Why Companies Want AI They Can Own

A yet-to-be-released open-weight AI model is getting a lot of buzz. This could be the model, people say, that brings the open-source AI crown back to US shores. What's interesting to me, though, is less the model itself and more the evolving discourse around open-weight models in the US. Increasingly, this is not just one conversation, but 3: an AI safety conversation, a national security conversation, and an enterprise strategy conversation. Now, if those conversations point in potentially different directions, which will win out? How will they be reconciled? Today we explore where Openweight AI is in the US right now and where it might head next. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Section, Robots and Pencils, and Blitzy. To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts. And if you want to learn more about sponsoring the show, send us a note at sponsors@aiddailybrief.ai. Slowly but surely, the hyperscalers are getting the message that they cannot treat community attitudes around data centers as a secondary priority. The latest example comes from Amazon, who on Friday committed to spending more than $1 billion over the next 5 years on community projects surrounding their data centers. In addition, the company has said that they've stopped using nondisclosure agreements to keep their deals with local officials under wraps. The commitments came as part of a 3,000-word essay from AWS CEO Matt Garman, who, in addition to making those commitments, implored the public to think about data centers as critical infrastructure for modern life. He called the data center buildout the race our nation can't afford to lose and compared it to the construction of the interstate highway system. Garman wrote, with any change, there will be important questions raised, but there will also be misinformation and outright lies. And in the age of social media and 24/7 news, myths take hold faster than ever before. In fact, this buildout is so important geopolitically that there are widespread reports of various countries intentionally seeding misinformation in the U.S. about data centers to trick us into slowing down. Noting 100 data center moratoriums being considered across the country, Garman continued, if these measures are enacted, the U.S. could be writing its own losing ticket to this race, and the consequences would last generations. As a country, we can't afford to find ourselves in that position. The new community pledge included all of the commitments that are quickly becoming standard: preventing increases to local energy rates, creating local jobs, and ensuring communities have more opportunities to discuss data center projects. The approach to the announcement saw some pushback in the press. The Verge barely touched on the new community pledges, focusing instead on what it called the scary blog warning communities not to block data centers. And part of the issue was that Amazon buried their new pledge beneath a long section debunking the myths around data centers. Tom's Hardware wrote, the first part of Garmin's post actually addressed many of the concerns raised by community members near these developments, calling them false and misleading, despite various reports that have raised concerns about utility hikes and noise pollution. From my perspective, while I am very glad to see these sort of commitments starting to become normalized, I tend to agree that this post was fairly tone deaf and ill-advised to the extent that it was trying to actually appeal to the communities where these data centers are going to happen. It's pretty difficult to imagine anyone will change their opinion because a data center developer said that their genuinely held concerns were false. And whether that's right or wrong, these companies have to realize that they have very different constituencies that they're dealing with. The individual in a community has vastly less consideration for the race our nation can't afford to lose. What they care about is their own lives and the lives of people near them, period. Wrapping what is an otherwise positive pledge in a blanket of PR and myth-busting just undermines the pledge, as witnessed by the article that The Verge was able to write. For now, the moves that the data center builders are making are positive. The pitch still needs a lot of work. And speaking of the pitch needing some work, Sam Altman has handed a huge new bag of ammunition to everyone who dislikes AI. In an interview with Politico, Altman said that there was a, quote, lot of daylight between OpenAI and Anthropic's views on AI safety. Asked for specifics, Altman responded, we believe that the world should accept some bad things happening for the benefits of this technology and people having the agency. Now, if you are sitting there thinking to yourself, well, that has to be just part of a larger assessment, And a much more nuanced argument that Altman is trying to make, you would of course be right. It's pretty clear he didn't come into this interview with that line prepared, wanting to drop it for some big emphasis. But at some point, the folks who are scheduling themselves on all of these interviews are going to realize that whether they like it or not, we live in a soundbite culture. And if you say, we believe the world should accept some bad things happening for the benefits of this technology, that's gonna be the only thing that anyone publishes for the next couple of days. Now, as to what Altman was actually trying to get into, the discussion was around current safety proposals and where OpenAI agreed with Anthropic and where they didn't. For example, in terms of agreement, Altman said that both support things like third-party safety auditors. However, one of the big distinctions Altman tried to present was around the risk of regulatory capture. Altman characterized the Anthropic view as, this technology is going to get so powerful and it's so dangerous that a single lab in San