OpenAI Blueprint and Federal Regulation

Tay in the A I Daily Brief What open AI and Ethropic, think about what happens next in AI? before that in the headlines, is the U.S. government gonna take a stake in the big AI labs AI Daily Brief is aaily podcast and video about the most important news and discussions in AI All right, friends, quuick announcements before we dive in First of all, thank you to today's sponsors, KPMG, Section, Zencoder, and Out Systems To get an ad free version of the show, go to patreonot com slash AiDailybrief or you can subscribe an app a podcast. To learn more about sponsoring the show, send us a note at sponsors at aidailybrief d. i You can also find out about everything else going on in the ecosystem, including a bunch of free education programs like the Aent OS program Or you can check out some of the paid programs that we've been building with Nufar Gaspar leading, including the upcoming four week AI executive sprint called Eecutive catch upp that is registering folks right now, but is going to be closing very soon. So if you want to check that out and get up to speed fast, check out the link in the show notes. Welcome back to the AI Daily Brief headadlines edition all theaily AI news you need in around five minutes. And boy, do we have a juicy little end to this week First up, Bombshell repeporting claims that the US. government is in talks to acquire equity in major AI companies. writes notice Senior U.S. officials have held primary discussions with major artificial intelligence companies about the potential for the federal government to acquire some shares in their firms The commentary is sourced to three people familiar with the matter Notice adds that Sam Altman has discussed this idea periodically with senior administration officials since the beginning of the second Trump term. In fact, Altman is said to have pitched the idea directly to the president in early twenty twenty five. Discussions have reportedly continued with senior officials in recent weeks Sources said that Altman viewed this as a way to more broadly distribute the ecomic benefits of AI to the public And what's more, people familiar with the discussion said that it currently centers on the idea of AI labs quote voluntarily ceding shares to the government. The shares would then produce returns that could be directed to public purposes, such as cutting an AI dividend check to all American households Now that language of voluntarily ceding makes it a little unclear whether the government would pay for the shares, and sources also state that Anthropic is not involved in discussions about providing equity at this time Now for those of you with one ey raised on the sourcing, nototice is a relatively new publication but has extremely strong credibility, founded by pololitico reporter Robert Abitron Former Washington post reporter Jeff Stein is the lead journalist on this story, and Stein is generally considered to be one of the most well sourced and highly regarded journalists in Washington. Overall, while some of this is fairly dramatic, it's also not entirely unexpected. This administration has floated the idea of building a sovereign wealth fund and taken steps towards it by acquiring minority stakes in numerous companies including Intel It's also not, as we saw earlier this week, entirely novel thinking in Washington, with Bernie Sanders floating the idea of the government taking a fifty percent stake in AI companies through a one time tax earlier this week. The idea has made for us some strange political bedfellllows, with figures on the populous right finding themselves aligning pretty closely with Sanders Responding to the Sanders proposal, Steve Bannon said this week, You can smell the stench of desperation, emanating from the oligarchs as they run heedlessly to a public market takeout We should not take tip money but force them to cough up fifty percent of the equity to be dispersed to American citizens. The horseshoe theory of American politics is well and truly intact Now in terms of the public reaction, a lot of folks just question simply why taxation wasn't the right way to do this Georgetown Law professor Peter Harrell writes, The government should tax and regulate them and potentially distribute the taxes as a dividend, but ownership risks giving the government control outside of public view and potentially the wrong incentives Bobak McGuffin writes, What if they set up some system where the company sent a certain percentage of their profits to the federal government every year? in quarterly installments Maybe the states could also choose to tax the companies if they operate in that state Joel Griffith was more blunt, writing, more quote unquote capitalism, but with Chinese Communist Party characteristics brought to you not by AOC and Bernie Sanders, but by the current United States presresident Even AxiOos businessusiness editor Dan Primack wrote, This is basically the Bernie proposal. Really never expected that electing Trump would push the U.S. government so far towards actual socialism Not even a judgment call, just surprise Now heading on over to a completely different type of topic. OpenAI has shipped a huge update for their memory system, which they're calling dreaming Now some version of memory has been available in ChatBT for a little over two years and has already come a long way. Early versions of memory were very manual and pretty clunky, relying on a list of saved memories. Users often needed to tell the chatbot to remember specific things and actively callull useless information