The Power to Shape AI

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Now we are back with a weekend episode, and as you guys know, weekend episodes are long reads/big-think episodes. There are a chance for us to zoom out a little bit, get away from the grind of daily news announcements, and try to think about things a bit holistically. Recently there have been more big-think than long reads episodes, but today we get to do

both because Professor Ethan Malik has dropped his latest essay "The Shape of the Thing." And what we're going to do is read some big chunks of that, and actually an earlier essay

“of his as well, and then talk about what I think the big thesis is, which is the power”

to shape AI. Ethan writes, "In October of 2023, I wrote about the shape of the shadow of the thing, speculating on the thing that AI might turn into in the coming years. I think we can see the thing much more clearly now, and some of the consequences that come with it."

Now, record scratch editor's note, for the sake of posterity, let's actually go back to that writing from October 2023, "The Shape of the Shadow of the Thing." It's a pretty interesting time capsule, and provides a nice pairing with this new essay. So zooming back to October 2023, Ethan writes, "A lot has happened in the past week or so, so I wanted to write a post-taking stock of where we are."

In many ways, I see us reaching the culmination of the first phase of the AI era that started

only 10 months ago with the launch of Chatchee BT. It ends with the upcoming launch of Google's Gemini, the first LLM model likely to beat OpenAI's GPT4. Now enough pieces of the Jigsaw puzzle are in place that we can start to see what AI can actually do, at least in the short term.

“Even more importantly, the actual implications of what this phase of AI will mean for work”

in education is currently a noble. It is unknowable to all of us who don't have insight into what the AI labs have planned, but it is also actually a noble to them. I guarantee that the people at Google are open AI or Microsoft do not know the implications of AI for your job or your company or your education, or even all the ways in which the

systems they are building will ultimately be used for good or bad. So we can't see the thing that is being built, or even the shadow it is going to cast over work in education, but we can get a sense of its general shape. From there, Ethan makes a few observations in a few predictions. He predicts that Gemini would outperform GPT4, which didn't exactly end up happening, but

what he did get right is that we would be in a period for a while where all the models were floating around the same level of GPT4 style intelligence. That was of course a lot of the story of 2024. He talked about the increasing capabilities of AI image creation as well as AI voice, and concludes, we have these pieces which let us guess at the shape of the AI in front of

us. It isn't science fiction to assume that AI is will soon talk to you, see you, know about you, do research for you, create images for you, because all of that is already built and working. I can already pull all these elements together myself with just a little effort.

That means AI can quite easily serve as personal assistant, intern and companion, answering emails, giving advice, paying attention to the world around you, in a way that makes the series an Alexis of the world look prehistoric. In many ways what happens next, the actual thing that all of this becomes in the near-term depends on our agency and decisions.

It is not going to be imposed on us by machines. With these new capabilities AI can either serve to empower and simplify or to remove power. Some of these consequences are noble, and need regulation or responsible action by individuals, and some is going to fall unevenly across industries and societies. It is up to us to figure out how to use this new technology to empower and uplift rather

than harm. So that's where Ethan ended that post back in October 2023, but now we return to today. As I've been discussing in recent posts, Ethan writes, "We've entered a new phase of AI. After Chad GBT was introduced, human AI worked at the form of what I call co-intelligence, where humans would prompt AI back and forth to get help on tasks.

During in late 2025, we entered a new era thanks to AI-agencyc-clod code, open-AI's codex, and open-clod. These are AI systems that you can just give work to, sometimes hours of human work and get back reasonable and useful results in minutes." This is an era of managing AI's rather than working with them.

This new approach to AI is the outcome of the rapid exponential improvement in AI abilities. That means you can't understand where we are and where we might be going without understanding the increasing capability of AI.

changed with the evolution of an image generation test he's been running for years now, order on a plane using Wi-Fi. As you can see, he writes, "The progress from 2022 to 2025 was rapid and remarkable." So what has happened in the time since April 2025, he asks, "With nearly perfect images, video has become the new frontier and has also seen exponential gains."

He then shares a video from C-Dance 2.0 that was created with the prompt, a documentary about how otters view Ethan Mollick's otters test, which judges AI's by their ability to create images of otters sitting in planes. "In a world of ones and zeros, there exists a final furry altitudes of truth." The verdict is clear, back to the drawing board humans.

Ethan writes, "Aside from a single pronunciation mistake, this is pretty perfect. Down to the fact that the otters are animated to have human-like expressions." Of course, video models are cool, but they are not necessarily indicative of what useful agent a AI can do.

“So what if we look at the benchmarks of AI ability?”

