusions, resulting in drastically reduced latency.

Epistemics and Censorship Resistance

A notable element of Thinking Machines' release is its explicit focus on the model's epistemics—specifically its calibration, instruction following, and resistance to censorship.

In an ecosystem where open-weight models adopt either overly restrictive safety guardrails or echo state-aligned ideological talking points, Inkling was intentionally trained to answer directly on politically sensitive or heavily censored topics.

To validate this approach, Thinking Machines submitted Inkling to the Propaganda and Censorship Eval developed by AI startup Cognition. According to the published findings, Inkling demonstrated "strong patterns of censorship non-compliance," effectively resisting ideological capture or boilerplate refusals when presented with sensitive subjects.

Despite its resistance to censorship, the model maintains a robust defense against genuinely malicious, dangerous, or illegal queries. On the StrongREJECT benchmark—which tests responses to unambiguous harmful requests—Inkling scored 98.6%, placing it in line with strict frontier safety standards. Furthermore, on the FORTRESS benchmark, Inkling successfully navigated the line between safety and over-refusal: it achieved a 78.0% refusal rate on adversarial queries (such as those involving weapons, cyberattacks, or violence) while maintaining a 95.9% compliance rate on benign, look-alike queries.

Thinking Machines noted that typical open-weight vulnerabilities remain within the architecture. Internal safety evaluations revealed an "occasional tendency to comply with role-play and indirectly framed prompts concerning harmful topics". The company advised enterprise developers to treat the model's built-in refusals as just one layer of security, recommending the downstream deployment of external moderation tools—such as Llama Guard—to filter adversarial jailbreaks and enforce use-case-specific safety policies at the application level.

Under the Hood: Architecture and Multimodality

Inkling's scale is staggering, yet sparse. The MoE architecture features 975 billion total parameters, but only 41 billion parameters are active during any given token generation. It supports a massive context window of 1 million tokens and diverges from typical transformer models by using relative positional embeddings instead of the industry-standard Rotary Positional Embedding (RoPE).

True to the company's foundational vision, Inkling was trained from scratch to be natively multimodal. Unlike models that rely on bolted-on external encoders, Inkling uses an encoder-free early fusion approach. It directly ingests audio as discrete dMel spectrograms and visual data as 40x40 pixel patches via a hierarchical multi-layer perceptron (hMLP), projecting all modalities into a shared hidden space.

Licensing: True Open-Source for the Enterprise

For enterprise IT teams and developers, the most disruptive aspect of Inkling may be its licensing. Inkling is released under the permissive Apache 2.0 license.

In an ecosystem where many so-called "open" models from Western labs are tethered to dual-use commercial licenses, acceptable use restrictions, or revenue caps, an Apache 2.0 designation makes Inkling a true open-source foundation. This gives developers the legal freedom to download, modify, integrate, and commercialize the model weights entirely royalty-free.

The model is readily deployable across major open-source inference libraries—including SGLang, vLLM, TokenSpeed, and llama.cpp—and comes with a native NVFP4 quantized checkpoint optimized for NVIDIA Blackwell systems.

Community Reactions: The Engineering Feat

The AI community's response has been swift, praising both the model's openness and the underlying engineering execution.

In a post on X, Thinking Machines co-founder John Schulman reflected on the rapid development cycle: "Inkling is out today, with open weights and in Tinker. It's been fun to watch this one come together: pretraining began last winter, and starting in mid-January a small team built up the coding, reasoning, and agentic training from there. We learned a lot building it, and I hope people find good uses for it."

Horace He, a researcher at Thinking Machines (previously from PyTorch), underscored the difficulty of the task in another post on X: "It truly takes a village to release a model, perhaps especially an open weights model. Actually doing the entire process from scratch, from data to pretraining to posttraining to actual release, gives a lot of appreciation for anyone who does it!"

The broader open-source ecosystem has also embraced the technical integrations. Lysandre Debut, the Chief Open-Source Officer at Hugging Face, shared his enthusiasm regarding the model's optimization in his own X post: "One thing I find quite striking is how much easier accelerating models has become... We replaced the model's causal Conv1D with the `causal-conv1d` kernel. One line changed, +4% tokens per second. We then replaced its attention implementation with FlashAttention-4. Another single change, another +11%. That's a total throughput improvement of about 15%, without changing the model architecture or retraining anything."

Tiezhen Wang, an ecosystem growth expert and ex-Googler, celebrated the release as a massive win for the open-source community, listing the model's impressive specifications on X, highlighting its "975B total, 41B active" size, "Native MTP support," and the highly coveted "Apache 2.0 license."

Background: The Road to Inkling

To understand the significance of Inkling, one has to look back at the rapid trajectory of Thinking Machines over the past 18 months.

When Mira Murati departed OpenAI in late 2024 to found Thinking Machines alongside industry veterans like John Schulman and Barret Zoph, the stated goal was to pivot away from building isolated autonomous agents. Instead, the company aimed to build flexible, multimodal systems designed for genuine human-AI collaboration and open science.

By July 2025, the startup had secured a historic $2 billion seed round led by Andreessen Horowitz at a $12 billion valuation. At the time, Murati promised the impending release of a product with a "significant open source component" to empower researchers and startups.

The company’s philosophy began coming into sharper focus in October 2025 with the launch of Tinker, a Python-based API for large language model fine-tuning that gave researchers granular control over training pipelines without the friction of distributed compute management.

That same month, Thinking Machines researcher Rafael Rafailov delivered a provocative critique of the AI industry at TED AI. He argued that the current trajectory of simply throwing more compute at models was fundamentally flawed, noting that today's systems take shortcuts—like wrapping code in try/except blocks—because they are trained strictly for task completion rather than genuine learning.

Rafailov posited that the first artificial superintelligence would not be a "god model," but rather a "superhuman learner" capable of meta-learning and internalizing abstractions. Inkling’s architecture—specifically its controllable thinking effort and its ability to organically compress its chain of thought during RL—feels like the first tangible realization of Rafailov's thesis.

In May 2026, the lab teased its technical prowess with the research preview of TML-Interaction-Small, a system that eliminated "turn-based" chat by processing inputs and outputs simultaneously in 200ms chunks. This "full-duplex" breakthrough proved the company could build highly responsive, natively multimodal models from scratch.

Now, with Inkling out in the wild, Thinking Machines has delivered on its foundational promises. By offering a massive, natively multimodal model under a true open-source license, they aren't just giving developers a new tool—they are attempting to fundamentally rewrite the economics and accessibility of frontier AI development.

Hundreds of enterprise leaders and technical experts packed the main ballroom of the luxurious Hotel Nia in Menlo Park this week for VB Transform 2026, the year's preeminent conference on using generative AI agents to drive business outcomes.

Rachad Alao, vice president of product engineering at the rising Canadian enterprise AI startup Cohere, joined VentureBeat CEO and editor-in-chief Matt Marshall for a fireside chat about building agentic systems without surrendering sensitive data, infrastructure control, or the ability to change vendors.

Alao, who previously led responsible AI and trust and safety engineering teams at Google and Meta, argued that AI sovereignty means more than downloading an open model or running an application behind a corporate firewall.

Asked how Cohere defines sovereignty, Alao pointed to organizations operating mission-critical systems, including banks, hospitals and governments.

“It is important to have very tight control on where the data resides, have tight control on the AI,” he said, adding that AI operations should take place in jurisdictions an organization understands or directly controls.

That extends from GPUs and private-cloud infrastructure through governance systems that route requests among models, as well as the connectors, search tools and agent frameworks acting on enterprise data.

“You want to have control on the entire stack,” Alao said.

Agent workloads could outrun falling token prices

Marshall challenged one of the central economic arguments for smaller, locally deployed models: Inference prices continue to fall rapidly, potentially weakening the case for optimizing every token.

Alao countered that total consumption is climbing even faster as enterprises move from relatively simple chatbots to agents that reason through problems, call tools, search internal systems and take multiple steps before returning an answer.

“Your token utilization is going exponentially up, because you’re dealing with more and more complex agentic use cases,” he said. Those workflows require “a lot of processing, thinking, tools interaction” to complete their objectives, he added.

Alao also drew a contrast between providers that bill customers according to token consumption and Cohere’s approach.

“If your whole way of charging customers is for token utilization, you want to maximize token utilization,” he said. “We do not sell our models and our platform that way.”

Instead, Alao said Cohere tries to help enterprises solve their hardest problems privately and securely while reducing unnecessary model usage. His prescription was straightforward: “Use the right model for the task at hand.”

Rather than sending every request to the largest available frontier model, enterprises should route work according to the intelligence required and the sensitivity or regulatory burden attached to the task.

Alao cited an unnamed Canadian bank that uses Cohere’s on-premises models for highly regulated workloads, while sending less sensitive tasks requiring greater intelligence through Cohere’s North platform to larger frontier models.

“So model routing can become super useful,” he said.

Smaller models for most enterprise work

Asked by an audience member how Cohere’s open-source North Mini Code, released last month, could compete against proprietary coding models, Alao acknowledged that larger frontier models may perform somewhat better on the hardest tasks.

But that advantage may not justify using them indiscriminately.

“For 80% of the use cases that they needed, this was a lot more effective, a lot cheaper,” Alao said of developers adopting the model.

Cohere’s North Mini Code runs on a single Nvidia H100 GPU and targets agentic software engineering, including terminal work, code review and tool use.

The company has also released Command A+, a 218-billion-parameter mixture-of-experts model with only 25 billion parameters active during each generation step.

Its compressed four-bit version reduces the hardware required for private deployment, while its Apache 2.0 license gives enterprises broad freedom to operate and modify it.

Search becomes part of the agent

Asked about Cohere’s longstanding work on embeddings and enterprise search, Alao said the field is moving beyond retrieving text and inserting it into a model’s context window.

“Today, the state of the art is around multimodal search,” he said. “It’s beyond just the text modality.”

Search across documents, images and other forms of information is becoming “an integral component of your agentic workflow,” Alao added, with the model deciding when and how to use retrieval like any other tool.

Asked what would persuade enterprises to move beyond bundled AI services from existing cloud providers, Alao returned to data control and portability.

“If you’re interested in sovereignty, you want to have more control on your data,” he said. Cohere’s governance layer, he added, lets customers route traffic to appropriate models, “breaking that vendor lock-in concern that a lot of our customers have.”

Organizations need to transform to meet the needs of agentic AI.

Meta VP of Engineering Barak Yagour opened his talk at VB Transform 2026 wearing a pair of Ray-Ban Meta AI glasses, a small sign of how far AI has already worked its way into physical life. His argument went further: enterprise infrastructure was built for humans, not for agents, and it's starting to show.

Yagour, who leads its data infrastructure organization, told the audience that agentic queries hitting Meta's data systems grew 30x in a single half, an inversion that he said is breaking assumptions the company spent two decades building around.

The shift is not confined to Meta. Automated traffic overtook human traffic on the internet last year, reaching 51% of the total, according to Imperva's 2025 Bad Bot Report. That traffic is also growing roughly eight times faster than human traffic, according to HUMAN Security's 2026 State of AI Traffic report. Yagour cited both figures to describe what he called an inflection point already underway inside his own organization.

Yagour framed the shift as an open question for infrastructure teams everywhere. "What happens to the infrastructure we've spent years building when agents and not humans become the main consumers of that," Yagour said. "That's the world we're stepping into."

Capacity, identity and velocity are breaking at once

Yagour said three assumptions are breaking simultaneously inside Meta's infrastructure: capacity, identity and velocity.

On capacity, the math no longer works the way engineering teams are used to. "One engineer used to mean one unit of load," he said. "Now one engineer spawns 10 agents, each spawning subagents. Your 1,000-person org can generate the load of 100,000 users practically overnight."

His answer is not to block agent traffic but to make infrastructure agent-aware, with dynamic controls that understand agent hierarchies, cost attribution that traces consumption back to the use case that spawned it, and throttling that adapts based on priority.

Identity is breaking, too. Yagour said an agent does not fit the categories infrastructure teams built access controls around. It is not a human user, it does not carry a badge and it is not a deployed service, yet it makes decisions on its own.

Velocity is the third assumption under strain. Yagour cited a company-reported figure that GitHub Copilot writes 46% of the average user's code, then noted that faster code generation does not make the rest of the pipeline faster.

"That code still needs to be built, tested, deployed, monitored," he said. "The agent writes the code in seconds, but your CI/CD pipeline doesn't get faster just because the machine is the author."

Trusted data environments keep agents inside guardrails

Data is where Yagour said the pressure from agents is most direct. 

"Data sits at the center of everything," he said, pointing to the decisions, products, recommender systems and next generation models it drives.

Meta is also rethinking how much autonomy to grant agents inside its own data systems. In February, the company shipped what Yagour called agentic data apps. Within three months, 63% of dashboards published across Meta were built using the new tooling, part of the same 30x rise in agentic queries Yagour cited earlier.

That growth raises a governance question. Human analysts have traditionally sat between raw data and business decisions, curating it and serving as an informal check on quality. Yagour said Meta wants to grant agents more independence on harder problems, but was direct about the risk. 

"Autonomy without governance is nothing but chaos," he said. That's why the company built what it calls trusted data environments, to preserve the human check as agents take on more of that work.

"Inside, the agent can explore data freely, but every output is traced back to its source and scrutinized. So you always know that the data shared back is trusted and governed," Yagour said.

Sensitive fields are masked before an agent can reach them, and every access request is evaluated in real time against what the agent is trying to reach, why and whether it is allowed. Yagour summarized the approach as exploring broadly while releasing narrowly.

Reasoning models are rewriting the data layer

Meta's models are also demanding more from data as they shift from correlation to reasoning. 

"Reasoning is data hungry," Yagour said. 

Pattern matching works on sparse, summarized signals. Reasoning demands the full behavioral history, every interaction across every surface over time. Yagour pointed to two shifts already underway inside Meta's infrastructure to keep up.

Real-time streaming is replacing batch ETL for ranking pipelines. A pipeline that takes 24 hours to run is not viable when a model is reasoning about a user's current intent. Yagour said real-time streaming, not batch extract-transform-load processing, is becoming the backbone of Meta's ranking and recommendation systems.

Storage is becoming schema-aware to stop GPU starvation. Meta previously stored user data as opaque blobs with no awareness of what the data contained, which Yagour said led to heavy overfetching and idle GPU capacity. The company is now building storage that understands what it holds, pulling only the columns and time ranges a given query needs. Yagour said Meta is building toward 500 million queries per second and a petabyte per second of throughput for training data reads.

That data feeds directly into how Meta's recommendation systems behave. Yagour said 42% of Instagram users have told the company they want to fundamentally change the algorithm, not adjust a single session or setting. Meta's response is what Yagour called fully conversational recommendations, where a user tells the system what they want more of and it reasons about intent rather than matching on keywords. Yagour said the same search term, soccer, would return different results for a casual fan looking for highlights than for a club athlete seeking training drills, because the system would reason about which one is asking.

Yagour described the three threads of his talk, agents, data and recommendations, as reinforcing each other rather than moving independently. 

"Agents make data more accessible. Better data makes reasoning. Reasoning creates new demands that push agents and infrastructure forward," he said. "This isn't linear; it's a flywheel."

During the Q&A, an audience member asked whether Meta's push toward more intelligent infrastructure signals the end of traditional file systems in favor of newer neural storage approaches, and whether agents will keep using SQL as their interface to data the way humans do. Yagour said Meta is experimenting at every level, including questioning whether SQL is the right interface for agents at all, and that storage at Meta's scale already operates in the multi-digit exabyte range and needs to keep expanding.

Yagour closed his talk with the timeline he believes the industry is working against. "We spent 20 years building infrastructure for humans. We have maybe 20 months to rebuild the whole thing for a world where humans and agents co-create at scale," Yagour said. "The window is open, but it won't stay open for long."

1Password on Tuesday launched AI Spend and Consumption Management, a new capability embedded in its SaaS Manager platform that gives IT and finance teams a unified, real-time view of how their organizations consume and spend on AI services from vendors including Anthropic, Cursor, and OpenAI.

The move marks the latest strategic expansion for a company that built its reputation on password management for consumers and, over the past three years, has aggressively repositioned itself as a broader identity security and SaaS governance platform for enterprise buyers. With this release, 1Password is staking a claim in one of enterprise technology's newest and most chaotic budget categories: the consumption-based cost of large language models.