from the list Indeed, one of the big challenes of memory is when it remembers details that are no longer relevant Last April, openenAI integrated the first elements of the system that would become Dreaming It allowed ChatTBT to actively curate memories in the background, slowly building a more accurate picture of the user's preferences. This upgrade made the process feel more natural and continuous, eliminating many of the early pain points. With this release, OpenAI said that they have made the memory system much more capable and compute efficient. Individual saved memories are gone Instead, the dreaming system will maintain a summary that provides richer context about the user The summary is fully accessible to the user and can be edited directly to make corrections or add more information OpenAI provided a simple example of where memory is useful in the context of asking ChatGBT about buying peripherals for a photography setup Without memory, ChatTPT provides generic information and makes standard recommendations. With memory, the chatbot can tailor its suggestions to the gear the user already owns In opening, I devised a new benchmark to test their system based on asking questions that required the model to recall relevant facts. With the twenty twenty four version of memory, which again was just a saved list of facts, the model succeeded on forty one point five percent of tasks The twenty twenty five version, which added the early version of Dreaming, kicked that success up to sixty seven point nine percent With the version of Dreaming announced week, OpenAI found the model could succeed at eighty two point eight percent of tasks that require the recollection of relevant facts Now, if you have done either our cllaw camp or agent OS program, you might be thinking to yourself, this system looks a lot like building the memory. md file for an agent or even something like the personal contacts project we ran through the AIDB operators community And indeed, one could be forgiven for thinking that memories is just about creating and maintaining a markdown file that stores crucial information about the user. The key difference is that ChatyBT is now running this process automatically through the backend requiring far less of the average user to take full advantage OpenAI also noted that the huge efficiency gains from their new setup will allow them to provide dreaming to free users for the first time Last summer when paid subscribers gained access to Dreaming style memory free users had only access to basic saved memories OpenAI says that they've been able to cut down the compute requirements for dreaming by five X, meaning it's now practical to serve at scale Mark Kretchman argues that this is a bigger deal than it sounds, writing The more ChatuBT becomes an actual work partner, the less sense it makes to restart from zero every time Projects, preferences, constraints, tools, writing styles, code based details, all of this should carry forward. Sounds small, but it changes the product A chatbot with real memory becomes much closer to a persistent agent I also think it's interesting in our context of how companies are adapting to the token scarcity era You remember Arvin Jane, the CEO of Glean, wrote a piece about the token economy that he called your token spend is an AI architecture problem, and in it he discussed a lot of similar themes around token efficiency. In short, all the time that you spend getting a model to remember all the relevant context each and every time are wasted turns and wasted tokens that could be fixed theoretically through better memory systems So again, in some ways, a small update, but one with potentially bigger implications Now speaking of dealing with the token scarcity era TSMC has warned that there's only so much they can do to alleviate the chip shortage that is expected to last all of this decade In candid commentary at their annual shareholders meeting on Thursday, CEO CC Weay said, Customer demand is so high and we can only support so much. We are already working very hard. We're doing our best to ensure TSMC does not become a bottleneck TSMC has already committed to building multiple new fabs in the US as well as more capacity in Taiwan, but construction takes time Way discussed a series of roadbocks that have caused the U. S. plans to fall behind schedule, including environmental permitting and a shortage of construction workers TSMC has expanded plans for six new fabs in Arizona, adding another four facilities to their construction plans Still, Way commented that construction and operational progress in Arizona is proceeding better than originally expected. While Wade didn't forecast how long he expects the overall shortage to last, he did comment It will be a long time before we can meet customer demand When a shareholder asked Weay if he plans to raise prices given the shortage, he said he would like to do that but wants to avoid the abrupt price hikes that have been seen in memory chips TSMC is famously a relationship based company, with N videoideo CEO Jensen Huang commenting that he's never signed a contract despite becoming TSMC's largest customer. Still, referencing the memory chip suppliers Way said, I envy their eighty percent gross margins, but I would never do that One interesting little one Airbnb CEO Brian Chesky is planning to launch a new AI lab Bloomberg said the lab would be focused on user interaction and design, although it's not entirely clear what that means It doesn'tound like the pitch for a new foundation model lab, so there's speculation