# Why Companies Want AI They Can Own — Transcript (2026-10-05)

https://aidailybrief.ai/e/2026-10-05 · Listen: https://pod.link/1680633614

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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 three: 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 [00:01:00] patreon.com/aidailybrief, or you can subscribe on Apple Podcasts.

And if you wanna learn more about sponsoring the show, send us a note at sponsors@aidailybrief.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 a billion dollars over the next five years on community projects surrounding their data centers

In addition, the company has said that they've stopped using non-disclosure 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 build-out the race our nation can't afford to lose and compared it to the construction of the Interstate Highway System

Garmin 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 twenty-four seven news, myths [00:02:00] take hold faster than ever before. In fact, this build-out is so important geopolitically that there are widespread reports of various countries intentionally seeding misinformation in the US 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 US 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, [00:03:00] 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

agree, 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 work And And speaking of the pitch needing some work

Sam Altman has handed a [00:04:00] 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 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

pretty clear he didn't come into this interview with that line prepared wanting todrop 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

as to,

now as to what Altman was actually trying to get into

the discussion was around current safety proposals 

and where OpenAI agreed with Anthropic [00:05:00] 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, "Well, 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

at... 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 [00:06:00] 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 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 Besant 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."

Besant weighed in on a range of other hot button issues in AI, representing some updated thinking for the Treasury Secretary on AI risk generally, Besant 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, Besson seemed fully [00:07:00] convinced the model was dangerous and unsuitable for general release, suggesting at least some slight change in perspective Bessen also dismissed the notion of an AI bubble, pointing out that industry leaders like Microsoft are seeing solid returns from their infrastructure spend

overall, Besson appears to still have concerns, but is expressing increasingly nuanced views of AI safety that go beyond some simplistic slowdown





now speaking

now speaking of last week's White House AI events

After President Trump declared

that the term will no longer be artificial intelligence. 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 SpaceXSI, 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

Brief



lastly today,

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 hadcooked [00:08:00] 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 Homelink. 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 five thousand of these devices and will 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 Jessic Min went [00:09:00] 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 wanna see more examples, go do an X search for Muse [00:10:00] Gadgets. For now, however, that's gonna 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 five hundred 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. 

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Welcome back to the AI Daily Brief. Today we are talking about OpenWeight AI in the United States

This is a conversation that's evolved quite a bit in the last [00:13:00] 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 the 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 toplay 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 [00:14:00] 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

however, 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 [00:15:00] intelligence capable enough to help companies build their own proprietary low-cost AI systems."

c-- 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 Shinsegae 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 US labs are preparing to launch open models this month.

we've heard rumors that NVIDIA is training Nemotron [00:16:00] 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 US government wants American open models to compete with Chinese models globally."

was re... the discussion that Andrew Curran is referring to

is the one that happened over the summer when it seemed for a moment like the US 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 US labs to create their own open-weight models to counterbalance China, and has [00:17:00] 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 US 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 US AI infrastructure, both from a hardware and software perspective

Now, at, 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 US open source is NVIDIA. The company has now trained multiple generations of LLMs, with Nemotron 3.5 seeing significant use and Nemotron 4 promising to be relatively close to the frontier.

[00:18:00] 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, 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 twenty-three 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 [00:19:00] 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. [00:20:00] 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 thirty percent?'

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 [00:21:00] 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 andtoken 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 [00:22:00] partner

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

data, 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 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 Rauch 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[00:23:00] 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 US openopen-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 one

And during the current administration, he began as a US 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 Emil Michael, Under Secretary 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 [00:24:00] 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 JD Vance, 

Treasury Secretary Scott Bessent, 

and former AI Czar David Sacks will also be included on the task force

Confirming the task force in a Truth Social post, the president wrote, " The Super Intelligence Force is tasked with coordinating the effort of the federal government to ensure that America continues to lead the world in super intelligence. The Super Intelligence Force will coordinate the federal government's engagement with consumers, public interest groups, religious organizations, critical infrastructure providers, and super intelligence 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, particularlyfrom our adversaries. Being [00:25:00] 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 US companies pausing development of AI models 

for the 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 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 US Attorney's Office for the Southern District of New York, which prosecuted me

is that the con-- my instinct is that the two very different conversations we're having around open weights models, or maybe honestly even three

Open models in terms of their implications for the enterprise, open models in terms of their implications for AI safety, and open models [00:26:00] 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, 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 gonna do it for today's AI Daily Brief. Appreciate you listening or watching.

As always, until next time, peace 

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