"Executives want teams to build faster with AI, but that speed is creating a new kind of spending pressure," Greg Henry, 1Password's chief financial officer, said in an exclusive interview with VentureBeat. "Developers are consuming tokens at a pace that traditional budgets weren't built to manage, and IT and finance teams are being asked to forecast and justify AI investments without a clear view of what's actually driving costs."

The product, now in public preview with broad availability planned for fall 2026, connects directly to vendor admin APIs to pull token-level consumption data daily. It normalizes that data across providers into a single dashboard and allows organizations to set vendor-level spend limits, configure threshold-based alerts via Slack and email, and break down usage by team, user, vendor, and model.

Why traditional software budgets can't keep up with AI token pricing

The core challenge 1Password is targeting is structural. Traditional SaaS pricing operates on a per-seat, per-year model that is easy to budget and reconcile. AI pricing does not. Every API call to Claude, GPT-5.6, or a Cursor-powered coding assistant consumes tokens, and the cost of those tokens varies by model, by input versus output, and by the complexity of the task. A single engineering team running agentic workflows can burn through a prepaid token budget in weeks — and the finance team may not notice until the invoice arrives.

Henry drew a sharp analogy to a problem enterprises have already lived through once. "Consumption-based pricing isn't new," he said. "We saw it arrive with cloud infrastructure, and it took years to build the tools and disciplines to manage it. AI is the next version of that shift."

That comparison resonates across the industry. When Amazon Web Services, Microsoft Azure, and Google Cloud popularized consumption-based pricing for compute and storage in the 2010s, enterprises initially lacked the tooling to monitor and optimize their cloud bills. That gap spawned an entire FinOps ecosystem — companies like CloudHealth, Spot.io, and Apptio built multi-billion-dollar businesses helping organizations understand what they were spending on cloud and why. Henry is explicitly betting that AI token spend will follow the same trajectory, and that organizations that fail to build visibility now will end up, as he put it, "paying far more than they needed to, for far longer than they should have."

The scale of the coming wave lends credibility to that bet. Goldman Sachs has estimated that token consumption from AI agents alone will grow 24 times by 2030, a projection driven by the expectation that autonomous AI systems will increasingly execute multi-step workflows — booking travel, writing and deploying code, managing customer service interactions — that generate vastly more API calls than a human sitting at a chat interface.

How 1Password's new dashboard tracks every token across Anthropic, Cursor, and OpenAI

The new capability extends 1Password SaaS Manager's existing foundation of application discovery, license management, and spend analytics. It is not a standalone product. Existing SaaS Manager customers can activate it by connecting their supported AI vendor API keys, at which point consumption data flows into a dedicated AI Consumption Management dashboard. Henry confirmed that there is no separate product or add-on fee: "AI Spend and Consumption Management is available to all 1Password SaaS Manager customers."

The system provides four core functions. First, it aggregates token usage and spend across Anthropic, Cursor, and OpenAI into a single, normalized view — eliminating the need to toggle between three separate vendor dashboards with three different reporting formats. Second, it enables budget controls: organizations can set vendor-level spend limits, configure percentage-based thresholds, and receive automated alerts when prepaid balances approach depletion. Third, it disaggregates consumption by team, user, vendor, and model, allowing finance and IT to understand not just how much is being spent, but where and by whom. Fourth, it situates AI spend within the broader SaaS portfolio, helping organizations see how token costs relate to their total software investment.

Notably, the system captures consumption regardless of whether a human or an AI agent generated it. "Token consumption is captured at the API level regardless of whether a human or an agent is generating it," Henry explained. "Organizations get the total consumption picture, including the spikes that agent loops can create, which can be some of the hardest usage to catch before it becomes a problem."

That agent-level visibility matters because autonomous AI systems can generate runaway costs in ways that human users typically cannot. An agentic coding assistant stuck in a retry loop, for example, can consume thousands of dollars in tokens in minutes — with no human in the loop to notice. For now, the product alerts but does not enforce. When asked whether 1Password will eventually give organizations the ability to automatically cut off spending when a threshold is crossed, Henry said the company is "actively evaluating" automatic enforcement but emphasized that visibility must come first: "You can't enforce what you can't see."

The choice of launch partners reveals where enterprise AI budgets are under the most pressure

The decision to start with Anthropic, Cursor, and OpenAI — rather than casting a wider net — reflects where enterprise AI adoption and budget strain are most concentrated right now. Henry said the choice was driven entirely by customer demand. "Anthropic, Cursor, and OpenAI are where we're seeing the highest adoption, and where token consumption can move fast and get ahead of the teams responsible for managing it," he said. The company plans to add additional vendors based on customer demand, API availability, and budget impact, though it has not committed to a specific timeline or vendor list.

The inclusion of Cursor alongside the two major foundation model providers is telling. Cursor, an AI-powered code editor that has rapidly gained traction among developers, represents a category of AI tool where consumption is particularly difficult to forecast. Unlike a chatbot interface where a user consciously types a prompt, Cursor integrates AI suggestions directly into the development workflow, generating token consumption continuously as developers write code. That ambient, always-on consumption pattern makes it especially prone to budget overruns.

Henry also addressed who inside an organization should actually own this problem — and acknowledged that the honest answer right now is no one. "When spend is fragmented across vendor dashboards and finance teams are reconciling it monthly, you're always behind," he said. "AI spend can't be treated as a finance-only or IT-only problem." He noted that the pricing differences between models have become significant enough that the choice of which AI model a team uses is now a meaningful financial decision, one that is pulling CFOs into conversations with IT, product, and engineering leaders "in ways they never had to before."

Steve May, director of IT at ServiceTrade, a 1Password customer that has been using the capability, said it addressed a concrete planning gap. "Forecasting tools for AI consumption and spend was one of our biggest gaps in planning because we didn't have a reliable way to track it," May said. He added that the visibility has "prevented overages that would have cost far more to fix after the fact."

Where 1Password fits in the fast-consolidating SaaS management market

1Password is not the only company racing to solve the AI cost management problem, but the competitive landscape is still fragmented and the category is far from mature.

Zylo, a SaaS management platform that Gartner has also recognized as a leader in the space, published its 2026 SaaS Management Index in January showing that AI-native application spend surged 393% year over year in organizations with more than 10,000 employees and 108% overall. Zylo's data also revealed that ChatGPT has become the most expensed application in enterprise environments, highlighting how AI tools are entering organizations through employee credit cards and expense reports — outside formal procurement and governance workflows. Zylo has added its own token-level cost tracking for AI vendors including Anthropic, OpenAI, Cursor, and Perplexity.

Meanwhile, according to a comparison published by Coommit in May, Vendr — which focuses more on SaaS negotiation than discovery — tracks AI tools at the contract level but does not yet offer consumption-level visibility. And the FinOps Foundation reported in its 2026 State of FinOps survey that 98% of organizations now actively manage AI costs, up from just 31% in 2024. The broader SaaS management market is also consolidating rapidly. In May, Deel acquired Sastrify, a German SaaS management vendor, and began folding it into its HR platform — a signal that SaaS management capabilities are increasingly being absorbed into adjacent enterprise platforms rather than remaining standalone products.

1Password's approach differs from pure-play SaaS management competitors in one important respect: it is building AI cost management on top of an identity security platform, not a FinOps or procurement tool. The company's SaaS Manager product grew out of its 2025 acquisition of Trelica, a UK-based SaaS access management startup whose technology enabled the discovery of unsanctioned applications — so-called shadow IT. As BetaKit reported at the time of that deal, 1Password co-CEO Jeff Shiner described Trelica as "a pioneer in modern SaaS access management" and said the acquisition would accelerate 1Password's Extended Access Management product roadmap by more than a year. CRN noted that Trelica brought more than 300 SaaS integrations to the platform. That identity-first lineage gives 1Password a natural advantage in connecting spend data to specific users and teams — a linkage that matters when the question shifts from "how much are we spending on AI?" to "who is spending it, and is it delivering value?"

From password manager to platform company: 1Password's $6.8 billion bet on enterprise identity

The launch raises a question that Henry addressed head-on: whether a company that started as a consumer password manager can credibly compete in enterprise AI cost management.

"It doesn't feel like a stretch to us. It feels like a natural progression," he said. "For more than 20 years, 1Password has evolved alongside how our customers work. We started by protecting passwords. Then we helped organizations manage secrets, control access, and get visibility into the applications their teams rely on."

The company's evolution has been rapid. 1Password raised a $620 million Series C in January 2022 led by ICONIQ Growth, reaching a $6.8 billion valuation — at the time, the largest funding round ever raised by a Canadian company, according to Crunchbase. The round also attracted celebrity investors including Ryan Reynolds, Scarlett Johansson, and Robert Downey Jr. As of early 2025, BetaKit reported that 1Password had surpassed $250 million in annual recurring revenue, with B2B sales accounting for nearly three-quarters of total revenue and the company claiming to be cash-flow positive.

In May 2024, 1Password launched Extended Access Management, a platform designed to secure sign-ins across both managed and unmanaged applications and devices. That same year, it acquired Kolide for device trust and, in early 2025, Trelica for SaaS discovery. In June 2026, Gartner named 1Password a Leader in its Magic Quadrant for SaaS Management Platforms. According to 1Password's own blog post on the recognition, its SaaS Manager now supports over 400 integrations and provides visibility into a library of more than 40,000 pre-populated application profiles. Each step has moved the company further from its consumer roots and deeper into enterprise infrastructure. The AI Spend and Consumption Management launch extends that trajectory into financial operations territory — a domain where 1Password will compete not only with SaaS management vendors but potentially with dedicated FinOps platforms and the AI vendors' own billing dashboards.

Why high AI token consumption doesn't always mean wasted money

Perhaps the most revealing part of Henry's commentary concerns what organizations should actually do with the consumption data once they have it. He pushed back forcefully against the assumption that high token consumption automatically signals waste.

"A team burning through tokens may be building something genuinely valuable," he said. "A lower-usage project might not be moving the business forward at all. What matters is whether that consumption is producing enough business value to justify the spend."

Henry drew a distinction between personal productivity — "having a bot summarize your meeting or draft a quick email" — and genuine business outcomes. "What organizations need to see is where consumption is actually driving revenue, efficiency, or something that moves the needle."

That framing positions AI Spend and Consumption Management not just as a cost-cutting tool but as a decision-support system for AI investment allocation. If a CFO can see that one engineering team's heavy Claude usage is powering a product feature that drives revenue, while another team's OpenAI spend is funding low-value internal automation, the organization can reallocate budget accordingly rather than imposing across-the-board cuts.

"When costs rise faster than expected, the instinct is to cut," Henry said. "But most organizations can't yet tell which teams, models, or tools are responsible for the increase, so they end up cutting across the board rather than directing investment toward the AI projects that are actually delivering business value. Blunt cuts on a technology you're counting on for competitive advantage is not a management strategy, it's a missed opportunity."

The next enterprise budget crisis is already here — and it's priced per token

The product's current scope — three vendor integrations, alerting but not enforcement — is clearly a starting point. Henry signaled that automatic spend limits are on the roadmap and that additional vendor integrations will follow based on customer demand.

But the broader trajectory he described suggests 1Password sees this launch as a wedge into a much larger opportunity. "As traditional SaaS products add AI capabilities, their pricing models are going to follow," he said. "Organizations that build visibility and management discipline around consumption now are going to be in a much better position when that happens across the rest of their software portfolio."

If Henry is right, the chaos currently confined to AI token budgets is not a temporary growing pain but a preview of how all enterprise software will eventually be priced. A decade ago, companies scrambled to understand their cloud bills. Today, they are scrambling to understand their AI bills. The question is whether the organizations building the dashboards this time around can get ahead of the curve — or whether, as Henry warned, they will end up where so many companies ended up with cloud, realizing too late how much they were overpaying, and for how long.

AI Spend and Consumption Management is available now in public preview for 1Password SaaS Manager customers. Broad availability is planned for fall 2026.

Canva on Tuesday launched Canva Code 2.0, a major upgrade to its AI-powered coding tool that lets users build interactive websites, apps, and experiences using plain-language prompts — and then edit the results as easily as tweaking a Canva presentation. The feature is now available to all of the company's more than 265 million monthly users across every pricing tier, including free accounts.

The move is Canva's most aggressive push yet into the fast-growing "vibe coding" market, a category that barely existed 18 months ago but has already minted billion-dollar startups and reshaped how non-developers think about building software. But where rivals like Lovable, Replit, and Bolt.new have focused primarily on generating functional code from text prompts, Canva is making a different bet: that the real bottleneck isn't creating the code — it's making the output actually look good.

"Most vibe coding tools stop at functional — generating output that looks the same as everyone else's," Canva states in its announcement. "You might get a working prototype, but making it actually look like yours requires a complex editing surface, a separate design tool, a developer, or endless back-and-forth prompting that rarely lands where you want it.”

Danny Wu, Canva's Head of AI Products, framed the product's positioning in stark terms during an exclusive interview with VentureBeat ahead of the launch.

"We are deliberately targeting non-technical users," Wu said. "Canva Code isn't a tool we're building for developers. What we're trying to do is bring the power of AI coding — and really lightweight coding — into the Canva platform, while answering our users' requests for more interactivity, more customization, and more flexibility, from websites to interactive presentations."

Canva Code 2.0 brings drag-and-drop editing, HTML import, and 75% faster generation to AI-built websites

The update introduces several capabilities designed to collapse the distance between generating code and publishing a polished interactive experience. Users can now create Canva Code projects directly inside other design projects — embedding interactive elements within a whiteboard, presentation deck, or standalone page. Canva has also added more than 50 new templates specifically designed for interactive designs, along with the ability to import raw HTML files from other AI coding tools and convert them into editable Canva designs.

The performance improvements are significant. Canva says it has reduced average code generation time by 75 percent and cut the median time from initial prompt to a published site by 30 percent. The company also reports that integrating Canva Code into the broader Canva editor — allowing users to treat coded outputs like any other design element — has increased active Code users by 25 percent.

Perhaps the most distinctive feature is the editing experience itself. Unlike most AI coding platforms, which require users to re-prompt or modify raw code to make visual changes, Canva Code 2.0 lets users click directly into generated elements to change text, drag and drop images from Canva's built-in library of over 120 million templates and assets, update colors and fonts through a familiar toolbar, or select a specific element and refine it through conversational AI. Every output is fully interactive and automatically adapts to different screen sizes, with a built-in mobile preview.

Wu demonstrated the drag-and-drop editing during the interview, showing how a generated conference website could be modified in real time — swapping in photos, changing fonts to branded alternatives, and editing text directly on the canvas. "The key differentiator with Canva Code is the editability and the kindness of the outputs it generates," he said, though he noted one current limitation: "We don't support moving elements around. You still have to re-prompt for that."

How Canva plans to compete with Lovable, Replit, and Bolt in the booming AI app builder market

Canva's entry into vibe coding at this scale arrives at a pivotal moment for the category. According to market research published by Luminix AI in May 2026, the vibe coding and AI app builder market has reached an estimated $4.7 billion in 2026, with projections pointing toward $12.3 billion by 2027 at roughly 38 percent compound annual growth. The research also estimates that AI-generated code now comprises approximately 41 percent of all code written globally — a figure that would have seemed inconceivable even two years ago.

The competitive landscape has grown ferocious. Lovable, which focuses on conversational, design-forward app generation for non-technical founders, has achieved what may be the fastest revenue ramp in the category's history — reportedly reaching approximately $400 million in annual recurring revenue by early 2026, according to Luminix's analysis. Replit, which transformed its browser-based IDE into a full vibe-coding engine through successive AI agent releases, has tripled its valuation to $9 billion and is targeting $1 billion in run-rate revenue by the end of 2026, per the same report. Bolt.new, which runs a full Node.js environment entirely in the browser, scaled from $4 million to $40 million in ARR within months of launching.

And then there is Canva, which brings something none of those platforms possess: a quarter-billion-user design ecosystem where brands, teams, and individuals already store their visual identities, collaborate on projects, and publish content.