it will be some kind of agent lab. Now Cheski, despite a very prominent role in Silicon Valley, has so far been mostly in the periphery in the AI boom. He was considered for a role on the open AI board and was a key power broker in negotiating Sam Alaltman's return as CEO after his brief ouster back in twenty twenty three. Sources said that Chesky wouldn't be going founder mode for this venture. Instead, he plans to remain as CEO of Airbnb and hire a new leader to helm the lab. Bloberg said that the unnamed startup is in the early stages of fundraising, and honestly for most people the jokes write themselves Taylor writes, soon, you can rent your Airbnb as a data center But others are interested to see what comes out of this Sackham writes. Instead of the Nth lab to focus on coding benchmarks, we might get a model that is actually great at coming up with new UIUX primitives, Huge alpha in just having a model that makes great UIUX experiences. Lastly, today as we head into the weekend, X is a buzz with rumors of new models coming soon While some expected GBT five point six to arrive this week, it's looking like we'll have to wait a little while longer The openen AI account dropped a new promotional video with the tagline timee to fly, and the open AI developers account posted a still from the video with the caption, Look closely. There's more in the showcase Many thought this was referring to a solid diamond symbol next to the model selector, perhaps indicating a new ultra fast speed mode. Still, the open AIe release that everyone is holding their breath for is, of course, GPD five point six On the anthropic side of the house, it's all about mythos. Leo at Synthwaved posted Anthropic is gearing up for the public launch of a new version of Mythos better than Mythos prereview A checkpoint of the model, codenamed Oceanis was made available to Red Tamers yesterday. I'm told these programs typically begin seven days after the wider launch Lan on Twitter, meanwhile dug up another API endpoint serving the model, noting the sky high pricing of sixteen per million input tokens and eighty dollars per million output tokens price the model at around three times the cost of Opus four eight, but slightly below the reported pricing of Mythos's prereview, which was twenty five per million input and one twenty five per million output Whatever the details, it is clear that Mythos is coming soon with Andrew Curn writing, public release is almost here. I predicted this for the sixteenth and I'm feeling pretty good about it Now, usually model coming rumors aren't all that interesting, but in this case, I think they actually have an interesting story to tell about how the labs see competition. Specifically, we know that some version of Mythos is coming And that theoretically it's much more powerful than Opus four point eight. And so what's interesting to me is when OpenAI decides to release their next version of GPT Right now, they're not under a ton of pressure to do so. The release of Opus four point eight didn't all of a sudden catapult anthropic into the agreed upon leader position once again. Many people still think five five is better, and it hasn't really shifted the pro coder momentum around five five at all What that means is that if they release five point six right now, it is not a response to four eight, but a preemption to Mythos preview, meaning that they think that five six or whatever the version is called, probably won't be able to hang with Mythos when it comes Because you have to think that if five six is as good as or better than Mythos in the estimation of open AI, they would wait until right after Mythos came out to try to clip off the new momentum that Mythos will inevitably give anthropic So in this case, more than normal, the timing will tell us a lot about how companies see where the state of the art is relative to one another In any case, lots of fun coming up in the early summer of twenty twenty six But that is going to do it for today's slightly extended headlines. Next up, the main episode. 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Out Systems, Build your agentic future Welcome back to the A Dily Brief At the close of the headlines today, I mentioned that the particular release sequence of the next upcoming models from Anthropic and Op AI would go a long way to telling us what those labs think about where the state of the art is and implicitly where their competition with one another lies And that theme of the big labs revealing more about the state of the world as they see it is the subject of our main episode as well This episode is going to be anchored around two pieces of writing that came out one each fromanthropic and open AI The first from anthropic is called when AI Bus itself. It is a bit of a meditation, I guess, you would say, around the state of AI development and what comes next The Op AI document is a little bit more pointed. It is a policy document called Democratic Governance of Frontier AI, a blueprint for a federal framework, but actually starts from a similar place of giving us a picture of where openpen AI thinks we are when it comes specifically to AI development Now to do a little bit of upfront contextualization Here's how Ethan Mok framed the anthropic piece when AI builds itself. He said, I think it is really worth reading this piece on RSI at Anthropic. There's a bit of nvel gazing, some marketing, and a lot of very sincere beliefs about what Anthropic thinks is likely in the near future of AI that you probably want to be aware of The big theme of the piece