Do we see the same exponential curve? We certainly do in the most famous evaluation of AI today, the meter long tasks graph. It tries to measure AI progress by seeing how much human work an AI can complete autonomously with some measure of reliability. It is attracted to its share of critics and even meter has pointed out potential issues,

but if you don't like the meter graph, you will find most graphs of AI ability have that same curve. Even then picks a set of four different benchmarks and shows how all of them have much the same exponential growth curve.

And yet he writes, " Despite these amazing capabilities and tests, companies are still

very early in adopting AI, meaning that, as of yet, remarkably little has changed in most organizations, but most organizations doesn't mean every organization. We are already starting to see the first appearances of new approaches to organizing that take advantage of the new abilities of AI agents." Section?

Radical changes to work.

“A few weeks ago, a three-person team at Strong DM, a security software company focusing”

on access control, announced they had built a software factory, a way of working with agents that relied entirely on the AI to test, write, and ship production software without human involvement. The process included two quite radical rules. Code must not be written by humans, and Code must not be reviewed by humans.

To power the factory, each human engineer is expected to spend amounts equivalent to their salary on AI tokens, at least $1,000 a day. The basic idea of the factory is that it takes future product road maps written by humans and turns those into products. Coding agents use those road maps to build software while testing agents try out the software

in a simulated customer environment, with the testing agents built as needed. The sets of agents provide feedback to each other, looping back and forth until the result satisfied the AI. Then humans review the finished product, and the results are shipped to customers without anyone ever touching or even seeing the underlying code.

Ultimately, Ethan writes, "The particular details of the software factory matter less

than the fact that such radical experimentation into how we work is now not only possible, but likely necessary." AI is good enough to change how organizations operate, and the experimentation is just getting started, even as models continue to improve. Section, rolling disruption.

Practical agents, jagged exponential improvement, and the ability to radically experiment with the nature of work, combine to form a sort of rolling and unpredictable environment for AI advances. As AI capability crosses thresholds, it unlocks radical new use cases that change people's views sometimes overnight about what AI can do.

At the same time, organizations experimenting with AI will figure out how to make it work for them, leading to sudden announcements about new strategies or large-scale shifts, in which kinds of employees companies value most. Now, Ethan points out that this is no longer speculation, and points to the last we can February as an example of the sort of disruption to come.

That was, of course, the week that we got the Citrini Research Substack Post on how AI being too good would cause a huge financial crisis, destroying a bunch of different businesses by 2028, then that same week we got block announcing 40% of its company were being laid off, very heavily implying it was due to AI, and then, of course, to end the week, we got the very public and very aggressive spat between Pentagon and Anthropic over who gets to

control AI, and specifically how Claude could be used by the government.

In a lot of ways, Ethan writes, "Each of those cases were not what they first appeared

to be. The Citrini Report was a fictional scenario. The block layoffs were not about AI, and the conflict over AI at war revolved around a number of complicated issues that are still not completely clear.

“But I think that single week is a good illustration of what the near future will feel”

like. Sudden revelations about AI capability, leading to rapid-market reactions. Increasingly real impacts of AI on jobs, even if there is a lot of debate over whether those impacts will be good or bad in the short term, and increasing entanglement between AI companies and policy making around the world.

As the stakes go up, it is likely things will feel even more unstable.

organizations absorb the changes gradually, and the rolling disruptions become more manageable

“as people learn what AI can and can't do.”

History is full of technologies that were supposed to change everything overnight, but instead took decades to fully reshape the economy. But I wouldn't bet on it. One reason is that AI companies are telling us, barely explicitly what comes next, recursive self-improvement or RSI.

This is the idea that AI systems are increasingly being used to build better AI systems, creating a feedback loop that could accelerate the very curves I showed you above. At Davos in January, and Thropic Stario Amade explained that if you make models that are good at coding and good at AI research, you can use them to build the next generation of models, speeding up the loop.

He noted that engineers within Anthropic barely write code themselves anymore. When Open AI released its latest codex model in February, the company stated it was "Our first model that was instrumental in creating itself." And Google DeepMind's Demisha Sabis acknowledged at the same Davos panel, that closing the self-improvement loop is something that all the major labs are actively working on,

even as he warned there are still missing capabilities and real risks. We don't know how far this goes. RSI has been a theoretical concept for decades, and the labs may hit bottlenecks, whether in compute, in data, or in the sheer difficulty of AI research. We also don't know whether LLM-based AI will eventually hit a ceiling where they cannot

get any better, or where the jagged frontier never smooths out.

