AI trash-talkers love to rip on the technology for failing to produce meaningful business results, often pointing to studies like that from MIT NANDA, which reveals a 95% failure rate for enterprise AI solutions, or that from IDC, which states “only 9% of [Europe, Middle East, and Africa] organizations have been able to deliver measurable business outcomes from most of their AI-related projects over the past two years.” 

What many AI skeptics fail to account for is the experiential nature of AI prototypes; not all these projects are actually meant to move beyond the testing phase. Still, a 5% success rate is embarrassing. 

What’s the holdup? 

Two things. First, most organizations build AI prototypes on sand; that is, the data infrastructure on which they build early applications can’t support later moves to production. Meanwhile, the operational teams responsible for managing those applications in production often lack the human power to keep up with engineering’s growing output. 

4 reasons prototyping infrastructure  ≠ production infrastructure

When asked why so many AI prototypes don’t make it to production, Phillip Merrick, co-founder, CPO, and chairman, pgEdge, tells The New Stack that data infrastructure is largely to blame. 

Specifically, he explains that prototype environments don’t meet the requirements of large enterprises for production, naming four main ways they fall flat.

First, Merrick says prototyping environments lack the deployment flexibility organizations need to move from prototype to production. 

Vendor-managed cloud platforms, he acknowledges, may seem like an obvious choice for prototyping, as they allow teams to get up and running quickly. Still, he warns they lack the technical chops to support AI applications in production, especially in security, compliance, and governance. Particularly for organizations in healthcare, finance, or other regulated industries, vendor-managed cloud platforms often lack the stringent controls found in self-managed cloud or on-prem environments. 

“You’ve got to be able to choose where that AI prototype is ultimately going to be put into production.”

In this way, flexibility and security go hand in hand. Merrick asserts. “You’ve got to be able to choose where that AI prototype is ultimately going to be put into production.” 

Similarly, when it comes time to shift to production, Merrick says vendor-managed cloud platforms can introduce data sovereignty challenges at both the enterprise and regional levels. 

“Your data layer is obviously where you enforce this,” notes Merrick. But he says the environments most teams use for prototyping muddy the waters: “If it’s on a vendor-managed platform in who knows what cloud, what region, then you’ve lost data sovereignty.” 

Lastly, Merrick brings attention to reliability, explaining that AI prototypes can’t move into production without assurance of high availability. For example, when it comes time to upgrade the database or swap hardware, can it be done without downtime? 

“In the vendor-managed cloud world, the answer to that is almost always no,” says Merrick, reiterating his point that moving AI apps from prototype to production requires enterprise-grade infrastructure. 

So why are developers prototyping where they can’t productionize?

If data infrastructure selection is what’s holding developers back from moving AI prototypes into production, then why do they keep starting on the wrong foot? 

As Merrick explains, many are attracted to the ease of use of vendor-managed cloud platforms. “These prototyping environments admittedly make it very easy to get started,” he says. But prototyping shortcuts, it seems, don’t pay off in the long run, as someone else is ultimately on the hook for making those prototypes production-ready. 

Still, Merrick doesn’t blame developers for looking for the easy way out. Rather, he says there’s a disconnect between the prototyping playground and the production battleground that prevents developers from understanding what it will take to productionize prototypes down the pike. 

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More pgEdge articles in The New Stack

• Why developers are betting on Postgres for AI
• Moving AI apps from prototype to production requires enterprise-grade postgres infrastructure
• Reinventing PostgreSQL for the Next Generation of Apps
• How distributed Postgres solves the cloud’s high-availability problem
• Scaling AI inference at the edge with distributed PostgreSQL

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Years ago, he says, tooling decisions were primarily made top-down without developer input: “Then, starting 15–20 years ago, the developers won back, quite rightly, the power in being able to choose their own tools.” 

The problem now, Merrick claims, is that developers make tooling decisions exclusively for prototyping environments, without anticipating production needs. Internal divisions mean developers are often only responsible for building prototypes before passing the baton to an entirely separate operations team for production: 

“The upshot is you really don’t have, in some organizations, this throughline of understanding [of] what the production requirements are all the way back to the developer making the initial choices.” 

For Merrick, this disconnect is where AI projects start to fall apart, as teams are left trying to move AI prototypes from accessible-but-inadequate vendor-managed cloud platforms to enterprise-grade data infrastructure that meets requirements for deployment flexibility, security, data sovereignty, and high availability. 

“But if you make the right data infrastructure choice, you won’t have that disconnect,” he says, “because you’ll have this throughline from prototype to production.” 

He names Postgres as the data infrastructure that best helps developers bridge this divide, calling it “the Swiss army knife of databases” due to its extensibility, fully open-source nature, and ability to address diverse data management problems, from unstructured data to vector embeddings to geospatial data. 

Where and how Postgres is run matters too, Merrick points out, again drawing attention to the limits of many vendor-managed cloud environments that often lack the governance controls to meet data requirements and/or the deployment flexibility to shift to compliant on-premises or BYO cloud environments. 

But picking the right data infrastructure only solves half the problem

Merrick says there’s another part of the equation most organizations are overlooking: people, or more precisely, database administrators (DBAs) and their growing workloads. 

Per Stack Overflow’s 2025 Developer Survey, 84% of respondents use AI tools, up from 76% the year prior. Meanwhile, Supabase says over 60% of databases on its platform have been launched “by some sort of AI tool.” As Merrick points out, this explosion of productivity comes with a catch: there aren’t enough DBAs to keep up.

“You’ve had this massive, massive step shift in developer productivity,” he explains. “But you have to have some way of managing that on the production side; these databases can’t go unmonitored.” Operations and administration teams were already struggling to keep track of existing Postgres databases before agentic engineering added even more, he says: “Who’s going to manage them?” 

He says it’s time for agentic operations to catch up with agentic engineering. 

AI DBA agents can give humans “superpowers” 

If it seems Merrick is proposing organizations look to fully autonomous DBA agents to take over, he says the industry isn’t there yet: 

“The world is not ready for fully autonomous databases administered by AI DBA agents. But there is a massive resource shortage and productivity problem, and DBAs can only manage so many databases,” he explains. Meanwhile, new “AI applications require so many more databases to put in production.” 

“The world is not ready for fully autonomous databases administered by AI DBA agents.”

So how can organizations increase their operational capacity? 