is RSI or recursive self improvement. And what Anthropic is pointing to at core is an inflection point moment coming very quickly around how the next best AI gets built. Anthropic writes, For most of AI's history, humans drove every step in its development cycle. But atanthropic, we are delegating a growing share of AI development to AI systems themselves, which is speeding up our work taken far enough and given enough compute, that trend points to an AI system capable of fully autonomously designing and developing its own successor This is called recursive self improvement. We're not there yet, and recursive self improvement is not inevitable, but it could come sooner than most institutions are prepared for Now a lot of what you're going to see on social media from this Big surprising numbers For example, they write, anthropic engineers on average ship eight X as much code per quarter as they did from twenty twenty one to twenty twenty five Another big number is eighty percent. That is the percentage of Claud's production code that is authored by Claude itself. They also note that as they put it, the code that Claude writes is good and improving. The code they say means two things. It works and is written in a manner that allows another engineer to understand it and build upon it On the first criterion, they say, the evidence is clear. The rate at which anthropic staff correct, redirect or take over mid task from Claud has been falling steadily for a year, including on the most complex and open ended tasks This means problems with no clear specification, where the engineer isn't sure what the answer looks like As evidence, they point to a chart of Cloud Code session success rate, where across trivial tasks, routine tasks, substantial tasks, and open ended problems, the success rate of all of those has climbed well above sixty percent and for the trivial routine of substantial tasks, well above eighty percent from a much lower place less than a year ago They also note that the mode of how Claud interacts with the code base is changing. Alaud they write is getting better at proposing its own experiments They point to research that was published in April of this year that was exploring whether a weaker AI could manage a stronger AI The evidence suggests that the human role is narrowing at each step in the AI development process. Once human and AI authored code quality reach parity, humans will stop writing code entirely and shift to only reviewing it But if they can't review code as quickly as Cloud can generate it, human review will become the bottleneck to AI development Similarly, once Claude can run experiments, the question shifts towards which of those experiments is worth running. Put simply the doing, writing the code, running the experiment, producing the result now costs almost nothing in human time, even if it still has costs and compute An area of human comparative advantage for now is research, taste and judgment. including choosing which problems matter, which results to trust, and when an approach is a dead end Indeed, they continue The work that is still in human hands, choosing which problems to work on is what matters most Without that judgment Claude is a capable assistant, but not as a system that could drive AI progress on its own They write, it's genuinely unclear whether today's training methods and architectures could unlock that capacity But even if we suppose that Claude never achieves good research taste A conservative reading of our evidence still implies compounding acceleration Now the meat of the piece and the part that's generating the most discussion is the last section on three possible futures The first possible future which they say they include for completeness, but don't believe it's likely, is one in which the trend stalls, but today's AI capabilities are widely diffused They write This article features many exponential trajectories, but these trajectories may actually turn out to be S curves. We may be approaching the bend in the curve where returns to scale diminish and the lines straightens then flattens. The judgment that separates a competent researcher from a great one might be a capability that cannot come from scaling up training inputs like compute and data If so, getting past this bottleneck would require a new idea like an architectural approach that supplants the transformer architecture that all current frontier models use Alternatively, and this is my editorialization, but I think we're seeing lots of evidence of right now. The binding constraint to AI progress could be in the supply chain, not the model. Advancing and diffusing the frontier may require more energy and compute than presently exists The pace of chip fabrication, grid expansion or interconnect bandwidth may be the constraint rather than the intelligence itself Now, still they say, even if model capabilities were frozen at today's level, we would expect major changes to occur in the world. They point to the example of Mythos's prereview finding more than ten thousand high in critical severity software vulnerabilities across many of the world's most important systems. Still, like I said, They don't believe that this scenario is particularly likely. Every capability we can measure they write has so far followed the same curve. We've not yet seen that curve bend Of the three futures we consider, this one would give governments and societies the most time to adapt more worryed they continue about the next two, which would move faster and leave far less room for preparation Scenario two then is