Wu positioned Canva Code not as a direct competitor to these developer-focused tools but as something that fills a gap none of them have addressed. "A lot of the requests that we have been getting and the usage we're seeing is actually with using Canva Code not necessarily as just one artifact, but as part of an overall design, the visual communication they're trying to tell," Wu said. "Like when you have a sales deck, you're able to add a calculator, you're able to add a visualizer of what exactly your product does. That's something where an interactive slide can be worth a thousand pictures."

Why Canva's HTML import feature could turn it into a 'finishing layer' for every AI coding tool

One of the most strategically interesting features in Canva Code 2.0 is its HTML import capability, which allows users to take code generated by any AI tool — including ChatGPT, Claude, Lovable, or Bolt — and bring it into Canva as a fully editable design. The implication is unmistakable: Canva is positioning itself as the place where AI-generated code gets its finishing touches, regardless of where it was originally created.

When asked directly whether this amounts to positioning Canva as a "finishing layer on top of vibe coding," Wu offered a diplomatic but revealing response. "It's really a continuation of our goal to make all design as easy as possible," he said. "We've supported importing PDFs and translating them into docs, importing PowerPoint files — so in one way, it's an expansion of that. But in another way, it's really just listening to what our users want and making Canva both the most useful and the most compatible platform.”

He paused, then added: "It's not that we're deliberately positioning ourselves as a specific layer, say like a finishing layer after vibe coding. We just really want to make our platform the most accessible and the most pluggable."

That language — "most pluggable" — suggests a platform strategy that doesn't require Canva to win the AI code generation race outright. If Canva becomes the default destination for making AI-generated code look professional and on-brand, it captures value from the entire category regardless of which code generation engine users prefer. The strategy also echoes the broader import capabilities that already allow Canva to ingest PowerPoint decks and PDFs from competing platforms, gradually pulling users deeper into the Canva ecosystem without demanding they abandon existing workflows.

What Canva Code can build — and where Danny Wu says it hits its limits

Wu was notably candid about the product's boundaries — a refreshing departure from the typical Silicon Valley product launch. "Canva Code is great for anything that works as a front-end app, and it's especially good when you want to leverage data, data submissions, and interactivity at small to medium scale," he said. "I'll be honest about the limitations. Canva Code is probably not going to be suitable if you're trying to build a website with complex backends, or if you're handling hundreds of thousands of visitors per day."

This candor effectively draws a line between Canva Code and the more ambitious platforms in the space. While Lovable and Replit are pushing toward full-stack application development — complete with databases, authentication, and production-grade hosting — Canva is deliberately limiting its scope to interactive front-end experiences at modest scale. The question is whether that's a strategic weakness or a disciplined focus. For the teachers, small business owners, and marketing teams that make up the bulk of Canva's user base, complex backends and high-traffic scalability are irrelevant concerns. What matters is whether they can create an interactive event page, a property listing website, or a classroom hub that looks professional and works on mobile — without hiring a developer or learning a new tool.

When asked about the AI models powering Canva Code, Wu confirmed the company uses a combination of proprietary and third-party models, including those from OpenAI and Anthropic, but declined to specify the exact mix. "We don't share the exact mix, and it does change over time," he said. "We also route differently depending on what you're asking for and which model family we think is best for handling certain requests."

Canva's AI acquisition spree — from Affinity to Leonardo.ai — now powers its vibe coding push

Canva's broader AI infrastructure has been significantly bolstered by an acquisition strategy that has accelerated over the past two years. In March 2024, the company acquired Affinity, the British creative software suite popular with Mac users, in a deal that Bloomberg reported was valued at "several hundred million pounds." Canva at the time positioned the deal as a way to compete with Adobe's flagship products — Illustrator, Photoshop, and InDesign — by gaining ownership of Affinity's Designer, Photo, and Publisher applications.

Just four months later, Canva acquired Leonardo.ai, an Australian generative AI startup with over 19 million registered users and more than a billion images generated. Canva co-founder Cameron Adams said at the time that Leonardo.ai's technology would be integrated into Canva's Magic Studio generative AI suite.

Together with these acquisitions, Canva Code is the company's attempt to layer interactive, code-driven capabilities on top of a visual design platform that has already been enhanced by professional-grade design tools and generative AI models. The company reports over 32 billion uses of its AI products to date — a staggering figure that underscores how deeply AI is now woven into everyday Canva workflows, even for users who may not think of themselves as using artificial intelligence.

Six million sites published, but Canva's retention data remains an open question

Canva's announcement highlights an impressive traction metric: users have created and published more than six million websites using Canva Code since the feature was first introduced a year ago. But the number deserves scrutiny.

Wu clarified in the interview that the six million figure represents published websites over the past year — meaning sites that were either made public or shared via password-protected or private links. "They may have published publicly, or behind a password, or as a private link. But that's the number of published websites," he said.

When asked about active retention — how many of those sites are still live and being maintained — Wu acknowledged the gap in his data. This is a meaningful distinction. In the vibe coding market, raw creation numbers can be misleading because the barrier to generating a site is so low. The more telling metric — which Canva does not yet provide — would be how many of those six million sites receive regular traffic or have been updated after initial publication.

The early use cases, however, suggest genuine utility beyond novelty. Educators and school administrators are using Canva Code to build classroom hubs, with one teacher creating bespoke webpages for each of their classrooms to keep students and parents updated on announcements. Small businesses, like Alt Marketing School, have built mini apps for fundraising training and interactive roadmaps for their members. For World Book Day, 50 readers created educational games across different subjects, complete with pedagogical guides for classroom use.

Canva Code pricing, data governance, and what enterprise customers need to know

Canva Code 2.0 is available across all of Canva's pricing tiers, including its free plan — a notable decision given that competitors like Lovable, Bolt, and Replit reserve their most capable features for paid subscribers. "As you go from, say, free to pro to business to enterprise, you would get more AI credits and be able to have higher usage of Canva Code," Wu said. "But it is available and it is usable — even free Canva accounts as well as education and not-for-profit accounts."

This credit-based approach mirrors the pricing evolution happening across the entire vibe coding category, where platforms have converged on token or credit systems that meter AI generation capacity rather than gating features behind subscription tiers. The difference is that Canva's free tier serves as an acquisition funnel for a much larger design platform, not just for the coding feature itself.

For the institutional customers Canva increasingly courts — school districts, real estate brokerages, enterprise marketing teams — data governance is a threshold concern. Wu addressed this directly. "All users and customers have full control over how their data is used," he said. "They can choose whether their prompts and data are used for AI training in the settings. For businesses and enterprises, team admins can manage this at the organizational level and guarantee that their inputs, content, and outputs won't be used for training." This opt-out approach reflects a lesson the broader industry has learned the hard way. As The Verge reported when Canva acquired Leonardo.ai, Adobe suffered significant backlash over a policy update regarding user data and AI model training — a controversy Canva appears keen to avoid.

Canva's long-term vision: closing the gap between imagination and what non-technical users can actually build

When asked where Canva Code fits into the company's long-term trajectory — and whether Canva is building toward a full-stack app development platform — Wu steered the conversation back to the company's core audience.

"A huge part of it is reducing the gap between your imagination and what's possible, especially for everyday users — people who don't have a lot of time," he said. "They don't have time to figure out deploys or MCPs or APIs. They just want to design more interactive and more dynamic communication."

He pointed to the rapid improvement in AI model capabilities as a key accelerant. "The kind of things you can create today in one shot — like a 3D visualization of a solar system — you really couldn't have trusted the output a year ago. But today, you have a really high success rate."

Whether Canva Code becomes a durable product category or a feature that gets absorbed into the platform's broader AI workflow will depend on how quickly the company can close the gap between its current front-end focus and the full-stack capabilities that increasingly define the competition. Lovable is shipping Supabase-backed apps with authentication and databases built in. Replit's agents can execute autonomous long-running builds. Bolt.new runs entire Node.js environments in a browser tab. These are fundamentally different ambitions than making a conference landing page look good.

But Canva has never won by matching the technical depth of its competitors. A decade ago, it didn't try to out-feature Adobe — it made design accessible to the 99 percent of people who would never open Photoshop. Now, in a vibe coding market where every tool can generate a working prototype from a prompt, Canva is making the same wager it made in 2012: that for most people, the hardest part was never the building. It was making it look like it came from you.

Model routing is becoming a key component of the enterprise AI stack, dynamically sending prompts to the right AI model to optimize speed and costs. However, current frameworks mostly treat routing as a static classification problem, which severely limits their potential.

A new open-source framework called Agent-as-a-Router tackles this bottleneck, treating the router as a dynamic, memory-building agent. It uses a Context-Action-Feedback (C-A-F) loop to track model successes and failures and update the behavior of the router. 

The researchers also released ACRouter, a concrete implementation of this paradigm. In their tests, ACRouter significantly outperformed static routers and the expensive strategy of defaulting to premium models, all without requiring teams to train massive models or write endless heuristics.

For real-world applications, this framework provides the option to replace hard-coded AI infrastructure with self-optimizing systems that can adapt to changes in user behavior and foundation models used in the enterprise AI stack. 

The economics of routing and the information deficit

Single-model setups are useful for experiments but detrimental when scaling AI applications. AI engineers use model routing to map tasks to cheaper and faster open models when possible, while reserving expensive frontier models for complex reasoning. 

Currently, developers rely on two main mechanisms for this task. The first is heuristics-based routing, which relies on hard-coded manual rules. For example, a developer might write a rule dictating that if a prompt contains certain keywords, it is routed to GPT-5.5. Otherwise, it goes to a self-hosted open source model like Kimi K2.7. 

The second mechanism is static trained policies. These are machine learning classifiers trained on historical datasets that look at the prompt's embeddings and predict the best model based on past training data.

Both approaches are static. When the researchers tested these existing mechanisms on real-world coding and agentic workflows, they found a hard ceiling on accuracy. The key finding shows that static routers suffer from a severe information deficit. Because they only evaluate the input text and never see if the model actually succeeded in executing the task, they guess blindly when faced with complex edge cases.

This results in three distinct points of failure. First, static routers suffer from a frozen information state, meaning they cannot accumulate new execution feedback during deployment. Second, they fail in out-of-distribution (OOD) generalization. They break down during day-two operations when enterprise data or user behavior shifts because their training data no longer matches reality. Finally, they are highly vulnerable to model churn. A static classifier trained on today's models may become obsolete when a better model drops the following week.

Agent-as-a-Router: A self-evolving system

The core thesis of the Agent-as-a-Router is that a truly effective router must acquire and accumulate execution-grounded information during deployment, essentially learning on the job. 

The researchers achieved this through the C-A-F loop. When a new prompt arrives, the router examines the prompt and task metadata, such as the programming language or difficulty. It then searches its historical memory for similar tasks to see which models succeeded or failed in the past. The router uses this context to select the target model and execute the task. Finally, the system observes the real-world outcome, extracts a success or failure signal, and writes this feedback back into its memory to inform future routing decisions.

Consider an automated enterprise data analytics pipeline. The router receives a SQL generation task and sends it to an open-source model like Kimi. The model hallucinates a column name and fails to compile the SQL. The C-A-F loop observes the compiler error, registers it as feedback, and logs it. The next time a similar obscure SQL query arrives, the router checks its context and routes the task to a more advanced model like Claude Opus 4.8. 

ACRouter

The researchers developed ACRouter as the concrete instantiation of this framework. It is composed of three core components: the Orchestrator, the Verifier, and Memory. This architecture is supported by a tool layer to physically execute the C-A-F loop.

The Memory module powers the context phase. Built on a vector store, it retrieves relevant past interactions and updates the historical database with new outcomes. The Orchestrator handles the action phase. It processes the user prompt alongside the retrieved memory to select the most capable target model from the available pool. The Verifier manages the feedback phase by evaluating the chosen model's output to generate a clear success or failure signal.

The tool layer hooks the Verifier into real-world execution environments, like a Python code interpreter, an agentic sandbox, or a database engine. The tool layer allows the system to execute the generated code or query and observe the exact outcome, providing the verifiable signal the router needs to learn.

The Orchestrator itself is lightweight. Instead of a massive, computationally heavy large language model, the researchers trained a sub-billion parameter adapter based on Qwen 3.5 (0.8B parameters), which means it can be self-hosted on a device of your choice.

ACRouter in action: Outperforming the frontier baselines

To stress-test the framework, the researchers introduced CodeRouterBench, an evaluation environment comprising roughly 10,000 tasks with verified scores across eight frontier models, including Claude Opus 4.6, GPT-5.4, Qwen3-Max, and GLM-5. The evaluation was split between in-distribution (ID) tests (covering nine single-turn coding dimensions like algorithm design and test generation) and an out-of-distribution (OOD) agentic programming testbed. The OOD tasks were qualitatively different, requiring multi-step planning, file navigation, and iterative debugging to see if the router could adapt to fundamentally new domains.

The baseline results revealed why a single-model strategy is flawed: no single model dominates every category. For example, while Claude Opus 4.6 achieved the highest average performance, it was outperformed in algorithm design by GLM-5 (an 86% relative improvement) and in test generation by Qwen3-Max (a 111% improvement), despite Opus costing roughly 12 times as much as smaller models like Kimi-K2.5. 

In the benchmarks, static routers continuously failed by sending a specific niche coding task to a model ill-equipped for that exact syntax. The static router had no way to know the code was failing to execute. In contrast, ACRouter adjusted its strategy after receiving negative feedback signal from the execution environment. 

According to the researchers' benchmarking, ACRouter sits firmly at the Pareto frontier of cost and performance. On both the ID task streams and the complex OOD agentic tests, ACRouter achieved the lowest cumulative regret, a metric measuring sub-optimal routing decisions over time. On the in-distribution test set, ACRouter cost $13.21 across the full task run, compared to $34.02 for always defaulting to Opus — a 2.6x savings.

It dynamically matched tasks to the most capable model for that specific niche, suggesting that enterprises can achieve or exceed frontier-level accuracy across diverse workloads without paying a premium price for every query. 

Caveats, limitations, and how to get started

While the Agent-as-a-Router paradigm solves the information deficit, it is not a blanket solution for all AI workflows. 

The framework shines in verifiable tasks where the Verifier gets a clear success or failure signal from the environment, such as coding or data retrieval. It is effective for applications with distribution shifts and domains where different models excel in completely distinct niches. 

Conversely, the setup is overkill for trivial tasks where any model will suffice, or for low-volume applications that do not justify the engineering overhead. It is also unsuitable for subjective domains, such as creative writing, where a correct answer cannot be easily verified and feedback signals are impossible to standardize.

The researchers open-sourced the code on GitHub and released the orchestrator model weights on Hugging Face under the Apache 2.0 license. The router is compatible with Claude Code, Codex, and OpenCode.

Presented by Kasm Technologies

Enterprise infrastructure teams have spent the better part of a decade pushing workloads into Kubernetes. Applications, APIs, batch jobs, data pipelines — if it runs in a container, it belongs in the cluster. The operational benefits are well-established: declarative configuration, horizontal scaling, self-healing, native integration with CI/CD pipelines and observability tooling. Kubernetes has become the default operating model for production workloads.

Except for desktops.

Secure desktop and application delivery — the kind that enterprises depend on for remote work, privileged access, and regulated-industry workflows — has remained stubbornly outside the Kubernetes model. Legacy virtual desktop infrastructure was built in a different era, for a different set of assumptions: pre-allocated VM pools, bespoke management planes, proprietary appliances, and operational tooling that has nothing to do with how modern platform teams work. The result is a split infrastructure reality: a modern, cloud-native application layer on one side, and a manually managed, operationally isolated desktop layer on the other.

That split is expensive. It means different tooling, different scaling behaviors, different observability approaches, and different operational runbooks. Platform engineers who are proficient in Kubernetes still have to context-switch into an entirely different mental model the moment a desktop infrastructure problem arises.

The more fundamental issue is that this split is unnecessary. Secure, containerized workspace delivery is a workload that Kubernetes is architecturally well-suited to run. Sessions are containers. Scaling is demand-driven. Configuration should be declarative. The only thing missing was a platform built to take advantage of that alignment.

Why the timing is right

The appetite for Kubernetes-native workspace delivery has grown significantly as organizations mature their container platform investments. Platform teams that have spent years standardizing on Helm, GitOps workflows, and Kubernetes-native observability are increasingly unwilling to make an exception for desktop infrastructure. The question has shifted from "can we run this on Kubernetes?" to "why isn't this running on Kubernetes already?"

