Mark Zuckerberg gets it right: “The defining question of our age isn’t whether superintelligence will exist, but who will have access to it. Will it be centralized and restricted to a few institutions, or will it be a tool that empowers everyone?” Concentration of power is the biggest risk of AI. When a small number of labs (working hand-in-glove with the administrative state) decide who has access to which model capabilities, they inevitably shape what can be said, known, and built. That’s not “safety.” It’s control. As Mark points out, the history of open source shows that broad access and transparency are usually the best path to actual security and resilience. Decentralization creates checks and balances on power. By contrast, centralized alternatives, like bureaucratic approval regimes and mandatory gatekeeping, typically produce regulatory capture and reinforce cartels. Personal superintelligence in everyone’s hands, with competing models and real data sovereignty, is a far better check on a dystopian future than self-appointed guardians who claim to be “aligned” with all of humanity.

The entire tech industry (save for Anthropic) has come out in favor of open source AI. So what happens next? Will Anthropic change its lobbying efforts? Not likely. Now the gaslighting begins: “Nobody is trying to ban open source.” “We just want to limit who can use it.” “We just want to limit who can contribute to it.” “We just want to limit how powerful those models can be.” “We just want to make sure the guardrails (we lobbied for) can’t be removed.” The net effect will be the same. They won’t stop until they kneecap open source. The rest of the industry needs to watch these guys like a hawk.

Nobody is saying that all software has to be open source. What they’re saying is that open-weight AI should be allowed. By implication, they’re rejecting your company’s incessant machinations to kneecap the open model ecosystem. Read the room.

It’s true that Anthropic is the fastest growing company that Silicon Valley has ever seen. Which is all the more reason their incessant attempts at regulatory capture are not just unnecessary but frankly gross.

Cernovich is right that AI + Big Tech could easily turn into Big Brother. But what’s the alternative to living off the grid or in the Panopticon? The third option is to run your own AI on your own hardware, retaining data sovereignty. That’s why open source matters. It’s software freedom.

Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused because of “cyber guardrails.” There’s no reason to limit American models on tasks that Chinese models handle without issue. We’re only making ourselves less competitive.

This is *exactly* what I predicted would happen. I said Chinese models would have advanced cyber capabilities within a matter of months and the only thing to do about it was to use AI-powered cyberdefense to protect our systems. Trying to gatekeep models doesn’t work.

This is concerning. For the first time, a Chinese model Kimi K3 has taken #1# on the Frontend Code Arena and is scoring at or near the frontier on other benchmarks. Meanwhile America is tying itself in knots: politicians and bureaucrats are banning new data centers, piling on state regulations, and pushing for new federal agencies to pre-approve frontier models. This is how you lose the AI race. The rest of the world won’t play by our rules if we bog ourselves down. Permissionless innovation is how America won the internet and became the technological envy of the world. We can do it again with AI -- while addressing risks in a targeted way -- or we’ll watch our lead evaporate.

Legacy Media types are calling this Alex Karp interview a “crash-out” so that’s your first clue that he is actually saying something extremely insightful. He is articulating what real “AI safety” looks like in the enterprise. Not abstract alignment research or certification by a government-run DMV for AI. Real AI safety for businesses is the ability to control their own data, model weights, and compute — so a frontier lab can’t hoover up their proprietary knowledge and turn it into their next product. As Karp explains, technical customers want “control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it’s not being transferred to someone else.” Don’t think that can happen? Just look at Figma. According to The Information, Anthropic “blindsided” its then-business partner with the launch of Claude Design. Figma’s founder said Anthropic had not been “consistently honest” with them. Anthropic’s chief product officer had even served on Figma’s board until three days before the launch of Claude Design. Figma’s stock has fallen sharply this year while Anthropic’s valuation has surged. This isn’t an isolated example. Anthropic has launched Claude Science, Claude Security, Claude Legal, and of course Claude Code — each expanding into categories previously served by companies building on top of their models. The pattern is consistent: watch where value is being created, then move in directly. Dominate the model layer, then use that position to capture the most lucrative verticals. Dario has argued that open source models powerful enough to compete with Anthropic are “dangerous.” But dangerous to whom? Not to enterprises that want to retain control over their data and workflows. Dangerous to a business model that benefits from customers having few real alternatives at the model layer. As Karp exposes, true enterprise safety isn’t trusting that a lab’s future roadmap won’t include your business. It’s retaining the ability to choose — at the model layer — who gets to see and use your alpha.

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Woke up in London to all the conversation of Chinese Cybersecurity models getting to Mythos like ability with agent swarms. Swarms that can explore vulnerabilites, determine attack paths and potential fixes in addition to persistent red teaming. Happened just under 3 months, faster than my optimistic estimate. Expect that in a few weeks there will be more widespread capability. Highly likely that we will get US models released from bans faster with a promise of better hygiene. What does it mean for the rest. 1. Test your own code! 2. Validate your vendors, ensure they are doing the same. 3. Start evaluating direct and virtual patching approaches to ensure open source is protected. From a longer term perspective, we will need to ensure better security posture, no, no misconfigurations, robust platform products which can react swiftly, and a culture of constantly testing the enterprise with the most recent tools out there.

More data is showing the opposite of what many people expected with AI adoption and jobs. Ramp found that the more AI adoption a company has the more their headcount grows. At Box, we recently did a survey of 1,600+ mid and large sized companies, and the findings were similar. 58% of respondents expected headcount to rise over the next three years. Interestingly, that figure climbs to 79% among the most mature adopters of AI. The more advanced AI adopters expected to grow their headcount at a greater rate in the future than others. Of course it's true that the companies that can afford to adopt AI the most are also the ones that likely are seeing growth in their business, leading to more headcount. So the point of the story isn't necessarily that by adopting AI you will inherently grow. *But* the most important takeaway is that the opposite is not proving out. The fears a couple years ago would have been that the companies adopting AI the most would be hiring fewer people. But in reality this is what actually you should expect to happen. If a company can get more customers because they use AI in sales for account or market intelligence, they hire more sales people not fewer. If you can build way more software than before, you end up hiring more engineers because the projects get bigger and you take on more. And so on.

We can finally say AI isn't killing jobs. A new paper from me, @tryramp, and @RevelioLabs uses firm-level spend and workforce data across 21K U.S. businesses to measure AI's impact on jobs. Firms that adopt AI heavily grow headcount 10% over two years following adoption. Low adopters see no statistically significant change.

Narrative violation: A new study of 21,559 firms in the U.S. finds that “companies that adopt AI tend to grow faster following adoption”. “Firms making the largest AI investments grow employment by roughly 10% following adoption, while low-intensity adopters see no statistically significant change.” “Entry-level headcount rises 12% for high-intensity adopters.” “Gains emerge gradually and are broad across roles, including engineering, sales, administration, and customer service.” “The results counter predictions that AI adoption will lead to broad job loss.” The study is based on observed AI spending from Ramp card and bill pay data linked to Revelio Labs workforce records.

A year ago, President Trump declared that America was in a global AI race and that the way to win it was to be pro-innovation, pro-infrastructure, pro-energy, and pro-export. President Trump was exactly right; we deviate from that strategy at our peril.

Many smart people/AI insiders are saying GLM-5.2 is the first Chinese AI model to match and often beat the American big lab public AI models with no compromises. Incredible timing given current events.

This is what’s causing Anthropic to aggressively beg for govt protection (see below). Customers are finding cheaper alternatives. Keeping employees requires continuing ultra-rich secondaries ($$$) that are dependent on revenue growth. When you can’t win on the field go to DC.