Merrick says DBAs should look to new AI DBA agents, not to take over but to give them “superpowers” to monitor and manage more databases with less manual slog. 

pgEdge’s Ellie is one example. Part of the pgEdge AI DBA Workbench, Ellie is an AI agent that has 21 MCP tools and can run EXPLAIN ANALYZE, inspect schemas, query historical metrics, and walk through multi-step diagnostic workflows. When a database falters, Ellie finds the problem, diagnoses it, and provides a solution in the form of working SQL code for the human DBA to review. “When you’ve reviewed it and agree that it’s the right course of action, you literally press the play button, and the agent plays that SQL code into the database, and you solve your problem,” explains Merrick.

In this way, Ellie should bring more capacity to operations teams, where Merrick insists organizations are starved for DBA expertise. To his point, some industry predictions say 41% of today’s database professionals intend to leave the industry in the next decade, half moving into retirement and the rest seeking other work. 

“An agent … can actually respond to those alerts far more quickly and productively than a human can.”

Without AI agents, Merrick argues, DBA work is tedious, laborious, and time-consuming. As he explains it, a database may have been humming along just fine, but when there’s a snag, trouble can manifest across multiple applications; it’s then up to the DBA to comb through monitoring data to observe and diagnose the problem, essentially scouring for a needle in a haystack. 

“An agent,” he says, “can actually respond to those alerts far more quickly and productively than a human can.” 

Better infrastructure AND people: It takes two to improve AI prototype success rates 

In nearly any context, AI raises questions about quality over quantity, and enterprise AI projects are no exception. Agentic engineering means developers can now produce more, but all those prototypes don’t just fly directly into production. Limitations in both infrastructure and operational human power are creating obstacles that cause many AI prototypes to fail. 

For Merrick, easing the transition from prototype to production requires not only great AI tooling but production-ready data infrastructure, paired with agentic operations that can keep up with the agentic engineering boom. 

The post Why most AI projects fail: It’s infrastructure and people  appeared first on The New Stack.

Palantir CEO Alex Karp went on CNBC’s Squawk Box last week to discuss a new partnership with Nvidia to deploy open-weight AI models in sovereign government environments. But viewers got a nearly 20-minute broadside against the entire frontier AI model industry, calling it “effing insane” and accusing companies like OpenAI and Anthropic of overcharging enterprises while harvesting their proprietary data.

Days later, Mistral CEO Arthur Mensch made a strikingly similar case on LinkedIn, warning that closed AI providers are gaining “immense leverage” over enterprise customers as organizations connect proprietary workflows to hosted models. He suggests open-weight models, open data systems, and enterprises building their own training flywheels.

The two executives are approaching this from opposite ends of the market, yet their convergence on the same message within the same week underscores architectural control.

Two pitches, one argument

Karp runs a company that sells an application and ontology layer designed to sit between enterprises and the models. The Palantir-Nvidia deal pairs Nvidia’s open Nemotron models with Palantir’s Sovereign AI Operating System, built on AIP, Foundry, Ontology, and Apollo, enabling government agencies and critical infrastructure operators to deploy, fine-tune, and audit AI models within their own air-gapped environments.

When CNBC’s Becky Quick told Karp he sounded angry, he pushed back, saying, “This is the voice of American business that is being channeled through me,” and urged the panelists to call any CEO privately to verify.

“This is the voice of American business that is being channeled through me.”

Mensch’s company sells open-weight models, and he has a custom training platform called Forge, which frames the problem differently but reached the same conclusion. He argues in the post that closed providers have a track record of going after their most successful customers once they learn what those customers are building. His program runs from open models to open data stores, strict access controls, and a continuous training flywheel that improves systems on internal interactions.

Lock-in gets an upgrade 

If you’ve been building software at scale for any length of time, you already know that when new technology arrives, enterprises can’t help but rush to adopt it. But the dependency problem becomes impossible to ignore.

We saw the same problem with cloud computing when companies went all-in on a single hyperscaler’s proprietary services, only to later discover that the cost of switching providers could exceed the cost of staying, even when staying meant overpaying. It’s one of the reasons the industry spent years building abstraction layers, portability tooling, and multi-cloud strategies in response.

Foundation models are raising the same questions, but there’s a twist. When an enterprise connects a model to its internal data, including customer records, proprietary processes, and domain-specific knowledge, the dependency becomes informational. Karp’s argument is that model quality is converging across providers, but the operational leverage accrues to whoever controls the deployment layer and the data flowing through it.

Mensch’s claim has a concrete referent that enterprise architects will recognize. In 2025, Anthropic cut off model access to coding startup Windsurf while building its competing product, Claude Code. The Brookings Institution has separately warned that model providers increasingly compete with their own customers as they chase application-layer revenue.

When access disappears overnight 

When the U.S. government ordered Anthropic to suspend access to its most advanced models for foreign nationals, the company cut access across the board, including to enterprise customers in Europe who had built workflows on top of those models.

Access has since been restored, but for CIOs and enterprise architects who had treated model APIs as stable infrastructure, it was the same as if a cloud provider pulled compute resources without warning. It’s probably why the incident sent European policymakers into overdrive. Mensch, whose company had open-weight alternatives ready, seized the moment.  

Enterprise teams need a plan for when a critical dependency is modified, repriced, or revoked by a provider whose incentives may not always align with their own.

Enterprise teams need a plan for when a critical dependency is modified, repriced, or revoked by a provider whose incentives may not always align with their own.

Architecture shifts toward portability 

Enterprise AI architecture is now essentially this: don’t marry a single provider, build for portability, keep your most sensitive data and logic under your own control.

In practice, this is showing up in three ways.

Firstly, we’re seeing a portfolio approach. A powerful closed model is maintained for complex reasoning and customer-facing work, while an open-weight model is used for repetitive, high-volume tasks. For businesses handling sensitive information, the appeal is that an open model can be run entirely on their own infrastructure, so data never has to leave the organization.

The second is the rise of the model-routing layer, abstraction frameworks that let organizations swap models without rewriting their applications. Palantir’s ontology pitch sits here as does the emerging crop of agent orchestration tools that treat the LLM as a pluggable component behind a standardized interface.

The third is the open-weight movement itself. Nvidia shipped Nemotron 3 Ultra in June under a permissive Linux Foundation license. Meta’s Llama continues to expand. Mistral’s Forge platform lets enterprises train custom models on their own data. And Mistral is teasing an upcoming open-weight model this summer, with early access opening in July.