At the same time, the security case for containerized workspace delivery has become more urgent. Browser-delivered, containerized workspaces provide session isolation that VM-based desktops cannot match — each session is ephemeral, isolated at the container boundary, and terminates cleanly without persistent state. For organizations managing sensitive data, insider risk, or third-party access scenarios, this isolation model is a meaningful security control, not just a deployment convenience.

The convergence of these two trends — Kubernetes-native infrastructure expectations and containerized session security — creates a clear opportunity for platforms that can address both simultaneously.

What Kubernetes-native deployment looks like

A Kubernetes-native deployment uses Kubernetes as the control plane for workspace infrastructure — handling orchestration, scaling, and lifecycle management through the same declarative model used across the rest of the platform. Instead of relying on dedicated management appliances or pre-provisioned desktop pools, infrastructure is managed through the same CI/CD, GitOps, observability, and security workflows the platform team already operates. This gives platform teams a consistent operational model rather than maintaining a separate toolset for desktop infrastructure.

Kasm Workspaces, the browser-delivered workspace platform, is purpose-built to use Kubernetes as the control plane for workspace orchestration and delivery. Its deployment model is designed for real enterprise environments — not simplified demos — with production-grade Helm charts that follow Kubernetes conventions, tested upgrade paths between versions, and a standardized backend architecture validated across production deployments. An RDP Gateway component purpose-built for the Kubernetes topology enables Windows and Linux virtual machine access through the same platform.

Key capabilities include:

• Horizontal session scaling driven by actual demand, orchestrated by Kubernetes — no pre-warmed VM pools required.

• Declarative configuration through Helm values, enabling GitOps and CI/CD integration for workspace infrastructure.

• Namespace-level isolation and compatibility with existing RBAC policies, ingress controllers, and secrets management integrations.

• Metrics export for integration with Prometheus and existing observability stacks.

• Rolling builds by default, reducing maintenance windows and enabling more predictable version management.

Real-world applications

Regulated-industry remote access. A financial services organization running a Kubernetes-based application platform can deploy Kasm into the same cluster, using the same operational tooling, to deliver isolated browser and application sessions to analysts and advisors. Sessions are ephemeral, network egress is controlled, and the entire deployment is managed through the same GitOps pipeline as their application workloads.

Contractor and third-party access. Organizations that regularly onboard contractors or external vendors — with the associated privileged access risk — can provision Kasm sessions on Kubernetes that scale up during engagement periods and scale back during low-demand windows. No persistent access. No VPN extension to external parties. Containerized isolation at every session boundary.

AI/ML development environments. Teams building and running AI models need GPU-enabled development environments with security controls that general-purpose cloud desktops rarely provide. Deploying Kasm on Kubernetes with NVIDIA MiG Multi-Instance GPU support lets platform teams deliver fractional GPU resources into isolated workspace sessions — giving data scientists the compute they need without shared-infrastructure security exposure.

The operational shift

The practical implication of a Kubernetes-native workspace platform is that platform teams can stop treating workspace infrastructure as a special case. The same engineers who deploy applications can deploy the workspace platform. The same pipelines that manage application configuration can manage workspace configuration. The same dashboards that monitor application health can monitor workspace health.

That operational consolidation reduces overhead, improves consistency, and eliminates the context-switching cost that has made desktop infrastructure a persistent pain point for cloud-native organizations.

For organizations still running legacy VDI alongside modern cloud infrastructure, the question is no longer whether a Kubernetes-native alternative exists. It does. The question is when to make the transition.

Organizations interested in evaluating Kubernetes-native workspace delivery can explore the platform at kasm.com and try out community edition for yourself.

Daniel Ben-Chitrit is the Chief Product Officer at Kasm Technologies.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

DeepSeek's recent decision to drastically cut pricing on its V4-Pro model by 75% should have been unequivocally good news for enterprise AI vendors and developers. Instead, many are discovering that cheaper models don’t automatically translate into healthier margins.

The reason is simple: While inference costs plummet, agent systems are voraciously consuming tokens faster than prices are declining. For the last 2 decades, software economics was dictated by the same rule. Infra became cheaper every year whereas applications became more capable. AI was initially hypothesized to follow the same pattern. As frontier models improved and token prices dropped, many assumed inference would become a negligible operating expense.That assumption has begun crumbling exponentially. 

A chatbot usually turns one user question into one model call. An agent turns it into a chain of planning, retrieval, tool use, verification, summarization, and follow-up decisions. The user sees one answer. The vendor pays for the loop. That is the 100x problem: The same user-visible request can cost a lot  more to serve as an agentic workflow than as a chatbot or retrieval-augmented generation (RAG) response. In longer-running workflows, the multiplier is higher. Falling model prices help, but they do not fix a product architecture that turns one prompt into dozens of billable operations.

The scale of what is now at stake is clear in how model providers themselves are pricing developer relationships. OpenAI's proposed program to give every Y Combinator startup $2 million in API credits — a number that would have funded an entire seed round in any prior tech cycle, and when the same cohort got by on a few thousand dollars of AWS credits — is less a recruiting perk than an admission of what it now costs to run an AI-native company through its first year of product. For established enterprises retrofitting agents into existing product lines, the absolute numbers are larger still.

What token amplification is

In a single-turn chatbot, one user message produces roughly one model call. Input-to-billed ratio is about 1:5.

In a multi-step agent rolled out across customer support, sales operations, finance, legal review, and engineering, that ratio routinely lands at 1:700 or higher. Every loop iteration carries forward the cumulative conversation, tool outputs, and reasoning traces. Each step appends; nothing is dropped.

A "simple" agent query like “What did our top customer ask about last week?” typically touches seven priced operations before returning an answer:

1. User prompt (~50 tokens)

2. System prompt and tool definitions (~3,000 tokens, repeated on every call)

3. Retrieval (~5,000 tokens of context)

4. Model call #1 — tool selection (8,000 in / 200 out)

5. Tool execution (~4,000 tokens returned)

6. Model call #2 — summarization (12,000 in / 400 out)

7. Model call #3 — follow-up decision (12,400 in / 100 out)

One sentence in, roughly 35,000 input tokens billed. Somewhere between $0.10 and $0.40 per query on a frontier model. Multiply that by a million queries a month — the table-stakes volume for any enterprise B2B feature — and the line item is six figures.

Why this breaks the existing AI business model

The dominant pricing story for enterprise AI has been seat-based SaaS: Pay per-user per-month, deliver agent capability, capture margin. That model assumes a reasonably bounded cost-per-user.

Token amplification breaks the assumption. A power user running 50 agent invocations a day on a $40/seat plan can cost more in inference than the plan charges. Token amplification shatters the traditional SaaS pricing model. When a power user’s daily agent activity costs more in inference than their monthly subscription fee, vendor gross margins turn negative, a paradox that compounds as customers deepen their agent adoption, the very usage curve vendors are selling to their boards. Several vendors are now privately reporting negative gross margins on heavy users, mirroring recent cloud expenditure reports from the Bessemer 'Supernova' cohort, where the correlation between AI-agent adoption and gross margin contraction has moved from a theoretical risk to a primary P&L headwind.

The visible symptoms have started leaking into public coverage. Bloomberg this week documented a widening gap between Salesforce's Agentforce marketing demos and the capabilities actually shipping to customers. This is the kind of gap that opens predictably when promised functionality is technically possible but uneconomical to serve at the price the seat plan implies. Salesforce is the most-watched case, not a unique one.

"For my team, the cost of compute is far beyond the costs of the employees." — Bryan Catanzaro, VP of Applied Deep Learning, Nvidia

The strategic implication is not "AI is expensive." It is that the dominant business model assumed by most AI-native company plans does not survive contact with agentic workloads.

A simple example

Consider an enterprise software vendor charging $40 per-user per-month for an AI-enabled support assistant. A traditional chatbot might cost only a few cents per user per day in inference, leaving healthy gross margins.

Now replace that chatbot with a fully agentic workflow capable of investigating tickets, querying internal systems, drafting responses, validating outputs, and escalating exceptions. If a heavy user executes 50 to 100 agent requests per day, inference consumption can increase by an order of magnitude. What was once a negligible infrastructure cost becomes a material operating expense.

This creates an unusual dynamic: The customers receiving the most value from the product are often the customers generating the highest inference costs. In extreme cases, vendors can find themselves with their most engaged users contributing the least profit. The result is a growing realization across enterprise software that agent adoption and margin expansion are no longer automatically aligned.

Agent orchestration is the new moat

The technical responses are known and converging. They are not novel, but they are critical for survival

• Cost-aware routing: This technique involves a small classifier model that decides which tier (Haiku, Sonnet, Opus equivalents) handles each query. Well-tuned routers cut inference bills by around 60% without any degradation in quality

• Prompt caching: Anthropic, OpenAI, and Google now offer 75 to 90% discounts on cached prefixes. 

• Context discipline: You can truncate tool outputs, prune reasoning traces, and cap tool depth to prevent your agent from going down a rabbit hole

• Speculative decoding: for self-hosted deployments, this technique guarantees 2 to 3X effective throughput on the same GPUs.

"Organizations using orchestration-led governance report stronger productivity gains — a holistic orchestration layer is associated with six times greater productivity impact than compliance‑only approaches" — IBM

The companies building this layer well are starting to look less like microservice operators and more like financial trading systems: Every routing decision priced, every path with its own P&L, every tenant on a metered budget.

What enterprise leaders should actually do

Four moves separate the companies that will still have margin in 24 months from the ones that won't:

1. Make inference cost a first-class metric. Track it per-feature, per-tenant, per-query class the same way cloud cost was tracked starting in the mid-2010s.

2. Budget like a media buyer. Set cost-per-thousand-queries ceilings per feature. Cap them. Alert on overruns. Engineering will not enforce this on its own.

3. Treat the router as core infrastructure, not an optimization. It is the new load balancer.

4. Audit prompts quarterly. A 4,000-token system prompt that grew organically over six months is a six-figure bill in slow motion. Most teams have never read their own production prompts end to end.

5. Negotiate volume commits early. Frontier-model vendors now offer reserved-instance-style prepaid commits at substantial discounts. List price is the worst price any enterprise will ever pay.

The next 24 months

The structural shift underneath agentic AI is not that it is expensive. As DeepSeek's price cut today underscores, frontier inference unit costs are dropping roughly 3X per year, and the curve is not slowing.

The shift is that amplification is outrunning the price cuts. Cutting per-token costs 75% does not help a company whose agents are doing 700X more tokens per user query than its pricing model assumed. For the first time since the cloud era began, architecture decisions are again financial decisions in real time. A prompt redesign is a margin event. A poorly bound agent loop is an outage with a credit card attached.

The companies that survive the next 24 months of AI infrastructure pricing will not be the ones running the cheapest model. They will be the ones whose agents are smart and know what they cost to think.

That is the 100X problem. And it is arriving faster than the price cuts can hide it.

Maitreyi Chatterjee is a senior software engineer at a big tech company.

Devansh Agarwal works as an ML engineer at a leading tech company.

Slopsquatting represents an emerging supply chain threat made possible by AI hallucinations. As developers increasingly rely on AI coding assistants, they unknowingly grant cybercriminals access to their software from day one. 

Understanding what slopsquatting is

Slopsquatting is a new type of supply chain attack that uses large language model (LLM) hallucinations to inject malicious code into development workflows. The term combines "AI slop" and "typosquatting," a deceptive practice where attackers register misspelled or lookalike versions of popular domains to prey on users who enter URLs incorrectly.

This novel attack vector exploits LLMs' tendency to generate fictitious software package names, which threat actors can then register and populate with malicious code.

During AI-assisted coding, the model may generate fake open-source packages — bundled collections of files, programs and installation tools. This alone is not necessarily harmful. However, if an attacker registers that fake package name, they can inject malware that gets incorporated directly into a developer's codebase.

How AI creates a supply chain risk

Traditionally, AI safety risks stem from hallucinations, which can adversely affect users who treat misinformation as valid. However, those same hallucinations have evolved into exploitable security vulnerabilities.

Typosquatting is a deceptive practice where a cybercriminal registers a mispelled version of a popular package to trick developers. It has existed for decades, so registries have built protections against it. 

However, AI has changed the threat model. It recommends fictitious packages that sound plausible rather than making simple misspellings. Once attackers learn which hallucinated packages models tend to invent, they can register malware-filled packages under those names.

Since the hallucinated packages are not simply typoed versions of popular libraries, there are no protections against this practice at scale. For example, the registry protects against an attacker publishing "crossenv," a squat of the popular "cross-env" package. However, it would not identify "mpn install cross-env file" or "cross-env-extended" as threats.

Hallucinations are persistent and severe

Even if many LLMs recommend the same hallucinated package, widespread compromise is still possible. Malicious packages could remain undetected in production for months or even years, allowing threat actors to passively inject malware across countless environments. 

One research team analyzed 31,267 vulnerabilities belonging to 14,675 packages across 10 programming languages. They discovered that reported vulnerabilities are increasing at an annual rate of 98%, faster growth than the 25% annual increase in the number of open-source software packages. The team also observed an 85% increase in the average lifespan of vulnerabilities, indicating a decline in security.

Real-world dangers of AI hallucinations

Malicious actors can create open-access packages under the same name as commonly hallucinated libraries. Instead of standard code, they are filled with malware. The models believe they are referring to existing packages, so they often repeat the same hallucinated names. Since the hallucinations are not random, attackers could theoretically register packages that trick tens of thousands of developers.

These packages appear legitimate. String similarity to real libraries makes them recognizable. One-character typos suggest simple mistakes rather than malicious intent. Even fully fabricated names remain believable when the AI presents them in proper context. Detection is challenging, as developers trust their coding assistants to recommend valid dependencies.

Why are LLMs hallucinating packages?

LLMs generate the statistically most likely answer rather than prioritizing accuracy. Hallucinations are relatively common as a result. One study found hallucination rates range from 50% to 82%, depending on the model and prompting method. Even GPT-4o, the best-performing model, goes no lower than 23%, even with prompt-based mitigation.

Adversarial hallucination attacks could worsen this problem. Threat actors can leverage token-level manipulation or retrieval poisoning to force models to hallucinate in ways they want, increasing the likelihood that models recommend their malicious packages.

Which LLMs are prone to slopsquatting?

While all LLMs are prone to slopsquatting, some are more vulnerable than others. The likelihood of producing hallucinated packages during code generation depends on the model. Proprietary models are four times less likely to generate hallucinated packages than open-source models.

One research group proved this by conducting 30 tests across 30 different systems. Out of the 576,000 code samples and 2.23 million packages it produced, 19.7% were hallucinations. GPT-4.0 Turbo had a hallucination rate of 3.59%, while DeepSeek 1B, the best-performing open-source model, reached 13.63%.

This research suggests that organizations relying on open-source AI tools for code generation are roughly four times more exposed to slopsquatting attacks. That doesn’t necessarily mean proprietary tools will always remain safer, though. Once attackers realize this disparity, they may manipulate proprietary LLMs to take advantage of perceived safety.

Vibe coding contributes to the problem

Software developers who use AI tools estimate that over 40 percent of the code they commit includes AI assistance. They expect that percentage will increase considerably within the next few years. Already, 72% of those who have tried AI use it daily.

The uptick in vibe coding and AI-assisted coding amplifies the threat surface. As more developers integrate AI tools into their workflows without implementing proper verification processes, the attack surface for slopsquatting continues to expand.

For those using AI to assist with coding, double-checking output is essential. Verifying that recommended packages actually exist in official repositories before incorporating them into projects reduces risk.

Navigating AI-assisted development

Implementing automated checks that validate package names against known registries can help catch hallucinated packages before they enter production code. Security teams should also monitor for unusual package installations and maintain up-to-date threat intelligence on known slopsquatting campaigns.

Zac Amos is the Features Editor at ReHack.

An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pulled. The model did not fail. The context it was given did.

In the past six months, 57% of enterprises traced a confident but wrong AI agent answer to missing or inconsistent business context, and 31% said it happened more than once, according to a VB Pulse June 2026 survey of 101 qualified enterprises with more than 100 employees.

The reason is not hard to find. Retrieval over documents is the default way agents get business context for 38% of enterprises, nearly double the next closest approach. The way most enterprises choose a retrieval system compounds the problem. Ease of ingestion and operational simplicity lead the selection criteria, with retrieval accuracy running behind both. The accuracy problem only shows up after the system is already live.