the AI labs continuing to see compounding efficiency gains In this scenario, they say AI development becomes substantially automated, but humans continue to set research directions and judge results In this scenario, one hundred person companies could do the work of ten thousand or one hundred thousand person organizations They say this would revolutionize knowledge work in government services, but could also be turnermed to harmful ends, from authoritarian surveillance of whole populations, to influence operations that tailor manipulation to each individual and run at a scale no human team could match Now, interestingly, and this is where I wish there was a bit more of a discussion, they write that while this is the scenario that is most likely based on the evidence that they've seen They also note spepeeding up one part of a process often just shifts the bottleneck elsewhere. Overall, pace is capped by the parts that haven't sped up. In computing they write, this is known as AmDal's law, and the same logic can apply to organization Andthropic has already encountered one signature of Amdal's law as we've beun to push more code around the organization, human code review has become a new bottleneck. also encountered this friction outside engineering There's been an explosion of new ideas, initiatives, tools, and simulations as a result of anthropic employees working with highly capable models far more than we have the capacity to pursue. The rate at which organizations can spot and fix these bottlenecks may be a skill that improves over time, and it may become the most important skill for any organization. This gets into a lot of the ideas that I've explored around the infinite backlog And why all of a sudden I don' think we're going to have jobs Eron Levy from Box commented on this part, saying that it points to the key element of the optimistic scenario for AI AI he writes lowers the barrier dramatically to allowing us to do more. As a result of that, we have far more ideas than we can pursue, and the ones that we want to pursue were ultimately limited by our ability to go take on the surrounding work to execute those ideas There's almost no amount of AI progress that can happen where that goes away. AI is going to let us build much more software, launch more marketing campaigns, research more drugs and so on All of this work, even when augmented by agents, still ultimately requires people to manage Now back to anthropic, the third scenario they point to is the full recursive self improvement scenario, where AI systems start to build their own successors Now this scenario is where you see the most hand waviness phanthropic, with them just not really knowing how to guess at all the implications of this The final section is the one that has been jumped all over, especially by AI safety advocates They write If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications We think that would likely be a good thing We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology But they write in the same breath, if a slowdown simply lets the least cautious actors to catch up technologically It could leave everyone less safe Without a global coordination mechanism, companies and governments will have to make difficult decisions about safety while under competitive and geopolitical pressures They go through and talk about all that would be required for a slowdown or pause, noting that while none of it would be necessarily impossible in principle, pointing to, for example, the intertermediate range nuclear forces Treaty Qote, Th those regimes took decades to build both the infrastructure of the trust. We don't have that long, they, write? A unilateral pause by one lab by contrast is achievable immediately, but accomplishes much less It would change who the front rununner is, but would not create the wider deliberative process that is currently missing comoming months, we will organize conversations where policymakers, researchers, civil society, and other AI companies can help answer some of the questions this piece raises especially around full recursive self improvement and how to create better options for coordination and deliberation The window to investigate the questions together is here and people outside AI companies should be involved in this deliberation. Now the responses to this fully run the gamut. Some in the AI safety community are thrilled The AI safety memames account sums up Holy blank, let's blank and go Yet others, like if anyone builds it, everyone dies, co author Nates Sarz writes, onene big quibble is that they aren't thinking big enough The tone reads like RSI could happen but don't fret too much. It'll probably be fine Rather than OMFG, we're possibly on the brink of AI's that makes smarter AIs. Society needs to act Others though, find that the whole thing kind of leaves a bad taste to their mouth Seaan Ralston writes No way that anthropic slows or temporarily pauses frontier AI development What an insinincere and silly sentiment If they really feel that way, then let them act that way Corey Quinn writes, asking your competitors to pause development right after you file your S one is the single most effective moat building exercise I've seen pitched as ethics they not realize the quiet period is for them, not homework they assign their competitors Now there's an even broader critique of Anthropic that recently came out on the All in podcast from legendary investor Bill Girley, who spent thirty days reading everything that Anthropic had ever written, coming to the conclusion I don't think