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.

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.

AI has changed the web right before our eyes. With Google’s AI Overviews doing the heavy lifting, publications that once owned the first page of search results are being replaced by summaries. Readers get their answer without ever clicking through. Much of the traffic has simply stopped.

Cloudflare on Wednesday announced a slew of updates for publishers who are facing this new reality. From new crawler classifications and analytics dashboards to Answer Engine Optimization tools and an expansion of its Pay Per Crawl program, it’s clear the company is trying to become the economic pipes of the AI web.

The shift from ‘keep out’ to ‘let’s make a deal’

A year ago, Cloudflare’s pitch was practically defensive, asserting that website owners should be able to block AI crawlers. And while that still holds true, the company has pivoted to discussing building “rails” for an “agentic economy.” And it makes sense. If AI agents are already browsing the web, collecting content, and in some cases buying things, someone needs to handle the business end of how the sites they visit are compensated. Cloudflare thinks that someone should be Cloudflare.

Paying for value, not visits

Roughly a year ago, Cloudflare launched Pay Per Crawl, which let publishers set a price for AI companies to pay when they fetched a page. Now the company is pushing toward Pay Per Use, which means publishers get paid when their content actually appears in an AI-generated answer.

To backtrack, under the old model, an AI crawler pays to visit your site, whether or not it does anything useful with what it finds. Cloudflare says it’s already testing this with Ceramic.ai and You.com, each running slightly different versions of the concept.

Instead of charging for access — which is basically a toll booth — publishers are charging for value.

The economics here flip. Instead of charging for access — which is basically a toll booth — publishers are charging for value. That’s closer to how affiliate marketing or licensing deals work, and it’s a much harder problem to solve. It requires knowing which content contributed to which answer, which means an attribution infrastructure that doesn’t really exist at scale yet.

Crawlers need clearer labels

But here’s where things get more technical — and a bit political.

Cloudflare wants AI companies to stop lumping all their crawlers together. Right now, a single bot from a major AI company might be fetching pages for search indexing, model training, and agent tasks all at once. That makes it impossible for site owners to say yes to one use and no to another.

Starting September 15, Cloudflare plans to change the defaults for new and free-tier sites. AI search crawling stays on, but training and agent access get blocked on ad-supported pages unless the site owner opts in. Mixed-use crawlers that refuse to separate their traffic get blocked entirely.

The company is clearly taking a shot at Google here, noting that Google’s bundled approach gives it access to roughly twice as much content as AI-native competitors. Separating crawlers’ intent has become a prerequisite for any kind of functioning market between publishers and AI companies. Because if site owners can’t distinguish between “index my page for search” and “train your model on my writing,” they’ll increasingly just block everything.

Separating crawlers’ intent has become a prerequisite for any kind of functioning market between publishers and AI companies.

Optimizing for AI answers

Cloudflare is also rolling out a dashboard designed for business teams, called Attribution Business Insights. Think of it as the AI equivalent of knowing your Google Search Console numbers, except the “search engine” is now ChatGPT or Perplexity or whatever agent your reader happened to ask.

On top of that, Cloudflare is introducing Answer Engine Optimization (AEO). The idea is that ranking in Google is no longer enough; for publishers to succeed, they also need to understand how and where their content gets cited in AI-generated responses. That’s a different optimization problem than SEO, and right now almost nobody has good tooling for it.

Infrastructure as competitive advantage

Cloudflare already sits between websites and the internet. It sees the traffic and knows what changed on a page and what didn’t. It can tell a crawler to come back later because nothing’s new, which, by the way, it says would eliminate over 50% of current AI crawl traffic.

That puts Cloudflare in a unique position. It’s already part of the path between AI companies and the web. Now it wants to become the layer that manages access, attribution, and eventually payments between them. If AI agents become the primary way people find information online, Cloudflare is betting the next battle won’t be over who builds the smartest model but instead over who builds the infrastructure everyone else relies on.

If AI agents become the primary way people find information online, Cloudflare is betting the next battle won’t be over who builds the smartest model — it’ll be over who builds the infrastructure everyone else relies on.

The post Cloudflare wants to build the economic layer of the AI web appeared first on The New Stack.

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with...

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with day-to-day tasks such as coding, video editing, and content management. NVIDIA and Microsoft are teaming up to enable the next generation of developers to build on-device agents on the Windows platform, with easier setup, native security…

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From Rosie the Robot to Real Conversations

Remember Rosie the Robot from The Jetsons? She wasn’t just a housekeeper. She was a glimpse into a future where technology could hold a conversation, help with everyday tasks, and feel almost part of the family.

Rosie could handle her routines with ease, but the real promise was what she represented: a world where machines could engage in authentic dialogue and adapt both the conversation and the tasks they perform to meet the moment.

In many ways, customer service AI has been chasing that vision. But most solutions are still stuck in the early stages.