There is a known fix for this, a governed context layer every agent reads from instead of guessing. Vendors are racing to roll out context platforms while most enterprises are still figuring out what it is.

75% don't have an agentic context layer yet

The context layer is meant to be a shared model of what business data actually means, built once and referenced consistently instead of re-derived by every agent that touches it. 

The VentureBeat research shows the enterprise response to that idea is broad but unfinished. Twenty-five percent of respondents run one in production. Thirty-four percent are building one right now. The remaining 41% have not started.

Among companies already building or running a governed context layer, 78% report a confident-wrong failure — an AI agent that answered with total certainty and was still wrong. Among companies with no plans to build a layer, only 20% report the same thing. Companies that already got burned are far more likely to be building the fix. Companies that haven't been burned yet see no urgency.

What governed context looks like when someone actually builds one

Every major data and AI platform vendor is now building some version of this layer, and they are not converging on the same architecture. 

• DataHub is treating catalog metadata and years of analyst query behavior as a knowledge source, then keeping it current as a living system rather than a static wiki. 

• Microsoft's Fabric IQ is building a business ontology that any agent, not just Microsoft's own, can query over MCP. 

• Couchbase is pushing agent memory and context retrieval down to the edge, arguing the operational database is a more natural home for it than a search or analytics layer bolted on after the fact. 

• Pinecone's Nexus is compiling structural logic into the metadata layer ahead of runtime, betting that agents need pre-built structure more than they need faster search.

• Snowflake runs a two-layer system, Horizon Context for customer-managed definitions and Cortex Sense for context the platform infers on its own. 

• Oracle's Unified Memory Core takes the opposite approach, folding vector, graph and relational data into one transactional engine so there is no sync layer left to go stale. 

• Google's Knowledge Catalog mines query logs and usage patterns to curate semantic context automatically.

• AWS's Context service makes the same bet, a knowledge graph that gets smarter from how agents actually use it rather than from manual re-curation.

Analysts converge on one diagnosis

The vendor approaches differ. What analysts and practitioners have told VentureBeat about the underlying problem, across a run of interviews this year, does not.

When DataHub's context layer push landed this spring, Constellation Research VP and principal analyst Michael Ni framed the stakes in blunt terms. "Whoever controls runtime context controls the AI decision layer for enterprise data," Ni said. He was equally direct about how far any single product actually gets a buyer. "Vector memory isn't business meaning, business meaning isn't governance and governance isn't execution," Ni said.

In the same interview, BARC analyst Kevin Petrie pointed to a narrower but concrete gap. Most context platforms concentrate on structured tables, he said, which give agents trusted facts but miss the harder, messier context locked in documents and unstructured content, exactly the material a business actually runs on day to day.

Stephanie Walter, practice leader for AI Stack at HyperFRAME Research, made a related point earlier this year when VentureBeat asked her about enterprise context fragmentation. 

"The market is converging on the same conclusion," Walter said. "Agents don't just need more tokens or better models. They need governed, current, low-latency context." She made a similar case in an earlier review of Pinecone's Nexus launch, careful not to overstate how new any of this is. Nexus, she said, "shifts knowledge work from runtime chaos to pre-compiled structure. But it's an evolution of RAG architecture, not a complete reinvention." 

Gartner's Arun Chandrasekaran, reviewing the same launch, offered the more forward-looking read. Agentic AI, he said, is moving from pure information retrieval toward a reasoning architecture, one where long context works as short-term memory and a vector database functions as deep storage underneath it.

The fragmentation problem shows up hardest at the practitioner level, where separate tools for retrieval, memory and access control were never built to agree with each other. Steven Dickens, CEO and principal analyst at HyperFRAME Research, put it bluntly after Oracle's AI database push landed this spring. "Data teams are exhausted by fragmentation fatigue," Dickens said. "Managing a separate vector store, graph database and relational system just to power one agent is a DevOps nightmare." 

Matt Kimball at Moor Insights and Strategy, in that same story, put the production reality more simply. Getting an agent working is not the hard part, he said. The struggle is running it in production, where the goal becomes removing the distance between data and execution rather than adding another layer on top of it.

What this means for enterprises

Here's what this adds up to for enterprises building on this layer.

Retrieval alone will not close the context gap. RAG is the default source for context in most enterprises today, and it is also the layer most closely associated with the confident-wrong-answer failure. Adding more documents or a bigger index does not fix a definition that is inconsistent across systems.

The semantic context layer is where the budget is actually moving, even where it hasn't shipped. Fifty-eight percent of enterprises are already engaged — building or in production — but only 25% have actually gotten a layer live. That gap shows where enterprises have decided to spend, not where they've arrived.

No single vendor owns the architecture yet, and that is likely to stay true for a while. Enterprises evaluating this layer should expect to integrate rather than pick a single winner, at least for the next several quarters.

The buying decision is happening this year, and it is concentrated among the companies already burned by it. Fifty-seven percent of enterprises plan to switch or add a retrieval or context platform within the next twelve months. That intent is not spread evenly. Enterprises that reported a repeat confident-wrong failure plan to switch or add a provider at roughly 81%, against 32% among enterprises that never hit the problem. The companies shopping for new context tooling right now are largely the ones whose agents already got it wrong.

The agents are already running. The context underneath most of them is still being built, and the vendor selling the fix is being chosen this year.

This data will be part of a broader conversation at VB Transform 2026 on July 14 and 15 in Menlo Park: the context gap enterprises are racing to close, and which of the emerging approaches — governed semantic layers, hybrid retrieval, provider-native bundles — actually holds up in production.

OpenAI on Thursday launched ChatGPT Work, a new AI agent embedded inside its flagship chatbot that aims to transform ChatGPT from a question-and-answer tool into an autonomous work platform capable of executing complex, multi-step tasks across users' email, calendars, code repositories, and messaging apps.

The product is powered by OpenAI's latest flagship model, GPT-5.6, and is designed to go far beyond generating text. ChatGPT Work can gather context from connected apps, files, and workflows to produce finished documents, spreadsheets, presentations, reports, and websites. The agent takes a stated outcome, breaks it into smaller steps, and stays with complex projects for hours, completing them independently.

The launch marks OpenAI's clearest attempt yet to reposition ChatGPT as a workplace platform rather than a chatbot — and it arrives at a moment of extraordinary financial significance for the company. Last month, OpenAI confidentially submitted a draft S-1 registration statement to the SEC, initiating what could become one of the largest technology IPOs in history, with reported valuations clustering between $730 billion and $852 billion and annualized revenue that has blown past $25 billion.

In a short demonstration and conversation with VentureBeat on Friday, Ty Geri, a product manager at OpenAI who helped build ChatGPT Work, said the product's mission is to democratize the kind of agentic AI capabilities that OpenAI's internal engineering tool, Codex, has already demonstrated. "What's really exciting is we've seen how much Codex has been able to push the frontier of what we can get done with these AI tools, as opposed to just getting information or answers or guidance," Geri said. "Our internal adoption of Codex is literally an exponential curve across every single product function and every single use case."

Why OpenAI built a persistent virtual machine that works from the beach

The core architectural bet behind ChatGPT Work is a persistent cloud-based virtual machine that runs on OpenAI's servers, always available to the user regardless of which device they happen to be on. That marks a deliberate departure from competitors whose agents require a local machine to remain powered on and connected.

"What's really exciting about ChatGPT Work is that it's a virtual machine in the cloud that's always on for you, and this is available across all of our paid tiers," Geri said. "All Plus users are getting this. I think that's a very unique aspect of this."

The mobile-first aspect of the launch is something Geri described as "missing from the market." He pointed to the ability to create a website on a phone and share it with collaborators as a particularly novel capability. "Sites are new in general to Codex. They launched in Codex about a week and a half ago, but now we're launching also in web and mobile. You can create a site on your phone at the beach and share it with your friends," he said.

ChatGPT Work will roll out beginning with Pro, Enterprise, and Edu users, and will expand to Plus and Business users over the next few days. In the interview, Geri emphasized that the availability of the product to Plus subscribers — not just premium tiers — is central to OpenAI's strategy. "It's accessible to all paid plans, including Plus users, which in my opinion is a really big feat, and really part of that OpenAI mission, which is about bringing all this power to as many people," he said.

How MCP plugins connect ChatGPT Work to Slack, Gmail, and GitHub

The product relies on MCP-based plugins to connect to external services like Gmail, Google Calendar, Slack, and GitHub. When asked whether the plugin architecture is based on the Model Context Protocol standard, Geri confirmed: "These are all based on MCP." He added that connecting multiple Gmail accounts — a frequent user request — "is definitely on the roadmap."

The experience is designed to be action-oriented from the first interaction. ChatGPT Work offers a personalized onboarding flow that surfaces different suggested use cases depending on the user's role. Geri demonstrated how the system, detecting his role as a product manager, immediately suggested tasks like evaluating AI systems, building research artifacts, and managing his calendar. "You can start with a simple task like catch me up on Slack or Teams or read today's calendar," Geri said. He described a scenario where the system reviewed his calendar, identified scheduling conflicts, flagged meetings requiring preparation, and then — on his instruction — declined, accepted, or rescheduled events directly.

Users can also customize the agent by teaching it their writing style, organizing outputs into projects, and — in a lighter touch — choosing a virtual pet that accompanies them in the interface. The interface also introduces a hosted website feature that allows users to build and share interactive sites directly through ChatGPT Work, turning what would typically be a static slide deck into a dynamic, collaborative artifact. "Now we suddenly have a collaborative interface that's actually more exciting and more accessible than a slide deck, which has all these formatting restrictions," Geri said.

Scheduling 10 bug bashes at once: what agentic productivity looks like in practice

Geri's own usage of ChatGPT Work illustrates the breadth of tasks the system can handle. In the run-up to the product's launch, he needed to organize pre-release testing sessions — known internally as "bug bashes" — across dozens of features and team members.

"I just come to ChatGPT Work and say, 'Set up a bug bash for all the distinct features in ChatGPT Work. Add all the people that worked on that feature,' and it can check Slack, it can check GitHub, it can check Docs, and find a time that works for the four highest contributors to that feature," Geri said. "It went and scheduled 10 bug bashes, all coordinated across all those different people. That would have taken me 30 minutes at least."

But Geri pushed back against the characterization that ChatGPT Work is limited to rote administrative work. He described using it for analytically complex tasks like identifying the biggest causes of user churn for specific product features and generating product solutions — work he said would previously have taken months. "Things that we would have spent three months doing, we can now spend a week doing — and do much more, and make a much better product," Geri said. "Bugs that we would have found three or four weeks from now, we can now find within two days and fix for our users."

He also described handing off the tedium of product testing itself. "It used to be that even though like the most interesting part of my job is like what to test, I would actually end up having to spend most of my job doing the testing, which is like me taking a mouse and like clicking on the same thing over and over again, like five times," Geri said. "Instead, now I can define what do we want to test, and ChatGPT Work or Codex can actually go test it for me, deliver me that bug report, and then we can work on fixing that bug."

What OpenAI says about data privacy when AI reads your Slack and email

When pressed on data privacy concerns — given that ChatGPT Work pulls sensitive information from workplace tools like Slack, Google Drive, and email — Geri said privacy "is incredibly important, and the most important part of this is it's always in the user's control."

He pointed to OpenAI's existing enterprise security infrastructure, noting that "enterprise accounts have ZDR, and users can always opt out of letting their conversations help improve future models, which many users do." The comment aligns with assurances OpenAI made when it first launched ChatGPT Enterprise in August 2023, when the company wrote in a blog post that it does "not train on your business data or conversations."

The privacy question carries additional weight now because of the sheer volume of sensitive workplace data ChatGPT Work is designed to access. Unlike a chatbot session where a user voluntarily pastes text into a prompt, ChatGPT Work actively reaches into connected systems — reading Slack messages, scanning calendar invitations, pulling GitHub commit histories — to assemble context for its tasks. That represents a fundamentally different data surface area than anything OpenAI has offered before, and one that enterprise security teams will scrutinize carefully before granting access.

ChatGPT Work enters a three-way arms race with Anthropic and Microsoft

ChatGPT Work lands squarely in the middle of what has become the defining competitive battlefield in enterprise AI: the race to build autonomous workplace agents that can go beyond generating text and actually execute tasks.

The product arrives months after Anthropic took Claude Cowork out of preview and into general availability in April, bringing its AI agent to web and mobile platforms aimed at helping enterprise users monitor and manage long-running AI-driven tasks from anywhere. Meanwhile, Microsoft made Copilot Cowork generally available worldwide on June 16, built in partnership with Anthropic to move beyond chat and into execution. The three products — ChatGPT Work, Claude Cowork, and Microsoft Copilot Cowork — now compete directly for the attention of enterprise IT departments and individual knowledge workers alike.

The convergence is striking. All three products share a remarkably similar vision: a persistent AI agent running in the cloud that can break complex tasks into steps, connect to workplace tools via plugins, and produce finished outputs rather than just conversational replies. All three work across desktop, web, and mobile.

What distinguishes OpenAI's approach is its raw consumer distribution advantage. ChatGPT has reached 900 million weekly active users, and OpenAI now has 50 million paying subscribers. More than 9 million paying business users rely on ChatGPT for work, and 92% of Fortune 500 companies now use ChatGPT. By making ChatGPT Work available to Plus subscribers at $20 a month — not just Enterprise or Pro customers — OpenAI is betting that broad accessibility will drive adoption faster than any competitor can match.

OpenAI's product manager says AI is a partner, not a replacement — with a caveat

When asked about the potential impact on the labor market, Geri was careful with his framing. He declined to speak broadly about workforce disruption but offered his personal experience as a product manager whose day-to-day work has been substantially reshaped by the tool.

"My job is not to schedule bug bashes and find out who contributed to a specific feature. That's a task I do in my job, but that's not my job," Geri said. "My job is to make an amazing product." He described ChatGPT Work as "a partner" and "an extension of me, certainly not a replacement," adding: "Everybody feels far more productive than before, but is also almost working harder than before, because you get to work on all the things you want to work on as opposed to the drudgery around it."

But Geri was also careful not to minimize the sophistication of the work the agent can handle. "I also don't want to say that it's only doing mundane tasks because, like something like hill climbing retention curves on a given feature is not mundane. It's actually really hard to do," he said. The distinction matters. If ChatGPT Work were merely automating calendar invitations and expense reports, it would be a convenience tool. The fact that Geri describes it compressing three months of analytical product work into a single week suggests something with far greater implications for how teams are structured and staffed.

An IPO-bound company needs ChatGPT Work to prove enterprise AI can generate revenue

The timing of ChatGPT Work's launch is impossible to separate from OpenAI's IPO trajectory. The company needs to demonstrate that it can convert its massive consumer user base into durable enterprise revenue — a narrative that becomes significantly more compelling with a product explicitly designed around professional workflows.

OpenAI said it is generating $2 billion in revenue per month, growing four times faster than Alphabet and Meta did at comparable stages, with enterprise now making up more than 40% of revenue and on track to reach parity with consumer by the end of 2026. But OpenAI remains heavily loss-making, and the company does not expect to reach profitability until around 2030, with internal projections suggesting losses of $14 billion in 2026 alone.

The competitive dynamics are unprecedented. Anthropic filed for its own IPO on June 1 at a $965 billion valuation, setting up simultaneous public listings from the two most prominent AI startups in history. Whether both can sustain their lofty valuations under the scrutiny of public market investors will depend in large part on whether products like ChatGPT Work and Claude Cowork deliver measurable productivity gains to paying enterprise customers.

The launch also caps a product trajectory that began with ChatGPT Enterprise in August 2023, accelerated through the release of OpenAI's Operator agent in January 2025, and continued through Operator's deprecation and shutdown on August 31, 2025, when its capabilities were folded into the ChatGPT agent framework. ChatGPT Work is the consolidation of those efforts into a single, unified product — one that pairs GPT-5.6's three model variants (Sol for power, Luna for speed, and Terra for balanced everyday use) with a persistent cloud environment and an expanding library of MCP plugins.

The future of work may already be running in the cloud

When asked whether ChatGPT Work signals a shift toward a new kind of operating system — one where users interact with their computers primarily through an AI agent rather than through traditional mouse-and-keyboard interfaces — Geri stopped short of making sweeping predictions. But he hinted at the direction OpenAI sees ahead.