I don't think we know anything for certain, but I also think we are past the point where recursive self-improvement is science fiction. Instead, it is an explicit item on the roadmap of every major AI company. If the loop does close, the exponential curves we've been watching would get steeper with an uncertain endpoint.

So here is where we are today. The instability of that single-week in February was a preview of what it feels like when the increasing ability of AI starts to interact with markets, jobs, and governments all at once. That feeling of uncertainty will likely only spread further, but uncertainty is not the same as

helplessness. When a technology is this powerful and this unsettled, the choices that individuals and organizations make right now matter more. We can see the shape of the thing now, but we can still influence the thing itself, and what it means for all of us.

We clearly don't have rules or role models for how AI gets used at work in schools or in government. That's a problem, but it also means that every organization figuring out a good way to use AI right now is setting a precedent for everyone else. The window to shape the thing may not last long, but it is here now.

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“There's a new standard that I think is going to matter a lot for the enterprise AI”

agent space.

It's called AIUC1, and it builds itself as the world's first AI agent standard.

It's designed to cover all the core enterprise risks, things like data and privacy, security,

party.

“One of the reasons it's on my radar is that 11 labs, who you've heard me talk about before”

and it's just an absolute juggernaut right now, just became the first voice agent to be

certified against AIUC1, and is launching a first of its kind insurable AI agent. What that means in practice is real-time guardrails that block unsafe responses and protect against manipulation, plus a full safety stack. This is the kind of thing that unlocks enterprise adoption. When a company building on 11 labs can point to a third party certification and say

our agents are secure, safe, and verified, that changes the conversation. Go to AIUC.com to learn about the world's first standard for AI agents, that's AIUC.com. So that's the end of Ethan's essay, another great one. Thank you, Ethan, for that, and here's where I wanted to pick up the thread. It is very clear at this point, and everyone agrees, that we have just lived through or

are living through a major transition in the AI capability set. In fact, another even more crystallized distillation of that, also from Ethan, was a tweet from the beginning of March where he wrote, from an AI user perspective, the four big leaps so far in ability, one GBD 3.5, aka Chad GBT in November of 2022, two GBT4 in spring of 2023, three reasoners starting with O1 preview, but the real deal was O3, spring

of 2025, four workable agentic systems, harness plus good reasoner models to December 2025.

“I think that's right, but I think you could simplify it even farther.”

I think we are in the second great transitional period.

The first was the Chad GBT moment, which I would argue really came to its full expression in spring when GBT4 hit, and the second is now, these workable agentic systems. With the reasoners, although they were tremendously different, being just the prelude to what we have now. So again, there is as we've talked about on the show extensively, widespread agreement,

of the significance of this moment, and with it, as Ethan has pointed out, has come a feeling of destabilization. Certainly Wall Street is feeling it, where, of course, living through the Saspocalypse, which has been this cascading wave of disproportionate market impacts every time anthropic announces some new feature, we're also feeling it in politics.

It's not just the fight between anthropic and the Pentagon, AI as an issue is forcing itself into consciousness everywhere right now.

“Just this week, Bernie Sanders dropped the nine-minute video about his plans for legislation”

to declare a moratorium on AI data centers. You see it in polling of Americans, where members of both major parties have effectively no faith in either party to handle artificial intelligence, and you even see it in and around the people who are closest to this technology. A few days ago, semi-analysis is Dylan Patel wrote, "Being in SF is like being in Wuhan

right before the pandemic. Something is happening, it's going to hit everywhere, but so few people know it." And all year, there's been something bothering me about this discourse, and recently it's crystallized. There's sort of a fainid helplessness in all of these discourses.

A denial, maybe implicit instead of explicit, but they're nonetheless, of human agency to shape what this all is going to mean. It's as though because these forces are so large that we're shrinking rather than rising to meet them. We forget what our committee said, "Give me a lever long enough and a full-cremon which

to place it and I will move the world." Unfortunately, it feels like the imagined helplessness is getting worse, not better. A group called the Alliance for Secure I, which I know nothing about, announced the new website this week called JobLost.ai. It's a real-time tracker of AI-driven layoffs across the US.

They write these jobs are disappearing, the numbers are growing, and we're counting every single one. Now, we're going to hold aside the entire phenomenon of AI-washing, adding knowledge that even if lots of the layoffs that are being blamed on AI are not exactly about AI, but directionally this is still something that's worth engaging with.

But let's listen to the ad that they actually released. Silicon Valley CEOs love telling us that AI will create jobs. Meanwhile, companies are laying people off, left and right. Slowly AI is taking over coding, in 24 months, entire industries could be gone, so we are holding them accountable.