Follow the commercial incentives

It would be naive to ignore the commercial interests at play. Karp’s Palantir sells the deployment and governance layer; it benefits directly if enterprises treat models as interchangeable commodities. Mensch’s Mistral sells open-weight models and a training platform, and it benefits directly if enterprises distrust closed providers. Zoho’s Sridhar Vembu, who endorsed Karp’s position publicly last week, has his own reasons for wanting enterprises to own their AI infrastructure rather than rent it from Silicon Valley.

But the fact that multiple executives across different market segments are saying companies need to own their data, maintain deployment flexibility, and not hand their competitive advantage to a provider who might become their competitor suggests the argument is resonating.

What developers should watch

If your most sensitive data is flowing through a third-party API with terms of service that can change, you’ve made a governance decision that your compliance team may not have fully evaluated.

For the engineering teams actually building on foundation models, the takeaway is that if your most sensitive data is flowing through a third-party API with terms of service that can change, you’ve made a governance decision that your compliance team may not have fully evaluated.

The engineering choice, to abstract the model layer, to evaluate open-weight options alongside closed APIs, to think about deployment portability the same way you think about cloud portability, is increasingly strategic.

The post Palantir’s Alex Karp and Mistral’s Arthur Mensch agree: AI lock-in is coming for enterprises appeared first on The New Stack.

In April, Andrej Karpathy published a GitHub gist file called “LLM Wiki,” a brief text document designed to help one build a personal knowledge base using LLMs. It’s based on the premise that an AI agent will keep what it knows as linked Markdown files it can read and rewrite, because a language model does not get bored maintaining cross-references and can touch fifteen files in a single pass. It was only a few thousand words with no product attached.

Two months later, Google turned that instinct into a published standard called the Open Knowledge Format. The OKF packages organizational knowledge, metrics, tables, and runbooks as plain Markdown that any agent can read without a proprietary account. Google is careful to call it v0.1 — a starting point rather than a finished standard.

Garry Tan, the Y Combinator president, got there first in a different lane. His gstack, an MIT-licensed Claude Code setup that crossed 66,000 GitHub stars within weeks, comprises 23 specialist roles, each a Markdown file. No runtime; no code; just prose that runs across ten different coding agents.

Markdown has become the substrate agents read and write

Three approaches, three different needs, one common solution. Karpathy sought agent memory, Google aimed for enterprise context in BigQuery agents, and Tan wanted a way to summon an engineering team from a terminal. All three turned to the same basic resource: a folder of Markdown files versioned in git.

Developers had already established this practice. CLAUDE.md and AGENTS.md are present in millions of repositories as the initial files an agent loads. OKF and gstack are the evolved forms of this convention – one focused on what the agent knows, the other on how it behaves.

This is the Git and JSON playbook tied to the agent’s knowledge. The formats that survived are the ones you could start using without changing anything. You can simply cat the file, clone the repo, and any tool you already use can parse it. MCP remains important as the interface an agent connects to. Markdown is becoming the format that carries the content.

The lock-in moved from the model to the files

The significant factor to observe here is the competitive advantage, not technical specifics. For two years, the belief was that owning the best model meant controlling the developer.

This perspective is now shifting. Replacing Claude with GLM or Codex, gstack continues to operate because the core intelligence evolved, but the documentation did not.

The moat is shifting from the model to the Markdown a team owns and accumulates over time.

The moat is shifting from the model to the Markdown a team owns and accumulates over time. A company’s OKF bundle, including its runbooks, metric definitions, and architecture decisions, is, by design, portable across clouds, models, and frameworks.

That kind of portability is the reason vendor-neutral formats exist and why Google’s OKF deserves a closer look.

If no one develops consumers for it, it remains just a good idea that Google released on a slow Friday.

The area where I am most likely mistaken is durability. Declaring Markdown standards is easy, but making them reliable is difficult. OKF is merely a 0.1 draft with a reference implementation, not a full ecosystem. If no one develops consumers for it, it remains just a good idea that Google released on a slow Friday.

The direction remains determined by three separate bets targeting the same file format within a single quarter. Your next agent is likely to interpret its context from a Markdown folder, and the creator of that folder now possesses an advantage that the model vendor cannot easily replicate.

The post Andrej Karpathy, Google and Garry Tan agree Markdown is the answer, but they’re not solving the same problem appeared first on The New Stack.

Halfway through 2026, artificial intelligence has been at the center of every major story inside the world of software development and in just about every major story outside of it.

Last month, the Commerce Department ordered Anthropic to pull Fable 5 and Mythos 5 offline worldwide — only to lift the ban 18 days later. It wasn’t Anthropic’s only government clash; earlier in the year, the Pentagon fought the company over its refusal to give the military unrestricted model access.

Elsewhere, frontier labs planted their flag on Wall Street in May with new deployment arms and partnerships, while Anthropic and OpenAI both pursued IPOs at valuations above $800 billion. Underneath the valuations is an infrastructure buildout of chips, data centers, and deals meant to keep pace with model releases that land every few weeks.

Meanwhile, open-weight models are narrowing the gap between closed and downloadable, and the harness — the tools, memory, and orchestration around a model — matters more as agentic AI moves into the enterprise. Add tokenomics (the real cost of reasoning at scale) and non-technical execs vibe-coding their own tools, and you’ve got ten of the biggest AI moments of 2026 — so far.

Here are ten moments that have defined a pivotal first half of 2026 in the world of AI, as chosen by the editorial staff at The New Stack.

10. President Trump’s Executive Order on AI

President Donald Trump has been relatively friendly to AI, given his support from tech companies in Silicon Valley. On June 2, 2026, Trump signed an executive order aimed at hardening American systems against AI- related threats, while at the same time rejecting “overly burdensome regulation.” The order directs the Committee on National Systems Security to prioritize cyber defense and tasks the Treasury, the NSA, and CISA with establishing an AI security clearinghouse to coordinate vulnerability scanning and patching across critical infrastructure. The administration supports deregulation along with national security-driven AI oversight.

9. AI infrastructure buildout

Chipmakers and AI labs tightened ties in 2026 to keep pace with model releases. We also saw big moves, with Nvidia and SK Hynix striking a multi-year partnership spanning Vera Rubin supercomputers, Vera CPUs, and next-gen memory. Meanwhile, data center capacity has expanded globally. The buildout shows that compute, power, and hardware are now a bottleneck to AI’s growth.

