Christina Stathopoulos, the data and AI evangelist behind Dare to Data, continued her run sorting the week’s most impactful stories into a handful of themes we’ve been watching play out over the past month: more firms investing in the compute AI runs on, more concerns about who controls a model’s borders, and more AI-generated code posing challenges to scaling AI enterprise-wide.
Christina also quickly shared two updates from the frontier labs that we won’t get into below. First, OpenAI finished rolling out GPT-5.6, its family of models tuned for different workloads with an option to dial reasoning up or down, and launched ChatGPT Work, an agent workspace that connects the model to Slack, calendars, documents, and other enterprise tools. Anthropic, meanwhile, published research describing a hidden internal workspace it’s calling the “J-space” that suggests that Claude organizes and manipulates ideas before producing a response. It isn’t proof of anything like consciousness, as Christina was quick to note, but it’s one of the clearer steps yet toward inspecting what a model is actually doing between input and output. That kind of visibility is critical for catching problems like deception or unsafe behavior before they show up in an answer.
More AI labs are turning into chip companies
Last week, Christina covered the opening moves in an AI hardware race, with research from IBM and NVIDIA and a joint OpenAI and Broadcom project. Now there’s news that Chinese company DeepSeek is developing its own inference chips to cut its dependence on NVIDIA and Huawei, and Anthropic is in early talks with Samsung to build a custom AI chip. And as we saw with IBM’s sub-1 nanometer tech, chips are getting denser. Researchers in South Korea have developed a manufacturing technique that stacks more than 10 ultrathin memory chips, packing about four times the density of today’s commercial high-bandwidth memory into the same footprint. The layers align within about six micrometers, roughly a tenth the width of a human hair. The short distances between layers mean the signal doesn’t have to travel as far, making the whole stack run faster and more efficiently.
For AI companies, owning more of the stack is a way to control the cost and performance of running models once they’re built. As chip access becomes a lever in trade and security policy, it’s also a way to circumvent obstructions related to a supplier’s roadmap or a rival’s export policy.
A new security threat underscores the broader geopolitical stakes
JADEPUFFER is the first documented ransomware attack in which an AI agent carried out the entire operation end to end. A human chose the target, then the agent took over, exploiting a known vulnerability, searching for passwords and API keys, moving into the production database, encrypting it, and even writing its own ransom note, all without a person directing each step. Security teams have been bracing for this kind of sophisticated AI-driven attacks. JADEPUFFER is likely the first of many.
That growing threat surface was one reason why AI security took up so much of the conversation at the recent NATO summit in Ankara, where leaders discussed how AI is reshaping cyberattacks, drone warfare, disinformation, supply chain risk, and the speed at which leaders are expected to make high-stakes decisions. Paralleling US restrictions on who can access domestic models, China may also be moving to limit overseas access to its own frontier systems, and Alibaba is banning US-made models for its own employees. We’ve been tracking this story since May, when the US government’s on-again, off-again restrictions on Anthropic’s Fable and Mythos models offered an early sign that frontier model access was becoming of national interest. Christina shared findings from Our World in Data that show just how much the market share of Chinese models has grown from a year ago: Per data from OpenRouter, Chinese model usage at US-based companies, measured in tokens, is approaching parity with US model usage. For technical leaders, that’s a reminder that model choice is now as much a supply chain decision as a technical one, and it’s increasingly one with geopolitical repercussions.
Two challenges to watch for as enterprises scale AI
Now that code is effortlessly simple to generate, the real engineering work is making sure that AI-created code is correct, secure, and safe to run in production. As many in the field are now realizing, that’s easier said than done. A recent study of nearly 200,000 pull requests across more than 800 developers found that AI nearly doubled coding productivity, and reviewers couldn’t keep pace. Each reviewer is now responsible for roughly twice as many pull requests as they were in the years before widespread AI adoption, and the share of pull requests getting human review fell from 89% to 68%, with automated reviews filling the gap. It’s part of the same story Matt Palmer told on the show a few weeks ago when he compared running a team of agents to managing a mid-size team of human developers: “You’re just sending messages all the time, and you’re checking in to make sure things are being done,” he explained. The increase in velocity sets up a real risk of cognitive fatigue and burnout.
Here’s another challenge enterprises are facing as they scale AI: They’re connecting more and more of their data, workflows, content, and business processes to a single AI provider. As we already learned in the data space, the more attached you become to that provider, the harder it is to switch down the line. The solution to this vendor lock-in is to build an AI stack and the workflows around it that keep you in control of your data and ensure you can swap models as the technology evolves. Enterprises that treat model choice as a one-time decision are setting up the same dependency problem that OpenAI’s GPT-5.6 and Anthropic’s chip talks are trying to avoid, just one layer up the stack.
What’s next
Christina will return next week with another sweep of AI news, including a first look at Apple’s lawsuit against OpenAI, New York’s pause on new hyperscale data centers, and a landmark ruling in Germany holding Google accountable for misinformation generated by AI Overviews, plus updates on DeepSeek’s IPO plans, OpenAI’s first AI hardware device, and Anthropic’s new enterprise deployment unit. Join her live on the O’Reilly learning platform or catch up after the fact on YouTube, Spotify, Apple, or wherever you get your podcasts.
And if you want to keep learning between episodes, check out our new weekly show Zero to Agent in 30 Minutes, our AI Codecon live event on August 31, and The Agentic Enterprise now in early release on O’Reilly. Christina’s also hosting the AI Superstream on AI harnesses next week on July 23. Hope to see you there for this four-hour deep dive on turning models into agents and running them securely at scale.