Now, you say it's a 30-second video.

They can't fit all the remediation ideas and policy suggestions into that video. They just need to grab people's attention, right? Okay, but then you go to joblost.ai, and once again, there's nothing there about what they're trying to do. It's just a big list of job losses that are blamed on AI.

What about on the Alliance for Secure AI page? Surely there must be jobs policy there, except nope, there's not.

and even for the issues they do mention is conspicuously low on any actual policy ideas. So my question is this, what is the point here, just to make people aware that AI is going

“to impact jobs, what are you supposed to do with that information?”

What is holding the CEO's accountable even mean? Are you going to mandate that companies can't use AI or that they can't fire people? If so, how are you going to make those policies work practically and in the real world? Do you have other ideas for policies that could be pro-worker? If so, what are they?

The point is that while I agree, we are heading into an extremely challenging and disruptive middle, limital period between two totally different paradigms and eras, where we have to engage deeply with the disruption that that middle period will bring. This ad, this campaign, this organization, doesn't say anything and doesn't aim for anything. In fact, it does worse than nothing because all it does is perpetuate this feeling of learned

helplessness or worse the idea that there's some simple solution like holding CEOs to account, whatever that means. We are not helpless on an individual level, we are not helpless on a societal level. Being more aware of the feeling of being unmoored and feeling more acutely the instability, while uncomfortable is not a bad thing.

In fact, it is a prerequisite of action. The old parable about the frog being boiled in the pot is entirely about what happens when we don't have that feeling of discomfort as the environment changes around us. As uncomfortable as this discomfort is, it is necessary, and it can turn into good. There's a reason that it seems like every couple of weeks I'm turning around and dropping

another free self-directed program like AIDB New Year or Claw Camp.

It's not because I'm infinitely distractable and just always looking for the new thing

to keep my brain entertained. It's because I decided coming into this year that rather than having the type of debates that characterize a lot of the last part of last year, fighting with people about whether AI was or wasn't real, I instead wanted to spend my time in energy, providing value for the people who had decided that it was real, and that they were not just going to be a passive

recipient of the future. My whole thesis with things like Claw Camp is that, while yes, much of this is technically challenging and difficult in new ways that will stretch you, anyone who is willing to put in the time can take advantage of the greatest tutor and build partner we've ever had in the AI systems themselves to figure out how to leverage these new tools to achieve things

that were never possible before. And the fact that nearly 7,000 people have joined Claw Camp and decided to go try to build agents despite all that technical complexity. Despite the open-quality designer explicitly designing it not to be easy so that it kept people who might have trouble with it away is testament to the fact that people are not

just going to accept AI happening to them. Now of course on a societal level it gets a lot more complicated, but I would argue that even there there are reasons to view what's happening right now with optimism. The way that the fight between the Department of War and Anthropics is spilled over into the public might be unseemly and offend our better sensibilities, but the fact that the

conversation is happening live and in public means a lot more people are thinking about these things than they might otherwise have. That's creating space within the overturn window to expand the broader conversation about AI policy. I am very on record as thinking that Bernie Sanders' moratorium on data centers is likely

to have exactly the opposite impact that he wants.

“I think it's about a short-sighted and ill-conceived policy as is possible when it comes to”

that particular set of issues. But am I glad he's elevating the conversation? You better believe it. It's because of that elevated conversation that people who are willing to propose more wildly different types of policies are getting more space than the discourse now.

After Yang recently on CNBC, proposed that we should stop taxing workers and tax AI instead.

Basically if the balance between labor and capital is fundamentally shifted, change where

the tax burden goes. That might be a truly insane policy. You may be screaming at your headphones that I'm even giving that airtime, but I tend to believe that one of the best things about America is our long history of people not being scared of new ideas.

Even if we ultimately decide they're not the right ones. In a world where as much as up for grabs as it is right now with AI, we are going to just have some conversations that we wouldn't believe that we would have been having just a few years earlier. This theme of human agency was also what I wrote about a couple of weeks ago when I was

stuck after an emergency landing in Brazil, that we forget that markets and societies are ultimately mechanisms for structuring getting people what they need and what they want. A reality which, of course, should remind us of our agency even if it's manifest only in small ways. Even says we can still influence the thing itself and what it means for all of us and that's

what you need to take away. When it comes to AI, ultimately, as big as these changes feel, we do have the power to shape AI.

“For ourselves and for the world around us, and I think we should remember that.”

That's going to do it for today's AI Daily Brief.

Appreciate you listening or watching, as always, until next time, peace.