they're writing software. I think they're midwifing a deity here I don't know which one I'm more afraid of. The regulatory capture or this Dr. Frankenstein theory Jason Kalcanis chimed in. These are delusions of grandeur. Let's call it what it is. They believe they're so powerful they can create God The God you create is going to be so benevolent and perfect that will give you your little pellet of resources Now even if you don't think that that's exactly what's happening, the fact that that idea is coming up in mainstream conversation We'll give you some idea why the public discourse gets so frustrated with these companies who talk about the huge implications of their work and yet proceed on with it at an ever increasing pace former AIsR David Saxs wrot Signs you might be trying to get your frontier AI lab nationalized. You compare it to nukes, threaten half of white collar jobs, warn recursive self improvement could end humanity Then race ahead anyway In other words, you want the government to save us from you Now, like I said at the beginning, if anthropics's document is more meditation on the state of the world OpenAI's policy document about democratic governance is a little bit more precise And yet still, one part that people noted is that it also mentions RSI as a starting frame of reference In the first paragraph openAI writes, we also see signs of recursive self improvement in today's systems, where AI development is itself accelerated by AI. We expect this to increase competitive pressures among developers and nations and create governance challenges that existing institutions are not equipped to address writes Chubby The vibe is changed, something is happening Now the main thrust of open AI's paper is that democracies specifically have a key role to play in solving these very complex and difficult problems of advanced AI in larger society They proposeed three broad policy directions. First, they call building a national framework through reverse federalism basasically arguing that instead of the national system preempting state rules Congress should, in fact, adopt and scale up the best pieces of state regulations Now the second policy priority is one that actually runs a little bit counter to the executive order The recent executive order put the locus of the voluntary testing regime in the NSA, whereas OenAI is arguing that we need to be investing in civilian institutions, specifically groups like the CAISI, the center for AI Standards in Innovation They also argue contra the EO, that at least eventually there should be a mandatory evaluation process, not just a voluntary one Their last policy priority is, quote, mobilizing a whole of government resilience strategy, writing that frontier AI should be treated as a national priority, requiring coordination across national security, public health, cybersecurity, scientific, diplomatic, and economic agencies, as well as with international partners AI policy expert Dean Ball writes, This seems reasonable. Having CAassie a civilian agency conduct this testing in primarily non classified ways is the way to ensure it does not become a licensing regime Trump's EO classification of the process raises the risk that testing morphs into a de facto mandatory permitting and licensing system Now, in addition to this document, which is being treated in some ways as a response to the recent executive order, even though it feels fairly likely that it was being worked on before that EO was finalized, Congress is also starting to get a little bit more active on what they think AI regulation should look like On Thursday, Republican Jay Overnultty and Democrat Lorie Traayhan unveiled their bipartisan AI bill in the House. A comprehensive two hundred and sixty nine page bill aims to set up a federal regulatory framework that would override the growing number of state AI laws The bill would require leading AI labs to create and implement plans to deal with catastrophic risks posed by their models. thirird party auditors would be required to ensure compliance Now, while this seems in line with the AI law recently passed in Illinois, much of the controversy right now around any AI regulation is about a federal bill preempting state authority Representative Trahan has received pushback from fellow Democrats for supporting this bill, particularly in the Northeast New York has already passed their own laws and her home state of Massachusetts is quickly moving to do the same. With Brad Carson, the president of Americans for Responsible Innovation and former Arizona Democrat arguing that cutting state legislators out of the process would be a quote generational mistake Still, I would say that if you're reading the tea leaves on average, this bill is a little bit less dead on arrival than most, at least in terms of the substance, the problem is the current timeline Politico repeporter Meredith Lee Hill writes, Lots of skepticism in House GOP leadership about the Abernolty AI framework. And also getting any AI bill to the floor before midterms. Speaker Johnson when asked if he was committed to putting an AI bill on the floor before November said, Well, we're going to do it as soon as we're able to build consensus around a package So I mean, I would consider it a high priority, but I don't know yet on the timing In other words, I wouldn't hold my breath Anyways, friends, there is a lot going on in both the technology and policy of AI, but for now that is going to do it for today's AI Daily brief. I Appreciate you listening, or watching as always, and until next time Pace.