Francisco should have it and make sure nothing bad happens. and kind of figure out how to dole out its benefits. Altman, for his part, though, called this a completely unacceptable trade-off. He continued, I wouldn't take a trade of saying, we'll make sure there's no major hacks, there's no misuse of this technology, there's zero scams, there's zero all the other bad things that will happen, because I think people will do tremendously orders of magnitude more good stuff than bad stuff. In other words, he is getting at the AI version of the classic conundrum of safety versus freedom and is making the case which he is accurate has not been made enough in the popular press, that tightly controlling the use of AI or limiting it to some very specific group of people comes with its own set of risks that are certainly no less real than anything being discussed in the AI safety discourse. Overall, it definitely seems like OpenAI is trying to carve out increasingly different territory when it comes to AI safety. But unfortunately, at least for this round in the discourse, no one was seeing beyond the line that we should accept some bad things. To the extent that bad things do happen, however, Treasury Secretary Scott Bessent has reinforced that when they do, the frontier labs who enabled them are responsible for their own actions. Last week's AI summit at the White House ended in an accord between the frontier labs, which normalized and reaffirmed the concept of third-party auditors and board-level oversight. In an interview with Axios on Saturday, Bessent said, we want safe acceleration. It's the people at the labs who have to accept responsibility, and I agree with that. I think the labs have come around to that way of thinking too. Bessent weighed in on a range of other hot-button issues in AI, representing some updated thinking for the Treasury Secretary. On AI risk generally, Bessent scolded the communication from some quarters, commenting, I believe that we have to be prepared for every occasion, but on the other side, this alarmism without solutions by some of the AI community, that's not leadership. Remember that during the early stages of the Mythos rollout, Bessent seemed fully convinced the model was dangerous and unsuitable for general release, suggesting at least some slight change in perspective. Bessant also dismissed the notion of an AI bubble, pointing out that industry leaders like Microsoft are seeing solid returns from their infrastructure spend. Overall, Bessant appears to still have concerns but is expressing increasingly nuanced views of AI safety that go beyond some simplistic slowdown. Now, speaking of last week's White House AI events, after President Trump declared that the term will no longer be artificial intelligence, Elon Musk showed his support for the president's name change by posting, no more AI, SI, it's better. After an X user called on him to rename SpaceX AI to SpaceX SI, Musk responded, yes, we will make that change. At the moment, however, friends, I do not have any intentions of renaming the AI Daily Brief to the SI Daily Brief. Lastly today, a much more fun one. Meta has open-sourced their code to let users build their own Muse gadgets. When Mark Zuckerberg unveiled the Muse charm device at Meta Connect, it seemed like something the hardware division had cooked up from off-the-shelf components. Zuckerberg showed off a working prototype, but said that they're still nailing down the final build sheet ahead of a December release. Immediately, people started hacking together their own version from cheap generic hardware. And Meta's response on this one was, go right ahead. Nat Friedman announced an open-source build of Muse firmware for ESP32 devices and the Linux SDK. Hardware hackers can now easily build a fully customizable Muse gadget on top of a Raspberry Pi or ESP32 dev board. Meta has also hacked together their own new gadget with the Muse Home Link. The device is a small USB-powered Wi-Fi link that lets Muse tap into your smart home setup. Meta is making a batch of 5,000 of these devices, and we'll be giving them away to Muse subscribers once they're ready to ship. The result was a huge wave of people porting Muse onto a ton of random devices. Robert Soriano dusted off his old PSP and turned it into a Muse device, complete with microphone support. Wes Bos got it running on a conference badge, porting the firmware across to MicroPython. Riley Brown installed Muse on his mod-retro Game Boy by simply asking Codex to port it over, While Meta engineer Jessick Min went the easier route, loading Muse onto a $50 ESP32 device. He's currently using it as a scanner to inventory his wine collection, but notes that the possibilities are endless. Basically any device with a Wi-Fi connection can now act as a Muse gadget. Now, not to overstate this, but given how many false starts we've seen when it comes to AI devices, maybe this approach of just letting people build things is a better strategy for figuring out what the form factor for agentic gadgets should be. Historically, launching a dedicated device like a smart speaker has been a risky play. The devices are low margin and prone to failing in the market if they don't function perfectly. Meta is instead going straight to the tech community and giving them open-source firmware to use on whatever hardware they happen to have lying around. And while this set of devices might only be for those hobbyists who have enough technical capability to do it themselves, it wouldn't at all surprise me if the quote-unquote right form factor for an agentic device actually comes out of one of these experiments. instead of just brainstorming inside one of the labs. If you want to see more examples, go to an X search for Muse Gadgets. For now, however, that's going to do it for the headlines. Next up, the main episode. A new study from KPMG and the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than 500 early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs. These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at kpmg.com/us/aiamplifiers. Here's a harsh truth. Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized. Half of companies have AI tools, but only 12% use them for business value. Most employees are still using AI to summarize meeting notes. If you're the one responsible for AI adoption at your company, you need Section. Section is a platform that helps you manage AI transformation across your entire organization. It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value. The result? You go from rolling out tools to driving measurable AI value. Your employees move from meeting summaries to solving actual business problems. And you can prove the ROI. Stop guessing if your AI investment is working. Check out Section at sectionai.com. That's S-E-C-T-I-O-N-A-I dot com. The best teams don't have a single star carrying everyone else. They know their own strengths and each other's weaknesses and play to both. That's the team Robots and Pencils has built on purpose. Nobody there is grinding through busywork to pad a headcount number. People come for the hard problems, and they stay because everyone around them is leveling up at the same time. In a market full of companies that are just trying to hire fast, that's worth a look. Check out robotsandpencils.com/careers. Every AI coding tool on the market does the same thing first: it starts writing code. Blitzy does the opposite. Before writing a single line, Blitzy spends days reverse engineering your entire codebase. Thousands of agents ingest millions of lines, mapping every dependency, every undocumented constraint, every architectural decision made over the last decade. The result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years in the building. Other tools guess at context with grep searches and markdown files. Blitzy never guesses. It builds true understanding first, then delivers over 80% of entire software epics autonomously. Validated, end-to-end tested, production-grade pull requests. That's why Fortune 500 engineering teams trust Blitzy with the codebases that matter most. See for yourself at blitzy.com. That's blitzy.com. Welcome back to the AI Daily Brief. Today we are talking about open weight AI in the United States. This is a conversation that's evolved quite a bit in the last year. Its first big moment in some ways was the January 2025 DeepSeek moment, where Chinese lab DeepSeek dropped a reasoning model in a free app. Which ended up being most people's first experience with a reasoning model, which was very different than their experience using the existing crop of models that were available at that time. That moment caught the market's attention to the tune of $580 billion ripped off of NVIDIA's market cap in a single day, with the concern, of course, being that if cheaper-to-produce Chinese models were going to become what everyone shifted to, then maybe these infrastructure costs, which were propping up the entire economy, didn't really make as much sense. Over time, that narrative shifted, although it never fully went away. And in 2026, the discussion around open weight models has changed yet again. This year, the discourse around open models is much more about how they potentially solve key issues, not just for startups and developers who are willing to play in uncharted waters, but for enterprises who are worried about costs and data sovereignty. And one of the big questions for the last few months has been whether this was a section of the market that the US was going to compete in, or was it just going to cede it all to China? and try to stay ahead on the closed-source frontier models and the state-of-the-art. A new report from Axios is getting a lot of buzz— a disproportionate amount, in fact— that I think shows the interest in this particular area. On Sunday, Axios dropped a piece called Scoop: Powerful Open Model Is Set to Shake Up AI Race. The rumored model is coming from Reflection AI, who themselves have been a bit of an enigma since their founding in early 2024. The company raised several billion dollars and reached a $25 billion valuation without a product. They won a Pentagon contract in May and signed a $6.3 billion compute deal with SpaceX. Reflection AI did position themselves as a US-based counterweight to the growing popularity of Chinese open weight models, but without a currently available model, many assumed this was just a fundraising pitch. On Sunday, however, Axios reported that Reflection AI is preparing to release their first model later this month, with rumors that it's pretty good. Sources said that the model will be competitive with Chinese rivals and will quote. boast powerful intelligence capable enough to help companies build their own proprietary low-cost AI systems. And this is of course where it piques my interest and should be of interest to those of you who are thinking about enterprise AI deployments. According to the report, Reflection's goal is to build an AI factory, a system designed to help enterprises create their own AI systems based on Reflection's models. Reflection is already piloting this approach, recently announcing a sovereign AI factory partnership with Shinsuke Group in South Korea, And sources said that Reflection held briefings in Washington last week to discuss the new models and this concept of AI factories. They've certainly been securing a ton of compute in anticipation of this launch. In addition to the multi-billion-dollar SpaceX deal, Reflection has another billion dollars in contracts with Nebius. Now, all of that's great, all of that's very cool. It'll be interesting to see what Reflection puts out. But what matters much more than the individual model and their approach to factories is the trend that it represents. In fact, sources told Axios that several other U.S. labs are preparing to launch open models this month. We've heard rumors that NVIDIA is training NemoTron 4, and we could be due for a new release from