8. The rise of the harness

“The harness is where the hard work is,” Harness CEO and founder Jyoti Bansal told The New Stack last month. As base models are performing closely on benchmarks, the harness becomes the differentiator. The harness is the tools, memory, orchestration, and guardrails wrapped around a model. The harness determines whether an agent stays on task, recovers from errors, and performs safely. The harness helps shift competitive advantage from model capability to system design.

7. Tokenomics

Spend is now the battleground for both AI producers and consumers. AI labs are restructuring pricing around compute consumption rather than flat subscription, and companies are looking for ways to cut their token spend. Last month, the Linux Foundation launched the Tokenomics Foundation with support from Google, Microsoft, IBM, JPMorgan Chase, KPMG, Oracle, and Salesforce. The organization is tasked with establishing open standards, benchmarks, and best practices across the entire AI token economy.

6. Agentic AI goes mainstream

A year ago, agents were largely proof-of-concept demos that were unreliable in production. In 2026, they became infrastructure. For instance, ChatGPT’s browsing agent, Claude’s tool use and multistep coding runs, and Google’s autonomous information agents now run continuously in the background rather than on command. Meanwhile, enterprises are adding agents into real workflows such as monitoring, code review, procurement, and customer support. However, this shift may carry security risks as agents gain access to data and systems.

5. The Pentagon goes to war with Anthropic

In February, Department of War Secretary Pete Hegseth summoned Anthropic CEO Dario Amodei to his office to demand that the military be granted unrestricted use of the company’s technology. But Amodei held his ground and refused to allow the military to use Anthropic technology for mass surveillance on citizens or for autonomous weapons. Following that, President Trump ordered federal agencies to phase out the use of Anthropic, and Hegseth designated the company a “supply chain risk,” which is a label previously used for foreign adversaries – basically blocking the company. Anthropic sued in federal court, claiming the government’s move was unfounded and retaliatory. The company received a preliminary injunction from a San Francisco court, holding that the government’s actions constituted unconstitutional First Amendment retaliation. 

Meanwhile, amid the initial fallout from Anthropic’s battle with the Pentagon, OpenAI made its own deal with the military.

4. AI titans plant their flags on Wall Street

Within 72 hours in May, Anthropic and OpenAI each launched enterprise deployment arms, announced major financial services partnerships, and shipped agent tooling targeting Wall Street workflows. The message was the same — the next phase of frontier AI is not about models. It’s about deployment. 

Anthropic’s new services firm — backed by Blackstone and Hellman & Friedman alongside General Atlantic, Apollo, Goldman Sachs, and Sequoia Capital — targets mid-sized enterprises that the large consulting and systems integration firms don’t prioritize. These include community banks, regional health systems, and mid-market manufacturers. Applied AI engineers from Anthropic embed directly with clients alongside the new firm’s own engineering staff, doing workflow discovery, building custom Claude-powered solutions, and supporting clients long-term.

OpenAI’s Deployment Company — “DeployCo” — operates one market segment up, targeting large enterprises with the same forward-deployed engineering model. Its acquisition of applied AI consulting firm Tomoro brings roughly 150 experienced Forward Deployed Engineers (FDEs) from day one, backed by more than $4 billion in initial investment and a partner roster that includes McKinsey, Bain & Company, and Capgemini.

Meanwhile, both companies are considering IPOs with valuations exceeding $800 billion.

And both companies are betting on the same thesis: that the deployment gap — the widening distance between what frontier AI can do and what enterprises have actually shipped — is the next major revenue opportunity. And both moved on it in the same week. 

3. Open-weight models are coming

Chinese labs continued to close the gap with Western frontier labs in 2026. Alibaba’s Qwen, Zai’s GLM and Moonshot’s Kimi, delivering open-weight releases that rivaled closed models on standard benchmarks. Zai’s GLM-5.2, released June 13, beat Anthropic’s Claude Opus 4.8 in some benchmarks – showing the highest marks for open-weight models. GLM-5,2 is also one-fifth the price of comparable closed models. 

“The industry is all focused on which lab has the smartest model, but that focus looks to the past,” David Mytton, CEO of Arcjet, told The New Stack. “GLM-5.2’s capability indicates that usage of open-source models is about to explode. “This all seems obvious in retrospect: models became more capable at the start of the year, agents started taking real actions (particularly since most of the work happens following the chat prompt), and legal restrictions on using frontier models are causing people to look elsewhere. This will cause all sorts of security issues because managing so many model capabilities will become difficult.”

Paul Sawers, contributing writer at The New Stack, tells us for this countdown: “Budget open-weight model panels are now matching frontier proprietary benchmarks at a fraction of the cost, undercutting the case for paying top dollar for a single closed model. Example of startup ditching Anthropic for DeepSeek.”

Indeed, the European AI agent startup Lindy AI migrated 100% of its production traffic from Anthropic to DeepSeek, citing millions of dollars in savings,

2. CEOs vibe coding their own tools

While software developers seem to have wholly adopted AI coding assistants, the business side of organizations — the C-suite and other executives high up in the chain of command — is adopting these tools to “vibe code” a variety of agents and productivity applications.

The trend runs from simple workflow automations to full production systems serving hundreds of users. The tools are Claude, Cursor, and, increasingly, the AI features embedded in the platforms these executives already run. The motivations range from impatience with IT queues to genuine curiosity about what the technology can do. And the results are more varied than the enthusiasm surrounding them might suggest.

Woodson Martin, CEO of OutSystems, took a more structured approach to his own vibe coding experiment. He built a personal mobile app wrapper on top of MCP services his team had created — and he built it twice in parallel, once using OutSystems’ own AI coding tool, Mentor, and once using Claude, connecting to the same backend both times.

“I was tired of explaining it to somebody who was supposed to build it for me,” Martin told The New Stack in April. “I was just like, ‘I’ll do this myself.’”

The app is a personal chief-of-staff system that consolidates customer account intelligence — buying signals, website activity, internal data — into a pre-meeting briefing he can pull up on his phone. It replaced what had been a 45-minute PowerPoint session plus multiple prep meetings from his sales team. 

1. The government cracks down (and later relents) on Anthropic Fable 5 and Mythos 5

The Fable/Mythos takedown illustrated how unpredictable AI policy has become. Anthropic launched Fable 5, its only Mythos-tier model, on Jun 9, 2026, with the fuller Mythos 5 reserved for a small set of trusted customers under what the company called Project Glasswing. The rollout lasted only three days.