"Anybody who has worked with Codex or now ChatGPT Work will realize how exciting it is to interact with your environment and your computer via the agent," he said. "Especially in the desktop app, where the model has access to your entire machine and can interact with websites on your behalf — it's really able to be an extension of you and a real partner, and that certainly feels like the future."

At the end of the interview, Geri circled back to something personal. "I've never enjoyed work as much as I have in the last month using ChatGPT Work and Codex," he said — a striking admission from a product manager who, until recently, spent a meaningful share of his days clicking through the same interface five times in a row just to see if it would break. OpenAI is now asking 900 million users to believe that feeling scales. For a company weeks away from one of the largest public offerings in history, the answer to that question is worth roughly $850 billion.

Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack. 

Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six in 10 enterprises plan to switch or add vendors in each of five control layers within the next 12 months, and roughly a third — depending on the layer — plan to move within the quarter, the research finds.

There are five main layers where enterprises are building: identity for agents (which agent is allowed to do what, under whose credentials); evaluation of agent output (whether the work is any good); cost telemetry (what each agent costs to run); the context layer (the business data and definitions agents draw on to answer); and the orchestration control plane (the software that coordinates multi-step agent work).

Enterprises are already paying the price for deploying agents ahead of adequate control functions. Fifty-four percent of companies had an agent security incident or near-miss caught before harm in the past 12 months. Twenty-seven percent exercise only reactive control of agent spend — they learn what an agent costs when the invoice arrives, with no per-agent budget or ceiling in place.

Here are the five findings that anchor the set — one finding per layer of the tech stack — and what the data suggests doing first in each.

Expensive hardware is idle: 86% of GPU operators report utilization of 50% or less

Eighty-six percent of enterprises that run their own GPUs report utilization of 50% or less. Wall Street has spent the quarter debating whether the AI buildout is overbuilt. This is buy-side measurement, from the enterprises doing the buying, and the research says the most expensive hardware in buildings of these enterprises runs at no more than half its capacity.

The measurement gap compounds it: A minority 44% rigorously track what their AI compute actually costs and returns. Everyone else is only estimating. And the enterprise shopping process continues regardless: 45% of these enterprises say the emerging compute option they are most likely to evaluate in the next 12 months is an AI-specialized cloud (CoreWeave, Lambda, Crusoe, Nebius). However, under 2% of these enterprises report using one of these neoclouds today.

Moreover, roughly one in three companies appears to be considering a hedge against Nvidia: Asked which emerging compute option they are most likely to evaluate in the next 12 months, 32% of enterprises named non-Nvidia accelerators (AWS Trainium, Google TPUs, AMD), while 28% named next-generation Nvidia GPUs. The data suggests that enterprises should measure the utilization and per-workload cost of the GPUs they already own before committing budget to new compute — whether that's an AI-specialized cloud contract, new accelerators, or more GPUs. 

Most deployed "agents" do single-prompt work: 71% say a quarter or fewer complete multi-step tasks on their own

Seventy-one percent of enterprises say a quarter or fewer of their deployed "agents" can complete multi-step work on their own; the rest are single-prompt chatbots. Only 10% say true agents are the majority of what they run. To be sure, the respondents reported that they are in a position to know these things: 81% said they recommend or decide AI purchases at their companies.

That finding — that most agents are actually just chatbots in trenchcoats — lands amid adoption claims across the industry running well ahead of what enterprises are actually running. Gartner predicted 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. It also warned that the most common misconception is referring to these AI assistants as agents, a misunderstanding known as "agentwashing."

Meanwhile, Zapier's enterprise survey said 72% reported deploying or testing autonomous agents; and Writer's 2026 survey has 97% of executives saying their company deployed AI agents in the past year. 

Those surveys asked whether companies have deployed something called an AI agent, and companies said yes. Our survey asked the people running those deployments a harder question: Of the agents you have in production, how many can complete a multi-step task without a person driving each step? The gap matters for two practical reasons. First, the inflated adoption figures are the benchmark boards and vendors use to pressure technical leaders into moving faster — and this data says the real bar is far lower than the headlines suggest. Second, the label determines the bill: A single-prompt chatbot with a human reading every answer needs none of the identity, evaluation, and cost controls this report covers, while a true multi-step agent needs all of them. 

66% let agents push to production on automated evals alone — or are engineering toward it. 5% fully trust those evals

Two-thirds of enterprises fall into one of two camps: 34% already allow an AI agent to push a code or system change to production based on automated evaluation results alone, with no human reviewing it, and another 33% are actively engineering their pipelines to allow that within the next 12 months. Only five percent fully trust the automated evaluations that would make that decision.

The distrust is earned. Half of enterprises shipped an agent that passed internal evaluations and then caused a customer-facing failure in the past year; a quarter watched it happen more than once. Asked to name the biggest weakness in their current evaluations, more enterprises chose “poor alignment with real-world outcomes” than any other answer — 29% of respondents.

And most of the checking happens before an agent ships, then stops. Once agents are live with real users, only 23% of enterprises run real-time quality checks on the answers those agents produce. Another 51% monitor system health only — uptime, request traces, and gateway logs — which tells them the agent is running, and nothing about whether its answers are right. The first move: Before removing human review from any workflow, test your evaluations against production outcomes rather than internal benchmarks, and instrument answer quality, not just uptime.

This finding is explored in more depth in VentureBeat's related coverage of the evaluation gap, which found that larger enterprises are moving faster toward zero-human deployment while also failing more often — and outlines a regression-testing framework built on production outcomes rather than internal benchmarks.

69% run credential sharing somewhere in the agent fleet — and those companies get hit far more often

Sixty-nine percent of companies allow agent credential sharing somewhere in their agent fleet during runtime – meaning multiple agents operating under one API key or service account. Those companies were far more likely to get hit: Organizations with credential sharing anywhere in the fleet experienced a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent has its own scoped identity. 

The takeaway for enterprises is this: Give every agent its own scoped identity, starting with the agents that touch production systems.

57% traced a confident, wrong agent answer to their own missing or inconsistent business context

Fifty-seven percent of enterprises traced at least one confident, wrong agent answer in the past six months to missing or inconsistent business context: wrong metrics, stale definitions, absent documents. Most of them watched it happen more than once.

Most enterprise companies are fixing this, even though they’ve moved forward with agent deployment already: 25% already run a governed semantic layer, or one governed definition of the business that every AI reads from, in production. However, 34% are still building one, and 41% haven't started. The takeaway: Govern the definitions your agents answer from, metrics and entities first, before scaling the agents that depend on them.

The quarter where agent technology “portability” became a priority

One more shift is worth reporting with its limits stated plainly. In our spring orchestration survey wave, the top concern about provider-controlled orchestration was security and permissioning limits (32%). By June, vendor lock-in led at roughly a third, with security limits at 28%. 

Those are two snapshots one quarter apart, and here’s one possible explanation for why portability became a top issue for enterprises. Our June survey went into market after a June 12 U.S. Commerce Department export order took Anthropic's Claude Fable 5 offline for enterprises for roughly three weeks. Meanwhile, Chinese company Z.ai released GLM-5.2's open weights under an MIT license on June 16 at roughly one-sixth of GPT-5.5's price; and Tencent's Hy3 arrived July 6 under Apache 2.0; and OpenAI previewed GPT-5.6 on June 26 to a small group of government-vetted partners, opening it broadly on July 9 after the government's review cleared. The open-weight releases in particular promise enterprises more control over their agents, and while we haven't established a causal link here, the timing is worth noting.

The posture data matches the mood: 51% now expect their primary control plane for enterprise agents to be hybrid — provider-native plus external orchestration — by the end of 2026, up from 34% in the spring survey wave. Enterprises reporting that they rely purely on provider-managed agent services fell from 12% to 7%.

Five layers, no incumbents, 12 months

The synthesis across all five surveys reveals a huge “buying” window. In each of the five control layers, 57% to 64% of enterprises plan to switch or add vendors within 12 months — 64% in infrastructure and in evaluations, 59% in agent security, 57% in retrieval and context — and 26% to 38%, depending on the layer, plan to move within a quarter. No layer has an established incumbent: The most common evaluation tooling is the model provider's built-in evals, tied with no dedicated tooling at all (17% each); 82% of respondents name provider-native or hyperscaler controls as their primary agent security layer; and provider-native retrieval leads the context technology layer (RAG, etc) as well. 

Most enterprises are defaulting today to the built-in tools that ship with the big AI platforms they already use: Anthropic, OpenAI, Google, Microsoft, and AWS. That holds true across every one of these agentic technology layers: enterprises are looking to their primary cloud and model providers to supply the guardrails, evaluations, and retrieval solutions already bundled into those providers' offerings.

Those defaults are winning on convenience, and they're also what the coming spending decisions will test. The survey didn't ask which direction that money moves — toward the platforms' built-in tools or toward the specialists challenging them — which is exactly why every contract in these five layers is worth watching over the next four quarters.

The Q3 survey wave will measure whether the enterprises made good on these budget plans: whether their agents gained scoped identities, whether evaluations got tested against production outcomes, whether GPU utilization rose, and whether the semantic layers under construction shipped.

VentureBeat will release the full Q2 reports across all five VB Pulse trackers at VB Transform, July 14–15 at Hotel Nia in Menlo Park, where we convene enterprise technical leaders building autonomous agents in production. 

Disclosure: VentureBeat produces both this research and VB Transform

Enterprise AI teams are giving agents more freedom at the same moment their confidence in automated testing is collapsing.

Half of enterprises have deployed an AI agent or LLM feature that passed internal evaluations and yet still caused a customer-facing failure — one in four more than once — according to the June 2026 VB Pulse survey of 157 qualified enterprise respondents at companies with 100 or more employees.

The sample is self-selected rather than a probability sample, so the findings should be read as directional, not precise.

But enterprises are not responding by slowing automation: 66% of respondents already permit some production deployment without human review or are building systems intended to do so within the next 12 months. Only 5% say they fully trust the automated evaluations that would make those release decisions.

That mismatch is the evaluation gap: the autonomy ceiling is rising faster than the assurance beneath it. 

It also fits a broader thesis that will be explored at VB Transform 2026: enterprises ship agents first, while the control layers around identity, evaluation, cost, context and orchestration are arriving later. The next year will be a retrofit cycle, with buyers shifting budget toward the systems that make agentic deployments governable and dependable.

Why a passing evaluation is not a working agent

Traditional software testing usually asks whether a defined input produces an expected output. Agent testing is harder because the system may choose its own sequence of steps, call tools, retrieve data, alter state and respond differently from one run to the next.

An agent can make several individually plausible decisions and still reach the wrong result. It may retrieve the correct account but update the wrong field. It may draft a valid refund request but send it without approval. It may call five tools successfully before a sixth step leaks sensitive information or leaves a workflow incomplete.

The survey shows enterprises already recognize this limitation. The most common reason for distrusting automated evaluation is poor alignment with real-world outcomes, cited by 29% of respondents. Bias or inconsistency follows at 21%, lack of explainability at 18%, and data leakage or privacy concerns at 17%.

That hierarchy matters. Enterprises are saying the score often does not predict what happens when a customer, employee or business process encounters the agent in production — not that automated scoring is too slow or expensive.

NIST makes a similar point in its Generative AI Profile: measurements gathered in controlled environments may not transfer cleanly to deployment because behavior changes with prompts, users, context and operating conditions. Its guidance calls for field testing, post-deployment monitoring and clear processes for escalating failures.

Capability is not consistency

A single successful run proves that an agent can complete a task. It does not prove that it will complete the task reliably.

Anthropic’s guidance on agent evaluation distinguishes between measuring whether a system succeeds at least once across repeated attempts and whether it succeeds every time. That distinction is essential for customer-facing or operational workflows. A model that occasionally produces an excellent answer may still be unacceptable if the same task fails unpredictably on the next attempt.

Enterprise teams should therefore treat repeatability as a first-class metric. That means running the same scenario multiple times, varying phrasing and context, testing tool failures, and measuring whether the final business outcome remains correct even when the route changes.

The evaluation set also has to evolve. Every production incident should become a permanent regression test. Customer escalations, failed tool calls, incorrect approvals and data-handling mistakes should feed back into the pre-deployment suite rather than remaining isolated support cases.

Autonomy should expand by risk, not by ambition

The survey does not imply that every agent action should require a person. Human review cannot scale across millions of low-consequence decisions.

But zero-human operation should be earned by demonstrated reliability and bounded by the consequences of failure.

Low-risk actions such as drafting internal summaries or categorizing documents can tolerate broader autonomy. Financial transactions, customer communications, code deployment, access-control changes and data deletion need stricter thresholds, repeated consistency tests, policy checks, rollback mechanisms and clear human escalation paths.

The risk isn't evenly distributed by company size, either. Larger enterprises — those with 2,500 or more employees — are moving toward zero-human deployment fastest, at 70% versus 64% for smaller companies, and they're also shipping more agents that go on to fail a customer, at 54% versus 48%. 

That is the warning for enterprise leaders. Removing the human from the loop does not remove uncertainty. Without stronger assurance, it converts uncertainty into an automated production decision.

The market will keep pushing toward greater autonomy because the economic incentive is real. The organizations best positioned won't be those that remove people fastest — they'll be the ones that treat repeatability and regression testing as seriously as deployment speed.

The vast majority of business data is tabular — living in data warehouses, CRMs, and financial ledgers — yet building a reliable model from it still means training a new one from scratch for every dataset, then maintaining hyperparameter tuning loops, feature engineering, and retraining pipelines to fight data drift. Google Research is proposing a way around that: a new foundation model called TabFM that treats tabular prediction as an in-context learning problem instead.

It can generate predictions for a new, unseen table in a single forward pass. For enterprise developers and AI engineers, this reduces the time-to-production from weeks of pipeline engineering to a single API call.

The challenge with traditional ML

To extract reliable predictions from a gradient-boosted tree, data scientists must build and maintain complex data pipelines. They have to clean messy inputs, impute missing values, encode categorical variables into numerical formats, and engineer custom feature crosses.

Once the data is ready, they must run repetitive hyperparameter optimization loops, searching across learning rates, tree depths, subsampling ratios, and regularization grids to find the best configuration. 

Once deployed, these traditional models "incur ongoing operational debt through data drift monitoring and retraining pipelines to stay accurate," Weihao Kong, Research Scientist at Google Research, told VentureBeat.

Meanwhile, the rest of the AI industry has moved on. Generative AI models for text and computer vision have seamlessly shifted to zero-shot inference, where a model can perform a completely new task simply by being prompted with context. 

Large language models (LLMs) already excel at in-context learning, so why can't we just feed tables into an off-the-shelf LLM?

Because LLMs are trained on natural language rather than structured data, they struggle to process tables directly. First, their context limits are exhausted quickly by medium-sized tables containing just a few thousand rows and hundreds of columns. Second, LLMs suffer from tokenization inefficiency, awkwardly splitting numerical values and destroying mathematical precision. Finally, they suffer from structural blindness. When a 2D table is serialized as a 1D text string, LLMs lose track of which value belongs to which row and column as the table grows. 

"That's why, today, it is far more effective to use an LLM to write the code that handles feature engineering and calls XGBoost than to ask the LLM to read the table itself," Kong said.

What is TabFM?

To run inference with TabFM, you do not update any model weights. Instead, you take your historical examples (the training rows with their known labels) and your target rows (the new data you want to predict) and pass them to the model as a single, unified prompt. The model learns to interpret the relationships between columns and rows directly from this context at runtime.

For example, consider an enterprise analyst trying to predict customer churn. Instead of building a bespoke data pipeline and training an XGBoost model, they can simply pass a sample of historical user session data alongside a new, active session into TabFM. In one forward pass, the model returns an instant churn probability. 

TabFM overcomes the limitations of LLMs by treating the data as a grid, preserving its structural integrity without forcing it into a single-dimensional text string.

To effectively process diverse tabular structures while enabling scalable zero-shot prediction, TabFM synthesizes the strengths of earlier experimental architectures, TabPFN and TabICL. TabPFN, developed by Prior Labs, first proved that a transformer architecture could perform zero-shot classification on small tables, though it struggled to scale computationally to larger datasets. 