Thinking Machines Lab. Summing it up, Andrew Curran wrote, American OSS renaissance about to begin? Howard Lutnick has been hoping for something like this for some time, and at one point was reportedly even considering direct government funding to get the ball rolling. The U.S. government wants American open models to compete with Chinese models globally. The discussion that Andrew Curran is referring to is the one that happened over the summer when it seemed for a moment like the U.S. government could be trying to include open-weight models in their testing regime. Many thought that that move, if it actually happened, would effectively snuff out that segment of the industry. The calls, of course, came shortly after the release of Kimi K3, which the media was very quick to jump on as open-source mythos. According to Wired's coverage of that White House debate at the time, Commerce Secretary Howard Lutnick was a key defender of competition over bans. Back in July, they wrote, Lutnick has contemplated ways to create incentives for top U.S. labs to create their own open-weight models to counterbalance China and has spoken with leaders at a number of AI labs in recent weeks. Lutnick appears to be straddling a middle ground of regulation. He imposed export controls on Anthropic to bring them to heel, but has been more freewheeling than others. One of the long-term geostrategic questions and divides between different people who have different takes on this has been whether the right approach to U.S. leadership is to try to cut off China's access to the inputs by which they can make advanced models, or to try to own the entire stack from closed models to open models so that the world runs end-to-end on U.S. AI infrastructure, both from a hardware and software perspective. At no point has there ever really been any strategic coherence around that, but that is the constant back and forth that's happening in the halls of power as the conversation evolves. One company though that has quietly, or not so quietly depending on how well you're paying attention, been a champion of U.S. open source is NVIDIA. The company has now trained multiple generations of LLMs, with Neutron 3.5 seeing significant use and Neutron 4 promising to be relatively close to the frontier. Alongside LLMs, NVIDIA has also trained models in verticals like robotics and self-driving cars. Perhaps unsurprisingly, NVIDIA seems to hold the view that their GPUs are the core product, That core product stands to benefit dramatically from more use of freely available AI models. Their outside investments also speak to a strong commitment to open models. NVIDIA is a key backer of Reflection AI, as well as several other open-source labs. And of course, more recently, they made that huge investment into Hugging Face, spending $12.9 billion to acquire the platform last month. At NVIDIA's developer conference in July, when open-weight model regulation was firmly on the table, Nvidia CEO Jensen Huang made an impassioned plea to keep the models available. He said, Researchers need open source. Developers need open source. Companies around the world need open source. Open source models are really, really important. We lead in open source contribution. We have 23 models on leaderboards. We have all these different domains from language models to physical AI models to biology models. Each one of these models has enormous teams. We are dedicated to this, and the reason for that is that scientists need it. Researchers need it, startups need it, and companies need it. The question is, are the Chinese models good enough? Certainly a ton of startups and smaller companies have already shifted their workloads over to open models. Signal on X writes, tons of people do not realize how much new stuff is being built on top of Chinese open-weight models. You don't even have to disclose it because it's running on US-based infrastructure. When product designer Sam Solomon followed up and said, I keep hearing this, but honestly it doesn't actually seem cheaper than using Luna, Signal pointed out, you cannot fine-tune Luna. And increasingly, that fine-tuning is the story. This year, the conversation around open models has shifted from being about either A, the market implications of big infrastructure investments, or B, the general geopolitical competition with China, to instead also including this strong dimension of discussion around how open models potentially solve enterprise AI problems. Investor Joe McCann wrote, This is precisely why I invested in Reflection AI. Open source is how enterprises and the government will ultimately trust AI. The discourse about enterprise trust of AI is getting louder. Back in July, in a conversation with CNBC, Palantir CEO Alex Karp discussed what customers actually want, what he considers the real business of Frontier Labs, and consequently the importance of open models. Said Karp, what the technical customers want is control over their compute, Their models, their data stack, and their alpha. They want to know that they own the means of production, and it's not being transferred to someone else. Who owns the data? Are the prompts secure? Is this being transferred to you? If it was so valuable and I can make you a billion dollars, wouldn't I say, I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable? Now, Karp is a bit of a renegade, but he's not the only one making this argument. Increasingly, this is the pitch that Microsoft is bringing to the market as well. Microsoft CEO Satya Nadella wrote a blog post a couple of months ago called The Reverse Information Paradox. In it, he said, in the AI age, the buyer risks giving away knowledge just in order to use what they bought. You essentially pay for intelligence twice— once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it. Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return. In the same post, Satya later argued, enterprises need a real trust boundary for their human capital and token capital to compound. It's where an organization's data, traces, evals, adapted weights, and memory accumulate and improve together. And it is a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent. Enterprises will demand the rights to use model outputs to fine-tune and/or train their own models. I think of this as every firm's right to align models to their enterprise accountability obligations. Now, what's interesting about this is that Microsoft is, of course, not selling open models. What they are selling, however, is their own models as a base to do this sort of training with an existing trusted partner. Wrote Microsoft AI CEO Mustafa Suleyman, all of this is the foundation for Microsoft Frontier Tuning. It lets you customize our models to create custom company-specific agents that only you control. You can make our model your model, your data, your agents, your moat. And while the pitch was largely around sovereignty, there were also clearly benefits on this growing efficiency conversation as well. For example, Suleyman said, when we tuned our models for McKinsey's tasks, MAI, which is Microsoft's models, delivered the highest win rate, outperforming GPT-5.5, at the time the state of the art, on quality while being 10x lower on cost. Now, this trend has been coming for a while, and it's getting more and more serious. And everywhere you look, there are more indications that behavior is shifting this way as well. A couple of weeks ago, for example, Vercel CEO Guillermo Roche wrote, looks like today may be a record day for token volume percentage of open models on Vercel AI Gateway. Open models used 78.4% of tokens while closed used 21.6%. And certainly in the discussions that we're having both at AIDB and at Superintelligent, a conversation about open models which might have not even made the agenda last year at this time is now something that many enterprises are taking much more seriously. One big question of any potential U.S. open-weight AI renaissance, however, is what the regulatory environment for this type of AI might end up being. And on that front, the Trump administration has named their new AI czar and announced a new AI task force alongside him. The Wall Street Journal reports that current Director of National Intelligence Jay Clayton will be empowered as the new AI czar. Clayton has been a senior official across both Trump administrations. He served as SEC chair during Trump 1, and during the current administration, he began as a U.S. attorney in the Southern District of New York before being appointed Director of National Intelligence. Alongside Clayton, the administration will now have a cross-department AI task force as well, with that group including Emile Michael, Undersecretary of War for Research and Engineering, Scott Cooper, the Director of the Office of Personnel Management, and Andrew Ferguson, the Chairman of the Federal Trade Commission. The FTC seems to be becoming an increasingly important regulator for the AI industry, although the task force also includes several officials responsible for AI implementation across the government. Alongside this core group, the Wall Street Journal reported that Bush-era National Security Advisor Condoleezza Rice, Vice President J.D. Vance, Treasury Secretary Scott Bessent, and former AI czar David Sachs will also be included on the task force. Confirming the task force in a Truth Social post, the president wrote, the Superintelligence Force is tasked with coordinating the effort of the federal government to ensure that America continues to lead the world in superintelligence. This superintelligence force will coordinate the federal government's engagement with consumers, public interest groups, religious organizations, critical infrastructure providers, and superintelligence companies. Now, what's super clear so far is that Clayton views his role around artificial and/or superintelligence as first and foremost a national security role. Clayton said, the risk of not being first is high. Not being first increases the identified and unidentified risks, particularly from our adversaries. Being first will better enable us to address those risks on behalf of the American people. Even before this appointment, Clayton argued in an interview with CNBC against U.S. companies pausing development of AI models for these same national security sorts of reasons. Steve Bannon doesn't like it, accusing Clayton basically of being much too close to China. On his podcast, Bannon said, Clayton's a great lawyer, but I don't know how an Alibaba general counsel who got them through an IPO, a military info network for the CCP, ends up Director of National Intelligence. On the other end of the critique spectrum, Tornado Cash developer Roman Storm is pretty negative on what this likely means for open source in his estimation based on his personal experience with Clayton. He wrote, it doesn't look like we're headed towards a future that supports open source AI. Jay Clayton headed the U.S. Attorney's Office for the Southern District of New York, which prosecuted me. My instinct is that the 2 very different conversations we're having around OpenWeights models— or maybe honestly even 3— Open models in terms of their implications for the enterprise, open models in terms of their implications for AI safety, and open models for their implications in terms of national security are inevitably going to get a bit closer in one form or another, given the new regulatory phase we're getting into. Still, call me an optimist, but I think there are reasons to be excited about the future of American open models, if for no other reason than we tend to pretty aggressively follow market opportunity signals. And in a world where enterprises are looking to deal with costs and issues of data privacy and data sovereignty, Open models get a lot more interesting. Certainly a conversation that we will continue to watch. For now, though, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. Until next time, peace.