On June 12, Commerce Secretary Howard Lutnick sent Anthropic CEO Dario Amodei a directive ordering the immediate worldwide suspension of both models for all foreign nationals, including Anthropic’s own non-citizen employees.

Apparently, the trigger was a jailbreak that Amazon researchers found, which could expose the models’ cybersecurity capabilities. This raised concerns at Commerce. Anthropic said it didn’t have a way to restrict access by nationality in real time, so it disabled Fable 5 and Mythos 5 globally. The Commerce Department partially opened Mythos 5 to select government-approved organizations in the following weeks, but the freeze didn’t lift until June 30 — after an 18-day period of limbo.

Meanwhile, Anthropic added extra cybersecurity safeguards to Fable 5 and began restoring global access on July 1.

Having mentioned Anthropic’s run-in with the Pentagon, Frederic Lardinois of The New Stack tells us: “While the two situations are not directly linked, it’s hard not to read the Fable controls as an extension of this existing animosity between Anthropic and the Trump administration — and in part, this seems personal as well.”

What’s next

What’s in store for the second half of 2026? We’ll be tracking how much AI-generated code actually makes it into production — and the tools designed to close that gap; the expanding autonomy of agentic AI and the guardrails keeping pace with it; the fast-moving regulatory environment around the frontier labs; and enterprise adoption of open-weight models.

We’re also watching AI’s spread among knowledge workers, the ever-longer stretches agents run unsupervised, and the hiring and performance of forward deployed engineers.

Whatever happens this year, The New Stack will cover it. And if you haven’t already, subscribe to The New Stack Daily newsletter for timely, thoughtful updates on how AI is reshaping software development.

The post 10 moments that defined AI’s turbulent first half of 2026 appeared first on The New Stack.

Finding the most capable model at the lowest cost has always been the goal. But as agentic AI evolves, a new problem is frustrating engineers: token consumption is becoming too high across AI systems. Every agent operation consumes tokens, but in very different ways. For instance, a moderately complex agent request can consume 20,000 to 60,000 tokens across its reasoning chain, but a nontrivial engineering task can burn 150,000 to 200,000 tokens per problem.

Every agent operation consumes tokens, but in very different ways.

Developers are realizing that selecting the right model is important, but the bigger issue is limiting unnecessary token movement throughout an agent’s workflow. That is why teams are starting to consider how to accomplish the same tasks with fewer tokens.

Compounding costs across agents

The costs add up quickly. A task that takes about 50,000 tokens with one agent can easily consume several hundred thousand with multiple specialized agents together. That’s because each one needs enough context to do its job. It’s not unusual for an agent to process 30,000 tokens of context just to return a 500-token response, and those exchanges add up over the course of a workflow. Each handoff effectively pays a tax in input tokens that compounds with every loop iteration.

Each handoff effectively pays a tax in input tokens that compounds with every loop iteration.

This is especially noticeable among multi-agent architectures. When one agent delegates to another, it must encode its current state and task instructions into the downstream agent’s context window. The receiving agent processes all of that, produces a result, and passes it back. Then the orchestrating agent reingests it alongside everything else it’s tracking. Every exchange adds another layer of overhead.

Building more token-efficient architectures

A growing collection of strategies addresses this problem. Three practical solutions stand out:

Compress context, preserve reasoning

The most direct solution is to reduce the amount of context an agent carries from step to step. Rather than accumulating an ever-growing interaction history and replaying it with every task, systems can summarize earlier portions of a conversation or working memory before passing them forward.

One way to do that is by narrowing the agent’s field of view. Instead of handing it an entire codebase or document collection, the system surfaces only what’s relevant to the task at hand. Go too far, though, and the agent can lose important context that it will need later.

To make this work, the system needs a compact memory layer of key facts and decisions alongside the compressed context. The agent needs to recall the reasoning chain without having to reread it each time.

Route tasks to cheaper models

Hierarchical routing lets engineers parse a JSON response, format a log entry, or check whether a file exists without using the same model used to architect a system design. It assigns each subtask to the smallest model that can reliably do the job. A lightweight model, for instance, handles routine classification, extraction, and formatting steps, while a more suitable model handles decisions that truly require deeper reasoning.

So if 60 to 70 percent of an agent’s steps are routine operations, then a smaller model can handle the work at a fraction of the cost, substantially reducing the overall token spend for the workflow.

Cache reasoning, skip redundancy

That’s where semantic caching comes in. Instead of solving the same problem twice, it compares the meaning of a new request using embeddings. If the match is close enough, the agent can reuse earlier work instead of generating a new reasoning chain.

The savings make a difference, especially in scenarios involving customer support systems that answer similar questions all day, or even document-processing pipelines that handle thousands of nearly identical files. In such scenarios, reusing prior reasoning can significantly reduce the number of tokens an organization consumes.

Measuring what matters

But these workflows only deliver value if teams see their impact, and many organizations are still figuring out how to measure them effectively. Poorly designed agent loops or inefficient multi-agent handoffs can dominate costs in ways that are invisible when you’re only looking at per-request pricing.

It’s also easy to focus too much on tokens. Running an agentic application also means paying for GPUs, memory, vector databases, and the tooling needed to monitor everything in production. Saving tokens helps, but it won’t solve the whole problem if the rest of the stack is still expensive.

The next phase of AI infrastructure

The next generation is increasingly focused on building systems based on better architectural decisions. That means context management, model invocation, task decomposition, and intermediate work reuse are becoming just as important as inference pricing or even benchmark scores.

Model capabilities will continue to improve, and inference costs will likely fall. But if autonomous agents become the dominant way organizations build AI applications, the systems that scale most effectively may not be the ones with the cheapest models, but those that waste the fewest tokens.

The systems that scale most effectively may not be the ones with the cheapest models, but those that waste the fewest tokens.

The post Why cheaper models alone won’t save your AI budget appeared first on The New Stack.

Cyberattacks used to be the biggest security issue surrounding AI infrastructure, but that could be changing. A recent cargo theft outside Chicago suggests another vulnerability — and it’s one that has nothing to do with malware or prompt injection.