Later, TabICL, developed by France's National Research Institute for Digital Science and Technology, addressed this bottleneck by introducing row compression, allowing in-context learning to efficiently process much larger tables. 

TabFM combines TabPFN's deep feature contextualization with TabICL's efficient compression into a novel hybrid design built on three key mechanisms:

1. Alternating row and column attention: The raw table is first processed through a multilayer attention module that alternates across both columns (features) and rows (examples). By continuously attending across these two dimensions, the model natively captures complex feature interactions. This deep contextualization does the heavy lifting that would usually require tedious manual feature crafting by data scientists.

2. Row compression: Following this contextualization, the cross-attended information for each row is compressed into a single, dense vector representation. TabICL pioneered this by using CLS tokens to compress a row's rich information into one vector, "in contrast to TabPFN v2, v2.5, and v2.6, which attend over the full cell grid throughout the network," Kong explained. This drastically shrinks the computational footprint.

3. In-context learning (ICL): A causal Transformer then operates on this sequence of compressed embeddings. This Transformer model uses the attention mechanism of TabICL to attend over these dense row vectors, drastically reducing the computation cost and allowing the model to process large datasets efficiently.

A major selling point of TabFM is its pretraining recipe. The model was trained entirely on hundreds of millions of synthetic datasets. These datasets were dynamically generated using structural causal models (SCMs) that incorporate a wide variety of random functions. By training exclusively on synthetic SCMs, TabFM learned the fundamental mathematical priors of how tabular features interact without ingesting real-world, confidential CSV files.

TabFM in action

To test the model's capabilities, Google researchers benchmarked TabFM on TabArena, a comprehensive evaluation suite spanning 51 diverse tabular datasets across 38 classification and 13 regression tasks.

On these public benchmarks, TabFM's zero-shot predictions already match or beat heavily tuned supervised baselines. However, Google is careful to note that this does not automatically mean TabFM will universally dethrone bespoke, hyper-optimized production models on every enterprise workload.

"Instead of replacing hyper-optimized production models, the true practical business value it unlocks for lean engineering teams is velocity," Kong said. "It allows data analysts and backend engineers to instantly spin up high-quality baseline models without a dedicated data science team managing a complex lifecycle."

For advanced practitioners looking to squeeze out maximum accuracy, the research team also introduced a "TabFM-Ensemble" configuration. By running the model through 32 distinct variations and blending the results, TabFM pushes the performance even further. 

Getting started, trade-offs, and the cloud future

The shift to in-context learning for tables introduces a new economic trade-off that engineering teams must consider. 

With traditional algorithms, training is slow and expensive, but inference is lightning-fast and cheap. TabFM flips this dynamic. While training time drops to zero, inference becomes significantly heavier. Because the model must process the entire historical dataset as context during every single prediction, it requires more compute and memory at runtime. 

In this new paradigm, "traditional machine learning training becomes the 'prefill' phase (KV caching) in the context window," Kong said. While this prefill cost is steep, it is paid only once per table, and the cache is reused across subsequent queries. "The catch is prediction latency, which no amount of caching removes," Kong added. Every new prediction requires a pass through a large transformer. "Any production API requiring single-digit-millisecond response times cannot tolerate TabFM's forward-pass overhead."

For developers looking to evaluate the model today, the barrier to entry is low. Google designed TabFM as a drop-in replacement for traditional ML workflows, offering a scikit-learn compatible API (TabFMClassifier and TabFMRegressor). It natively handles mixed numerical and categorical columns, works directly with pandas DataFrames, and requires no manual ordinal encoders or numerical scalers. The library supports both JAX and PyTorch backends.

However, enterprise teams need to be aware of current limitations and licensing restrictions. The model architecture has a hard limit of 10 output classes for classification tasks, and it is optimized for tables with up to 500 features. More importantly, while Google released the underlying codebase under the permissive Apache 2.0 license, the pre-trained model weights are published on Hugging Face under a strict tabfm-non-commercial-v1.0 license. Developers can evaluate the model internally, but it cannot be deployed in commercial products yet.

Looking ahead, Google is addressing the commercial deployment friction through its cloud ecosystem. TabFM is being integrated directly into Google BigQuery, allowing analysts to run zero-shot predictions natively via an “AI.PREDICT” command. By putting foundation model inference right next to the data warehouse, TabFM could soon make complex tabular machine learning as accessible as a basic database query.

In practice, TabFM shines in rapid prototyping, high data drift environments, and small to medium-sized datasets under 100,000 rows. Conversely, teams should stick to traditional models for strict, ultra-low latency APIs, or massive tables exceeding one million rows, which currently require aggressive row sampling that degrades the foundation model's competitive advantage.

Share one API key across five AI agents, and a single compromised agent inherits the reach of all five. The attacker immediately benefits from the accumulated permissions of every workflow that the key touches. The forensic trail goes cold at the credential level because five agents on one account leave no record of which agent did what.

Sixty-nine percent of enterprises run agents with credential sharing somewhere in their deployments, according to VentureBeat’s June 2026 Pulse Research wave of 107 enterprises.

That one number explains the buying spree reshaping enterprise security this year. Palo Alto Networks, CrowdStrike, and Cisco have collectively bet more than $22 billion on it in the past year, targeting exactly the layer most enterprises in this survey haven't finished building.

Palo Alto Networks completed its acquisition of CyberArk on February 11 for $21.1 billion in total consideration at close — a deal it announced last July at roughly $25 billion and the largest in the company's history.

CrowdStrike closed its $740 million acquisition of runtime authorization platform SGNL and, by June 15, shipped the first product from the deal, Continuous Identity for AI Agents. CrowdStrike integrated SGNL in less than a year, delivering a product that validates every agent action in real time based on who owns it, who is calling it, and the device's risk posture.

Cisco announced its intent to acquire non-human identity specialist Astrix Security on May 4 for a reported $400 million.

For a security director, this survey reads as a board-level question, not a trend line. It also surfaces a finding no competitor’s data shows, one that exposes which companies are the most at risk.

The data below is the first look at VentureBeat’s Q2 Agentic Security report, drawn from 107 qualified respondents at organizations with more than 100 employees. The full report will be released to attendees at VB Transform, the event in Menlo Park next week (July 14-15) focusing on enterprise autonomous agents.

Forty-five percent are final decision-makers for AI purchases. The sample skews mid-market, so read the numbers as the view from organizations adopting agent security right now rather than from the largest enterprises.

More than half of respondents, 54%, have already had an agent security incident or near-incident. Eighteen percent confirmed an incident, and thirty-six percent caught a near-miss before a breach. Security teams are stopping most of these events at the last control point in the chain, but the rest of the data shows how thin that margin is.

Your agents are sharing credentials

Only 32% of enterprises give every AI agent its own scoped, managed identity. Nearly half (48%) report that some agents have scoped identities, while many still share credentials. Another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. The survey question allowed more than one selection, and 24 of the 107 respondents chose multiple options — which is why the three categories sum to 112%. Deduplicated by respondent, 74 organizations, or 69%, flagged credential sharing in at least one answer.

One number explains why the acquisitions target this layer. A shared credential converts a single compromised agent into many, and CyberArk's research puts machine identities at 82 for every human in organizations worldwide, with agents as the fastest-growing category of the ratio. Cisco made the same diagnosis when it bought Astrix, whose founders built the company around API keys, service accounts, and OAuth tokens. Cisco’s announcement calls those the credentials AI agents are now “using (and abusing)” to execute work at scale.

Adam Meyers, senior vice president of counter adversary operations at CrowdStrike, described the mechanism directly in an interview with VentureBeat. Some AI systems have their own identities, he said, and in other cases “people give their identity to the AI to take action on their behalf, and that also further kind of murkies the water and makes it very complex.” The murk is the point, because when the identity is shared, attribution dies with it.

Exposure scales with size, and containment does not

Forty-nine percent of enterprises enforce scoped permissions at runtime, and 47% monitor and log agent activity, which can help reduce security incidents. Only 30% sandbox their highest-risk agents, the one control that limits blast radius when the first two fail. Isolation is what keeps a single compromised agent from becoming a deployment-wide event. Enterprises have funded detection and resistance, but the containment layer barely exists.

The sharpest finding in the survey, and the one no vendor report captures, shows up when you split results by company size. The incident rate is 49% for companies with 101 to 1,000 employees, but it shoots up to 63% for companies with more than 1,000. Sandbox isolation moves the other way, falling from 35% to 20% at the larger companies.

The chart above shows the same finding at finer granularity: the 49%/63% split above is a binary cut at 1,000 employees, while the bars here break incident rate and isolation rate into four size bands. The red line measures incidents and near-misses, and the navy tracks the one control that contains damage after everything else fails. At organizations with 101 to 250 employees, the two sit 7 points apart, but above 5,000, the gap blows out to 60 points. That top band pools the survey's two largest size groups and holds only 15 respondents, so treat the number as directional. Larger enterprises run more agents across more systems, which drives incidents up while sandboxing, the engineering project that would contain them, goes unfunded. The enterprises with the most agents have the least isolation around them.

The deals target exactly those accounts. Palo Alto Networks, Cisco, and CrowdStrike sell to large enterprises first, where incident rates are highest and containment is the thinnest.

Guarded by whoever shipped the model

The model providers are the security layer. OpenAI's built-in guardrails lead at 51%. Google Cloud reaches 36%, Microsoft Azure's Purview and Copilot Studio DLP 35%, and Anthropic's managed-agent controls 29%. Eighty-two percent of respondents name a provider-native or hyperscaler control as their single primary agent security layer.

The purpose-built specialists are in single digits, with Palo Alto Networks' Prisma AIRS at 7%, CrowdStrike at 6%, and Okta for AI Agents at 4%. Zenity and the dedicated non-human identity platforms are at 3% each. Microsoft Entra Agent ID is the highest-penetration identity-specific control in the dataset at 13%, the only one from a hyperscaler, and it still falls outside the top four. Only 5% of enterprises run no dedicated agent tooling at all, and the rest have tooling that came pre-installed.

Bundled controls lead because they ship free and are enabled by default. Most filter prompts and outputs, but they do not give an agent its own identity or sandbox it. Hyperscalers sell identity-layer products, and Entra Agent ID is in the dataset at 13%, but adoption stays low. The two controls that reward incident data the most, scoped identity and isolation, are the two that the default stack does not include.

Prompt-and-output filters evaluate whether a call looks malicious. That is an intent problem, and intent cannot be solved at the language layer. CrowdStrike CTO Elia Zaitsev drew the line in an interview at RSAC 2026. "Observing actual kinetic actions is a structured, solvable problem," Zaitsev said. "Intent is not." CrowdStrike's Falcon sensor walks the process tree on an endpoint and tracks what agents did, not what agents appeared to intend. A scoped identity and an isolation boundary give that sensor something to track, while a shared credential on a bundled guardrail does not.

Cloud security went through the same cycle a decade ago, and Palo Alto Networks, CrowdStrike, and Wiz built multi-billion-dollar businesses on the gaps native cloud controls left open. Agent security is tracking the same path faster. A misconfigured storage bucket sat open until a human noticed. A misconfigured agent exploits its own over-permissioning on every run, and no human is watching when it does. Merritt Baer, chief security officer at Enkrypt AI and a former deputy CISO at AWS, told VentureBeat that the default layer is thinner than enterprises assume. "Enterprises believe they've 'approved' AI vendors, but what they've actually approved is an interface, not the underlying system," Baer said. "The real dependencies are one or two layers deeper, and those are the ones that fail under stress."

Comfortable, unconvinced, and already shopping

Here is the contradiction worth a keynote slide. Enterprises rate their agent security tooling 4.2 out of 5, with value for money at 4.1 and ease of implementation at 3.9. Those scores would make most SaaS vendors envious.

Only 35% believe their AI-enabled defenses are ahead of AI-enabled attackers, while thirty-two percent call it roughly even. Twenty-one percent say attackers lead, and another 21% say it is too early to tell, showing how enterprises trust their tooling more than they trust its outcomes.

Budgets confirm it. Forty-six percent allocate 6 to 10% of the security budget to agent security, and a full third spend 5% or less. Half the sample has already had an incident or near-miss, but the funding does not match the exposure.

Fifty-nine percent plan to adopt, add, or replace agent security tooling within 12 months, and twenty-nine percent plan to move this quarter. OpenAI leads forward interest at 34%, followed by Google at 30%, Anthropic at 29%, and Azure at 25%. The dedicated vendors draw more interest looking forward than their current single-digit footprint suggests. Satisfied customers do not reshuffle this fast unless they know the stack they're currently using is provisional.

Three moves for security directors

1. Inventory every agent’s credentials this quarter. Map which agents share credentials with other agents and which run on borrowed human or service-account identities. The goal is not one credential per agent. Agents that touch multiple systems need multiple scoped identities. The goal is zero shared credentials between agents and zero borrowed human identities. Thirteen percent of surveyed enterprises already run Microsoft Entra Agent ID. Okta for AI Agents and the non-human identity specialists sell equivalents. Shared and borrowed credentials are the first thing to eliminate.

2. Sandbox the riskiest agents first. Isolation is the least-adopted control at 30% and the only one that contains blast radius after prevention fails. Rank agents by the sensitivity of what they touch and isolate the top of the list. Above 1,000 employees, where isolation falls to 20%, this is the single highest-return move in the dataset. Sandboxing does not require replacing the agent or the platform. It requires a policy decision and an isolation layer.

3. Match the budget to the incident rate. A third of enterprises fund agent security at 5% or less of the security budget, even though more than half have already had an incident or near-miss. Nine percent allocate more than 25% today. The full report breaks out exposure and containment by company size, showing which bands carry the most risk and the least protection.

The board's question is simpler. If one of our AI agents was compromised this afternoon, which systems did it touch, and whose credentials was it holding? For the 69% of enterprises running agents on shared credentials, the answer is a shrug. The trail goes cold at the key.

The full Q2 Agentic Security report, with the complete vendor matrix, industry cuts, and the full dataset behind these charts, debuts July 14 and 15 at VB Transform, held at Hotel Nia in Menlo Park. The open question it leaves is whether enterprises close the agent security gap on their own terms, or whether a confirmed breach closes it for them.

A team routing queries across a coding specialist, a logic specialist, and a generalist model assumes each will cover the others' blind spots. A new study evaluating 67 frontier models from 21 providers shows that assumption is mathematically flawed — and the flaw has a name: the co-failure ceiling.

The assumption works like this: as long as two models don't usually fail on the exact same prompts, combining them is supposed to create a safety net against failures.

The real limit on orchestration is not how often models disagree, but the percentage of prompts where every model in the pool gives the wrong answer at once. By ignoring the co-failure ceiling, enterprises are building complex, expensive routing infrastructure to chase performance gains that do not exist. Fortunately, developers can use this same math to build a cost-free test that determines exactly when multi-model orchestration will actually pay off.

The hidden costs of the multi-model strategy

To orchestrate multiple language models, developers typically rely on three architectures. Model routers act as traffic cops, sending complex queries to expensive models and simple queries to cheaper ones. Cascades send every prompt to a cheap model first, only escalating to a premium model if the initial system signals low confidence. Finally, approaches like Mixture-of-Agents (MoA) fuse multiple models by asking them the same question and generating a synthesized answer from their combined outputs.

These architectures introduce a "shadow price" to inference costs. Every time a development team implements a router or a cascade, they pay a premium in added system latency, complex infrastructure maintenance, and increased governance risks across multiple API providers.

To justify these operational costs, engineers rely on “pairwise error correlation” to select their model pool. Imagine a developer has Model A, which writes excellent Python but fails at SQL, and Model B, which writes excellent SQL but fails at Python. Because they fail on different types of prompts, their pairwise error correlation is low. The developer assumes that by placing a routing layer in front of them, they have created a composite system that rarely fails at coding.

According to the study, throwing diverse models together based on low correlation can actually hurt performance if the models are not equally capable — when you vote across diverse but unequal models, the weaker ones often gang up and outvote the smartest one.

Josef Chen, author of the paper, told VentureBeat that in their experiments, "Naive majority voting across unequal models had negative mean gain (minus 10 points on our hard mix): diverse-but-weaker members outvote the strong one." The actionable advice for developers is to "combine only models within a matched quality band." If you cannot match quality, take the single-model baseline and spend your budget on the best model available.

The paper provides one bright spot for this approach regarding MoA architectures. When building ensembles, teams often use "Self-MoA," where they query the same premium model multiple times to generate a synthesized answer. The researchers found that at matched quality, building a diverse ensemble of models with low pairwise correlation beats a high-correlation Self-MoA setup.

However, when teams use that same pairwise correlation metric to predict the absolute accuracy of their overall system, the math breaks down.

"So teams pay the orchestration overhead up front (latency, complexity, multi-provider operations) on the assumption that a diversity dividend arrives later," Chen said. "Usually it doesn't, because today's best models agree, and, worse, they fail on the same queries … the prompt simply carries little signal about which model will be the one that's right when the frontier disagrees."

Why the math fails: the co-failure ceiling

The core finding of the study centers on a metric called the "co-failure rate" — the formal name for the all-wrong scenario described above. No router, voting system, or cascade can ever achieve an accuracy higher than the ceiling it imposes.

The coding, logic, and generalist pool shows low pairwise correlation on routine prompts — they rarely fail together. But the co-failure ceiling represents the obscure, highly complex edge case that pushes past the limits of current AI architectures. If a prompt is so difficult that all three models hallucinate or fail, it does not matter how intelligently the router distributes the task. The entire pool wipes out at once.

The researchers tested their 67-model pool, which included GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro, on the open-ended MATH-500 math benchmark. Based on standard pairwise correlation, statistical models predicted that the entire pool would wipe out simultaneously on only 2.3% of the questions. In reality, the co-failure rate was 5.2%.

Standard correlation metrics underestimated the failure rate by roughly 2.25 times. The culprit is not just independent difficulty, but a shared failure point.

"The driver is what we call a common-mode atom: a slice of queries on which the entire market fails together, which no pairwise statistic can see," Chen said. "Adding a 20th model to your pool doesn't buy tail coverage. The tail is shared."

The researchers also found that task format directly triggers co-failure. When they took graduate-level science questions from the GPQA benchmark and changed them from multiple-choice to free-response formats, the all-wrong tail expanded to 12.7%.

Developers can engineer around the ceiling, though. "The engineering implication is uncomfortable: multi-model setups buy the least exactly where teams want them most, on open-ended generation," Chen said. "Anywhere you can convert generation into verification or constrained selection (structured outputs, checkable answers, execution tests), you reopen the ceiling."

Ultimately, the researchers found this ceiling limits AI applications in two distinct ways, depending on the domain:

• Ceiling-bound environments (e.g., open-ended math): The co-failure rate is high. The task is too hard, and all models fail simultaneously. No amount of routing can bypass the lack of underlying capability.

• Realizability-bound environments (e.g., graduate-level science): The co-failure rate is near zero, meaning at least one model in the pool usually knows the answer. However, the models disagree so subtly that a routing layer cannot reliably pick the correct answer without an omniscient oracle.

The $0 pre-deployment sanity check

Before dedicating engineering hours to building a router, teams can calculate their absolute performance ceiling for free using a mathematical formula called a Clopper-Pearson bound.

The Clopper-Pearson bound operates as a worst-case scenario calculator. If you flip a coin ten times and get eight heads, you cannot guarantee the coin will land on heads 80% of the time forever. The bound takes a small sample of test questions and outputs a mathematically guaranteed ceiling.

Applied to language models, suppose a team tests a pool of five agents on 50 sample queries and finds they all fail together on just two questions. A developer might assume their multi-agent system will achieve 96% accuracy in production. The Clopper-Pearson formula corrects this optimism. It analyzes the small sample size and provides a mathematical guarantee that the true co-failure rate could actually be as high as 12%.

To use this in practice, enterprises must build a held-out dataset. A fintech company, for example, could take 200 complex customer support tickets from the previous quarter and have human agents write perfect resolutions to serve as a benchmark. While this sounds like a heavy manual project, mature engineering teams can automate the entire ceiling calculation.

"Integration is trivial: it's a counting job over eval logs teams already produce," Chen notes, "so it runs in the same CI stage as the eval suite and re-triggers whenever the model pool or the workload changes."

The engineering team then runs its candidate models against these 200 tickets once and records the results. When they want to evaluate multi-model configurations, they can use the co-failure rate measure to predict the maximum accuracy they can get from the system without running extra queries.

One important conclusion the study draws is that on tasks where answers can be definitively checked, combining models rarely beats using the single best model on the market, unless the team possesses an exceptionally strong query-level routing signal.

In an enterprise environment, a definitively checked task has an objective, zero-tolerance answer. This includes generating a SQL query that must execute without error, extracting a specific invoice total from a 50-page PDF, or formatting a JSON payload that perfectly matches a strict schema. For these tasks, enterprises are usually better off paying a premium for the smartest frontier model rather than weaving together three cheaper models and hoping a router picks the correct output. The study didn't test subjective, ungraded tasks like drafting marketing copy — the authors note that whether these findings hold outside their verifiable benchmarks remains an open question.

Because this mathematical check is free, enterprise teams can track their own co-failure rates as new models drop.

"The measurement costs nothing, so any team can track its own co-failure rate across model generations and watch whether the tail is closing," says Chen. Ultimately, "the lever buyers hold is failure-mode heterogeneity and market churn, not model count."

Presented by SAP

Generating code with AI is fast, but getting that code to run reliably inside a large enterprise, integrated with live systems, governed for compliance, and maintainable over years requires foundational work that most organizations underestimate.

While 81% of all organizations have a detailed strategy, only 12–16% reach AI‑driven execution, says SAP's Michael Ameling, CPO of SAP Business Technology Platform, and the reasons rarely come down to the quality of the generated code.

"Across industries, enterprises that have invested heavily in AI tooling are hitting a wall when generated code meets the reality of their existing environments, because generating code and operationalizing it are not the same problem," Ameling says.

There are specific requirements for deploying AI-generated logic at enterprise scale: what data and integration readiness actually look like, how governance works when AI agents move from producing recommendations to executing workflows, and how development teams are changing their role as AI takes over more of the coding work.

Why AI code generation fails in enterprise production environments

The productivity gains from AI code generation are real and well-documented, but the ease of prototyping has given many organizations a misleading sense of how far along they actually are.

"Generating code is one thing," Ameling says. "Enterprise customers, including multinationals and large organizations, need to ensure there are no compromises in compliance or security. Code that runs reliably for ten or twenty years, as it does at many of SAP's largest customers, also has to be maintained, patched, and understood by whoever inherits it. Life cycle management, in other words, does not generate itself."

The issue is rarely the generation quality. Teams build something compelling, then discover they lack access to the data it depends on, or the integrations it assumes, or the permissions required to run it in a real environment. The problem is essentially that AI amplifies an organization's existing data and process maturity, but it can't substitute for it.

This dynamic intensifies as AI moves from producing code to executing actions. Latency, cost, and system load all increase when logic runs continuously against live data rather than rendering a one-time output. The performance requirements of an autonomous agent operating across a multinational's transaction systems are categorically different from those of a developer copilot.

How to connect AI-generated logic to fragmented enterprise systems

The architecture challenge that most enterprise AI projects underestimate is integration. Real enterprise environments are not clean slates: they combine cloud systems, legacy on-premise infrastructure, fragmented data stores, and dozens of business applications that were never designed to talk to each other. Getting AI-generated logic to operate reliably across all of them requires a layer that unifies data access, process context, and governance, and it has to be in place before any agent starts executing. And organizations that see AI as a reason to defer infrastructure modernization are making a mistake.

"The question is not whether to modernize or not. Of course you need to modernize," Ameling says. "But the value you get on top of this is much higher with AI. Federated data access and harmonized process layers are not alternatives to upgrading a fragmented landscape, they're what make the upgrade worthwhile."

At the platform level, this translates into a set of practical requirements: structured data integration, end-to-end process visibility, and the ability to discover and connect to APIs across both modern and legacy systems. SAP's approach with the Business AI Platform draws on tools including its Joule Studio, Integration Suite, Business Data Cloud, and SAP AI Agent Hub enterprise architecture layer to provide that context. The goal is to give AI-generated logic accurate, current knowledge of what a business is doing and how, rather than just access to raw data.

AI agents handle large challenges by dividing them into smaller, autonomous tasks, with each agent responsible for a specific domain, and all coordinated toward a shared outcome. A financial close, for example, involves dozens of discrete sub-processes. Agents handling each task in parallel, within defined constraints, can compress cycle times dramatically, but only if the underlying systems they interact with are coherent and accessible.

The governance and oversight that AI agents require in production

When AI moves from assistant to operational actor, the governance questions loom large, because agents that trigger workflows, update records, and interact with live business systems need the same accountability framework that applies to human employees, i.e., identities, defined privileges, and auditable behavior.

There are two distinct models:

Principal propagation, where an agent acts on a user’s behalf, inheriting that user’s permissions and scope.

System-triggered agents, where the agent operates under its own identity and role-defined privileges, functioning more like an automated HR role than a personal assistant.

Both models require the same underlying infrastructure: an agent hub where operators can see which agents exist, what APIs they can access, and what they are authorized to do. Observability also needs to be operationalized correctly for AI, combined with both technical and business evals.

"In production, openness is very important," Ameling says. "We use OpenTelemetry as a framework, so we can integrate with other solutions, for end-to-end observability of the tool, third-party agents and the like."

On top of that, standard technical evals, which test whether an agent produces consistent outputs, are necessary but not enough. Business evals assess whether an agent is actually moving the performance indicators it was deployed to improve, but it has to work end-to-end.

Where the testing happens is equally important. The traditional software development cycle across dev, test, and production environments breaks down when a model produces different outputs depending on whether it is running against test data or live data. Getting to trustworthy AI in production means accepting that validation looks fundamentally different from what engineering teams have practiced for decades, with live environment testing, even A/B/C testing to ensure outcomes are reliable.

How AI-driven code generation is changing software engineering roles

The role of the developer is not disappearing in this environment, but its center of gravity is shifting. The productivity multiplier is significant when developers can run multiple coding agents in parallel across open terminals, each working on a separate problem and each taking several minutes to complete. But it introduces a new kind of cognitive demand, because humans have to stay in the loop. That means tracking context across concurrent workstreams, evaluating outputs that range across large codebases, and making architectural judgments that no agent can be trusted to make alone.

"The more specific and complete the prompt, the less intervention is required, and developers are learning that bringing more context upfront pays dividends in reduced back-and-forth," Ameling says. "But the output still needs to be understood, not just accepted."

The competitive edge will remain intellectual property, not tooling. The companies that pull ahead will be those that most effectively encode their domain knowledge into the systems they build.

"A manufacturer's process expertise, a financial institution's risk logic, a logistics firm's routing intelligence, these are the assets that AI can accelerate, but only if the organizations that hold them do the work to make them accessible and usable," Ameling says. "Protect that, and apply AI to accelerate your differentiation."

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Presented by Oracle NetSuite

Every major technology transition produces a set of assumptions about where the market is headed. The assumptions are often directionally correct, but they tend to underestimate the degree to which organizations adapt new technologies to their own circumstances. AI is following a similar trajectory.

Many current discussions about enterprise AI assume a future in which employees interact with business systems through a common interface. The details vary depending on the prediction, but the destination often looks similar: a conversational system that becomes the primary way people access information, complete tasks, and interact with software.

The history of enterprise technology suggests a more complicated outcome. Organizations rarely adopt new capabilities uniformly because different parts of the business operate under different constraints. A finance team responsible for reporting accuracy, controls, and approvals approaches technology differently than an analytics group exploring operational data. Both groups have different requirements than a customer service organization focused on response times and case resolution. Even when there is broad agreement that a technology is valuable, the path to adoption tends to vary across functions.

The shift to cloud software followed this pattern — some organizations moved aggressively while others spent years operating hybrid environments. Different departments often modernized on different timelines, reflecting the priorities of the work itself rather than any industry consensus about the correct pace of adoption.

There’s no one-size-fits-all AI

AI has accelerated many aspects of technology development, but it has not changed this underlying dynamic. Organizations still evaluate new capabilities through the lens of existing processes, responsibilities, and operational requirements.

For some employees, the most useful AI capabilities may be the least visible ones. A finance manager closing the books is often less interested in a new interface than in shortening a reporting cycle. An operations leader dealing with inventory issues is usually focused on identifying problems earlier and resolving them more quickly. In these situations, the value of AI comes from reducing the amount of effort required to complete existing work.

At the same time, another group of users increasingly wants direct interaction with AI systems. Analysts, planners, and operational teams often benefit from the ability to explore information conversationally, compare scenarios, and investigate questions that do not fit neatly into predefined reports. For these users, the interface itself becomes valuable because it provides a more flexible way to work with business information.

A customer service representative handling a high volume of inquiries has different requirements than a financial analyst investigating a trend in operating expenses. One benefits from information appearing automatically within an existing process while the other may benefit from the freedom to ask follow-up questions, explore alternative explanations, and move through data more dynamically.

Many organizations are discovering that both patterns exist simultaneously, which reflects a broader reality about how businesses evolve. Operational complexity accumulates gradually, systems multiply, and processes become fragmented. Information becomes distributed across applications, reports, spreadsheets, and workflows and employees spend increasing amounts of time locating information before they can begin acting on it.

Much of the value created by enterprise software over the last several decades came from reducing that fragmentation. Bringing financials, operations, inventory, customer information, planning, and reporting into a common system created a more complete picture of how the business was operating.

AI is beginning to address a related problem. Once information exists within connected systems, employees still need to find it, interpret it, and apply it. Reporting cycles consume time. Routine questions require investigation. Managers often spend considerable effort assembling information before they can make decisions. As organizations grow, these activities become increasingly expensive because they consume attention from people whose expertise is often in short supply.

AI's promise is to reduce the effort required to move from information to action.

At Dura Software, AI-connected workflows are helping automate portions of revenue reporting that previously required manual preparation during each reporting cycle. Sloan Session, CFO at Dura Software, described the arrangement in practical terms: “The agents handle the pull. The humans handle the judgment and the personal touch.”

That observation captures an important aspect of current AI adoption. Most organizations are not attempting to remove judgment from business processes. They are trying to reduce the amount of time spent gathering, organizing, and preparing information so that experienced employees can focus on the decisions that require expertise.

A similar pattern emerged at S&B Filters. Employees previously spent several minutes during customer interactions collecting backorder information from multiple systems. By connecting AI to operational data, the company reduced that process to seconds and eventually extended the capability directly to customers through self-service.

Don’t forget about governance

In both cases, the benefit comes from reducing the friction associated with finding and using information rather than introducing a new interface. The moment information becomes easier to access, questions about access itself become more important. Permissions, approval structures, and security policies exist because businesses need mechanisms for controlling access to information and managing risk. Those requirements do not disappear when employees begin interacting with data through AI systems. If anything, they become more important because AI can make information easier to access.

Berry Carter, CEO of S&B Filters, described the principle clearly. If a user cannot access specific information within NetSuite, that user should not gain access to the same information through an AI assistant. The statement sounds obvious. Implementing it consistently across systems, workflows, and models requires considerably more discipline than the statement itself suggests.

Lauren Polasek, former NetSuite administrator and board member of the Texas NetSuite User Group, recently made a related point. Connecting technology is often the easier part. Organizations still need to determine which tools should be used, who should have access to them, and how governance should evolve as adoption expands.

This is one reason predictions about a single AI interface are difficult to reconcile with how enterprises actually operate. The requirements of a finance organization closing the books are different from those of a customer service team handling thousands of interactions each day. Some AI capabilities will be embedded directly into business processes where employees may barely notice them. Others will provide more direct access to operational information through conversational systems. Many businesses will end up using both approaches because the underlying work is different.

Have AI your way

That perspective has shaped how we think about AI at NetSuite. Some customers want AI embedded directly within operational workflows. Others want the ability to connect NetSuite data to external models and assistants so they can interact with business information through tools that are already part of their daily work. Increasingly, organizations are asking for both.

The NetSuite AI Connector Service and our support for Model Context Protocol (MCP) were designed with that reality in mind. The goal is to allow organizations to connect business information securely to the workflows and systems that make sense for them while continuing to benefit from AI capabilities built directly into NetSuite.

The history of enterprise software suggests that adoption rarely follows a straight line. As organizations adopt AI, business leaders should identify the business objective and the workflows involved so they can match the solution to the reality of the work.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.