Just last week, the Cook County Sheriff’s Office recovered two stolen trailers containing roughly $1.3 million in data center equipment and copper wiring, taken from separate shipments originating hundreds of miles away. One trailer held about $300,000 worth of copper wire — reported stolen in Pine Hill, Alabama — destined for data center construction. The other carried roughly $1 million in data center infrastructure equipment, stolen out of Jacksonville, Florida. Both ended up at the same truck yard in Elk Grove Township, outside Chicago.

Viewed in the context of the AI boom, it highlights that the physical supply chain itself is becoming a new target for bad actors.

Viewed in the context of the AI boom, it highlights that the physical supply chain itself is becoming a new target for bad actors.  

A new high-value cargo

We’re all familiar with typical bottlenecks like GPU shortages, power constraints and cooling capacity, which have plagued the AI era since its inception. But we forget that building an AI data center requires an enormous volume of specialized hardware moving through freight networks. These include servers, networking gear, fiber, switchgear, cooling systems, power distribution equipment and thousands of pounds of copper. Each represents capital investment and potential deployment delays.

As hyperscalers accelerate the construction of data centers, the exposure of these items between the factory and data center creates a risk category that the industry as largely ignored.

When one delay cascades

Large GPU clusters depend on the synchronized delivery of dozens of interconnected systems. A training cluster is a tightly coupled system of servers, switches, optics, power distribution, and cooling that must be installed together. Missing networking hardware can idle racks, delayed power equipment can postpone an entire deployment and stolen copper can stall electrical work. So when one component category disappears, the delay cascades across everything.

So when one component category disappears, the delay cascades across everything.

Cargo theft by the numbers

Infrastructure resilience increasingly depends on whether critical hardware arrives at the construction site at all — and on schedule. Verisk CargoNet reported that U.S. and Canadian cargo theft losses jumped roughly 60% in 2025 to nearly $725 million, even as the total number of incidents held essentially flat — a sign that thieves are becoming more selective about high-value freight. Metal theft rose 77%, driven largely by demand for copper, while organized groups shifted toward enterprise computing hardware. CargoNet expects that focus on high-value technology — RAM modules, storage drives and enterprise computing equipment — to carry into 2026. For broader context, the Department of Homeland Security has estimated that cargo theft overall costs as much as $35 billion a year.

The Chicago incident fits squarely inside that trend.

Beyond firewalls and malware

Obviously, cargo theft isn’t an engineer’s problem. But organizations building AI infrastructure may need to broaden their thinking about deploying AI capacity on aggressive timelines.

Cloud providers, colocation operators and hardware vendors have already invested heavily in defending infrastructure from digital threats. As AI infrastructure becomes more valuable, protecting the physical systems behind it may deserve similar attention.

The next supply-chain conversation

The AI boom has already forced the industry to rethink electricity, cooling, networking and semiconductor manufacturing. Physical logistics may be next.

It starts long before the equipment reaches the data center.

If the value of AI infrastructure continues to climb into the billions of dollars, the industry’s definition of “infrastructure security” is likely to expand beyond firewalls and identity management. It starts long before the equipment reaches the data center.

The post The $1.3 million theft that exposed AI’s blind spot appeared first on The New Stack.

Microsoft’s latest AI services announcement suggests the era of standardizing on a single model may be ending. This week, the company launched a $2.5 billion AI adoption business designed to help enterprises customize AI deployments and use multiple models rather than lock themselves into a single provider — part of a larger shift toward systems that route each request to the model best suited to the task.

Simply put, the company that arguably has the deepest single-model partnership in the industry is now selling model swappability as the product.

Betting $2.5 billion on flexibility

Microsoft said Thursday it is creating a new operating entity, Microsoft Frontier Company, to help corporate customers select AI technologies that actually work for their businesses and produce a return on investment, Reuters reported. The unit launches with $2.5 billion in funding from Microsoft and will work with customers including Unilever and Novo Nordisk.

The new firm will help customers choose and integrate AI tools — from Microsoft and external providers — with each customer’s internal data. Customers will own the results of that work rather than handing it back to Microsoft. The move puts Microsoft alongside Palantir, which is doing similar work with large customers using Nvidia’s open-source models, and Amazon Web Services, which recently launched a $1 billion embedded-engineering unit of its own.

What’s most telling is the reasoning. Judson Althoff, CEO of Microsoft Commercial Business, told Reuters the new firm grew partly out of Microsoft’s own experience watching models like DeepSeek and Google’s Gemini catch up to OpenAI. Referring to the original Copilot, he said, “we made a mistake by binding it to OpenAI models only.” Customers, Althoff said, care more about the combination of their data and the models than about any particular model — and they need the ability to swap models quickly as the state of the art shifts.

“We made a mistake by binding it to OpenAI models only.”

One model no longer fits

Consider a typical customer service application that might need to summarize a support ticket, analyze a 300-page contract, generate an email, transcribe a meeting and review source code. Those aren’t necessarily the same problem. A model like Google’s Gemini, with a context window of a million tokens or more, may be the right choice for the contract. A small, fast model like OpenAI’s GPT-5.4 mini or Anthropic’s Claude Haiku may handle ticket summaries at a fraction of the cost. The transcription may go to a purpose-built model like Whisper. And if regulators require customer data to stay on-premises, an open-weight model like Meta’s Llama or Mistral is often the preferred choice.

Instead of choosing a single foundation model, developers increasingly choose several — and the application decides which one handles each request.

AI gateways become core infrastructure

The model is just one component of the stack, so the decision to route the request has to live somewhere.  That’s why developers are forgoing hard-coding an application to a single model and building systems that can choose among several. The routing logic might prioritize cost for one request, speed for another, or keep sensitive workloads on a local model. That way, if one provider experiences an outage, traffic can be routed elsewhere without changing the application itself.

The company that arguably has the deepest single-model partnership in the industry is now selling model swappability as the product.

That changes what developers build

Once companies stop relying on a single model, the challenge shifts to building the systems that decide which model to use for each request.

That means developers need tools to route requests, compare model performance, monitor reliability, control costs, enforce security policies and switch to another model if one goes down, which is a very different engineering problem, especially since deciding which model should respond to a request occurs every time someone uses your application. At enterprise scale, those decisions happen millions of times a day, so they have to be fast, reliable and easy to manage.

The ecosystem is already responding

Open-source proxies like LiteLLM and gateways like Portkey normalize APIs across providers. Orchestration frameworks such as LangChain and LangGraph assume the presence of multiple models from the start. The Model Context Protocol (MCP) is making tool integrations portable across models rather than bound to one vendor. And the cloud providers themselves — Amazon Bedrock, Azure AI Foundry, Google Vertex AI — now expose many models behind a single API.

Orchestration is the new moat

Core models will keep improving. But as performance converges for many business tasks, orchestration becomes the challenge. Microsoft’s announcement is one indication that the largest vendors believe enterprises are heading in that direction and are willing to spend billions to be the ones holding the routing layer.

Instead of treating the model as the platform, enterprises are consistently treating it as a replaceable component behind an orchestration layer.

The cloud era taught developers not to tie applications too tightly to one server; containerization made infrastructure portable. Now the same philosophy is being applied to AI. Instead of treating the model as the platform, enterprises are consistently treating it as a replaceable component behind an orchestration layer.

The post Microsoft just admitted its biggest AI mistake — and spent $2.5 billion fixing it appeared first on The New Stack.

When Subquadratic launched earlier this year, it could build a sparse-attention model that could handle a 12-million token context window and be significantly faster than today’s large language models. But it didn’t launch the model widely and it didn’t publish benchmarks.

Given the company’s large claims, that created quite a bit of skepticism. In June, Subquadratic published its first model card and benchmarks for its small model, SubQ 1.1, supplied third-party verification from data firm Appen, and started talking about its first design partners who now have access to its model.

So far, however, few people have actually used its model. To talk about the company, why its model isn’t widely available yet, and what it has in store for the near future, we met up with Subquadratic co-founder and CTO Alex Whedon.

“We’re not a sparse attention company either.” — Alex Whedon, Subquadratic.

One thing Whedon definitely wanted to clear up is that the company’s current model may be based on sparse attention, but that isn’t its full mission.

“We’re not a sparse attention company either,” Whedon tells The New Stack. “We’ve been working on non-attention architectures for quite a while as well. We think that we will be the first people to leapfrog ourselves in terms of the next model architecture.”

We’ll get back to that.

What the model card shows

It’s the company’s SubQ 1.1 Small model that people are talking about now. This model is built on Subquadratic Sparse Attention (SSA), an attention mechanism the company says scales close to linearly with context length instead of quadratically.

“In the case of Subquadratic Sparse Attention specifically, which is one of a couple model architectures we worked with, the idea is that not all of the token relationships matter,” Whedon explains. “Token relationship compute is why you see this quadratic scaling law.” This means there are almost a million possible two-token relationships in a 1,000-token input in a full attention matrix.

For SubQ 1.1 Small, the strongest results are in long-context retrieval, which makes sense, given that this is where the architecture should have its biggest edge.

On the needle-in-a-haystack test, SubQ 1.1 Small scores near-perfect from 1 million tokens out to 12 million, even though it was trained mostly at 1 million. It hits 99.12 percent on Nvidia’s harder RULER test, which asks the model to trace and aggregate facts across a 128,000-token context rather than just find one.

On general capability, it lands just below the mid-tier frontier models, at 85.4 on GPQA Diamond against 87.5 for Sonnet 4.6. On the LiveCodeBench coding benchmark, it scores 89.7, below Opus 4.8 and GPT-5.5, but slightly better than Sonnet 4.6.

Efficiency is where the model shines, though. The company says that at 1 million tokens, SubQ uses 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2 on a single attention layer. At the full 12-million-token window, it puts the attention compute reduction at close to 1,000x.

“Even in full dense attention, the relative importance of over 99 percent of tokens is very low, attention scores are below 0.1,” Whedon says. “We actually show this in our model card. So clearly we’re just wasting compute most of the time, and in fact we’re maybe making the modeling task harder, because we’re introducing noise.”

“Transformers are a brute-force approach to the problem of text modeling,” he says. “You could say, ‘I’m going to compare every single individual token to every other possible individual token.’ That’s what transformers do. Very brute force, very naive. It just assumes that the first needs to look at the second, the third, the 50th, and the 5,000th. That’s not how humans read text.”

SSA also differs from retrieval-augmented generation, which drops chunks of text before the model sees them. “Every token of the text is being seen by the model,” he says. “It’s just not being redundantly compared to every other token of the text.”

On capability, SubQ 1.1 Small lands roughly in Sonnet 4.6 territory, sometimes a bit above, sometimes below. But its edge, the company says, is size and cost.

“What we posted publicly was fewer than 100 billion parameters,” Whedon says about the size of the model. “I would venture to say that our model is smaller than any of the models offered by OpenAI or Anthropic. But our next model will not be.”

Smaller, cheaper, built for enterprises

Subquadratic is also making the pitch that its model’s capabilities will be especially interesting for enterprises.

“We think that’s a pretty interesting enterprise offering,” he says. “We’ve seen a lot of people in the enterprise space talking about using the mid-tier models as opposed to the frontier for large data-processing tasks, which is exactly where we’re trying to plug in.”

Given that a lot of enterprise problems start with searching through large heaps of data, this makes sense. You can pack a lot of documents into a 12-million token context window, after all. Most of today’s models break down well before the user fills their million-token windows, but with its near-perfect retrieval scores, SubQ may be a good answer for these problems.

As Whedon noted, the model’s first users are design partners, not the public. “We’re giving access to the model to design partners now, and these are mostly enterprises, largely with eight- to nine-figure spend,” Whedon says. “This is a core market that we really care about. It has been since day one.” A limited individual-access release will follow before any general availability.

The launch led with claims instead of benchmarks by choice.

“We were announcing mostly research,” he says. “We could have maybe messaged the launch a little bit differently. There was some debate about how we were going to message it.”

Built on an existing model

One question from May hasn’t gone away, though. The model card states that Subquadratic “started with an existing open-weight frontier model by replacing its dense attention with Subquadratic Sparse Attention (SSA),” and then ran roughly one trillion tokens of long-context continued pretraining on books, documents, and repository-scale code.

That confirms what some of the skeptics suspected at launch, when OpenAI researcher Will Depue wrote that SubQ was “almost surely a sparse attention finetune of Kimi or DeepSeek.” What’s new here then is the SSA mechanism and the long-context training recipe, not a model trained from scratch. The company has not said which open-weight model it started from.

The biggest lever on long-context retrieval was pretraining on very long sequences, Whedon says, something SSA’s efficiency made cheap enough to run as routine.

“Nobody’s talking about multimillion-token pretraining,” he says.

Why hybrids don’t go far enough

There have, of course, been attempts to improve on quadratic scaling, but Whedon thinks most of those attempts only go — almost literally — halfway. Hybrid models such as Nvidia’s Mamba-based Nemotrons, Qwen’s Gated DeltaNet layers, and the various linear-retention designs swap out some of the attention layers, but they don’t go all the way.

“If 80 percent of the layers are not quadratically scaling, then your maximum payoff is like a 5x increase as you scale toward infinity,” he says. “We see a 60x increase at 1 million tokens, almost 1,000x at 12 million. That is the type of payout that you only get if you actually change the scaling law, as opposed to a scalar win.”

He actually credits DeepSeek’s own sparse attention mechanism with making his company’s pitch easier.

“They showed that you could dynamically select relationships without a significant quality trade-off,” Whedon says. “However, they did so by redundantly using a smaller but still full-attention model that ends up using the vast majority of the compute at scale.”

Subquadratic ran its own benchmark against GLM 5.2. “At 1 million tokens, 58 percent of the prefill latency comes from that selection mechanism,” Whedon says. “So that selection mechanism, which is supposed to be seen as cheap, actually dominates the compute, because it’s a quadratically scaling component.”

Beyond sparse attention

It’s also why Whedon pushes back on the “sparse attention company” label. Subquadratic has been working on what he calls “zero attention,” architectures that drop the attention mechanism altogether.

“Attention is kind of similar to RAG in that you have queries, keys, and values that represent information about the tokens that you’re processing,” Whedon says. “There’s this discreteness of representation, where everything is represented within these nice little boxes. That’s super convenient. It’s easy to build a brute-force solution around it. But it also means your ability to compress information is limited. If you had a more continuous, abstract way of representing the information, then you could compress it further, which means you can make smaller models, or you could just scale things up again to create another leap in intelligence.”

He traces the idea to world models and to Yann LeCun’s work. “The stuff we’re doing takes a lot of inspiration from world models, not the video modality in this case, but some of the things LeCun is talking about,” he says. “Rethinking how to represent long-range dependencies, how to keep a long-range state, how to rethink the objective function.” He stops there. “That’s probably all I could say for now.”

Subquadratic has also marketed only one of the three kinds of efficiency it says it is chasing. “We care about compute, sample, and memory efficiency,” Whedon says. “We’ve done a lot of work on all three, but have only really talked about the compute efficiency publicly.”

The near-term plan

The near term plan for Subquadratic, however, is more modest. “Over time, yes,” Whedon says, when asked whether Subquadratic could rival OpenAI and Anthropic on raw quality in the long run. “In the shorter term, we have to be strategic. If we try to boil the ocean on much less capital, it’s not going to go well for us.”

The next model, he says, will likely be a mid-tier size rather than a frontier-class one (think SubQ 1.2 Medium), that he expects to outperform most of the competition in its tier.

How the team will bring the model to market, though, remains to be seen. I wouldn’t be surprised if the team launched its model on one of the hyperscaler’s large model platforms, but Whedon remained tight-lipped about the company’s plans.

The fact that we met with the Miami-based Whedon in San Francisco, though, gives you a bit of a hint of what the team is currently up to.

The post What comes after attention? This startup says it already knows. appeared first on The New Stack.

OpenClaw finally dropped its iOS and Android apps this week, meaning you can now ditch the Telegram and WhatsApp methods to talk directly to your personal AI agent. But what’s arguably more exciting is that the app isn’t actually running the AI on your phone. It’s just hooking up to an agent you’ve already got running somewhere else. Your phone now acts as a window into that agent, complete with voice, notifications, and camera access.

It’s a nice design choice, and exactly where personal AI agents are headed.

Phones become authenticated endpoints

The phone is basically becoming a really smart remote control for OpenClaw. Instead of cramming an increasingly powerful agent onto a phone with battery and memory constraints, developers are treating the phone as one more screen for an agent that lives elsewhere. The agent keeps working whether your phone is in your hand or charging in the other room.

Within this model, the phone approves actions, pings you with notifications, lets you talk to the agent, and shares your camera when the agent needs eyes on something.

Persistent runtimes replace mobile constraints

But OpenClaw isn’t the first to do this. Anthropic’s Claude Cowork with Dispatch follows a remarkably similar pattern. Users assign work from their phones, but execution occurs on a persistent desktop runtime. The mobile app acts as a companion for starting tasks, monitoring progress, and receiving results rather than becoming the agent itself.

OpenAI is moving in a similar direction as well. With Codex, developers increasingly interact with long-running coding agents that continue working independently and can be checked on from multiple clients, instead of treating the phone as the place where the agent runs.

Different companies, different products, but a similar architectural bet to keep the agent running in a persistent runtime and give people lightweight clients to interact with it.

When multiple teams independently converge on the same architectural pattern, it’s often an early signal that the industry has found a model that solves a real engineering problem.

The engineering problems are totally different now

This shift changes what developers spend their time thinking about. Building mobile apps used to mean worrying about battery life, memory limits, offline mode, and squeezing the best performance out of a phone. If the agent is running somewhere else, most of those concerns fade into the background.

Now, a new set of questions comes to mind, such as how a phone securely connects to a long-running agent? How do you manage permissions across multiple devices? What happens if every client disconnects but the agent keeps working?

Agent identity beyond login screens

There’s a downstream effect here. Once the phone is just one of several trusted endpoints talking to your agent, you need a much more robust approach to identity. You’re not logging a user into an app anymore. You’re authenticating devices into an ongoing relationship with a persistent agent.

As that agent gains the ability to read your files, send emails, call APIs, and control external tools, authentication becomes load-bearing infrastructure.

Distributed agents reshape developer tooling

Zooming out a bit and looking at the bigger picture highlights how personal AI agents increasingly resemble distributed systems rather than mobile apps. The intelligence lives in a persistent runtime while the phone is one authenticated endpoint among several.

For developers, the mobile app is only part of the job. They also have to build the components that keep an agent running, connect it to a user’s devices, and ensure those connections remain secure.

The agent keeps running independently, while the phone is simply another place to check in, approve actions, or start a conversation.

Looking at OpenClaw alongside Anthropic and OpenAI, it’s hard not to notice the same pattern. The agent keeps running independently, while the phone is simply another place to check in, approve actions or start a conversation. That architecture solves many practical problems, which may explain why several companies are heading in the same direction.

The post OpenClaw’s new app doesn’t run AI on your phone. That’s the whole point. appeared first on The New Stack.