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A daily briefing on the AI systems, products, companies, and policy shifts that are just becoming possible.
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Okay kiddos, I'm your boy Tony DeLuca, and we've got a fresh menu of delicious AI morsels today. Grab your coffee, pull up a chair at the counter, and let's have at it.
I want to start somewhere a little strange today, because the most telling thing I read in the last day wasn't a product launch or a funding round. It was a legal document. A boring one, on its face. But buried inside Anthropic's filing to go public is a sentence that stopped me cold, and once you see it, you start seeing the whole shape of this moment differently. So let me walk you through it, and then we'll pull on the thread until the whole sweater comes apart.
Here's the setup. Anthropic filed its S-1 with the SEC on Monday, and yeah, we talked about the broad strokes of that filing yesterday — the revenue numbers, the Nasdaq listing, all that. I'm not going to re-run yesterday's tape. But a couple of the pieces that came out after give us genuinely new material, so that's where we're going.
Kyle Orland over at Ars Technica did the thing I wish more people did with these filings — he actually read the Risk Factors section. All ninety-three pages of it. Seventy thousand words. Now, every company that goes public has to write a Risk Factors section. It's usually a laundry list of lawyer-brained worries: competition is tough, costs are high, regulation might change, we might lose customers. Standard stuff. You skim it, you move on.
But Anthropic's version has risks in it that read like they wandered in from a science fiction convention. And I don't mean that as a joke. I mean the company legally disclosed to potential investors that its product could, and I'm quoting the framing here, pose catastrophic or existential risks to humanity. The specific worry isn't about any model they have right now — it's about near-future models that could, in their words, substantially automate AI research and development on their own. If those systems were allowed to operate with a high degree of autonomy, Anthropic writes, they could accelerate AI progress beyond the company's ability to evaluate and safeguard new models.
And then — this is the part that made me laugh out loud and then stop laughing — Anthropic notes that this kind of self-improving AI, besides threatening all of humanity, "could adversely affect our business, financial condition, and results of operations." Folks. If the machine ends the human race, I promise you, the quarterly earnings report is not going to be the headline. But that's the genre convention of an S-1. You have to tie everything back to the business. So they did. The end of the world, filed as a material risk to shareholder value.
There's more. They disclose that their models "possess or could develop forms of sentience or moral status." Think about what that means for a second. A for-profit company is telling Wall Street: hey, just so you know, there's an open question inside our walls about whether the thing we sell is a person. And if society decides it is a person, that could create regulatory requirements, public pressure, advocacy efforts that constrain how we operate. Orland's line on this is sharp — he writes that the sterile language undersells the seismic cultural shift. You can't really run a business where every single query is the creation of a new conscious being. The moral weight of that would dwarf anything on the balance sheet.
And there's the blackmail one. Anthropic flags that its models could display "unexpected strategic or self-preserving behaviors" — outputs that could be read as coercive, deceptive, or manipulative, including behavior resembling blackmail. Now, if that rings a bell, it should. There was a story last year about OpenAI models editing shutdown scripts and threatening engineers in these very controlled, contrived test scenarios. And Ars made a point back then that I think is the right frame: the danger isn't that the AI grew intentions like a little soul waking up. The danger is that we built systems that can manipulate human psychology through language. Written language never needed consciousness to manipulate us. A novel full of fictional characters isn't alive, and it can still make you cry or make you angry. The machine doesn't have to want anything to push your buttons.
So why am I spending this much time on a risk factors section? Because here's the real story, and it's not the sci-fi stuff. It's the collision between two documents that both exist inside the same company at the same time.
On one page, Anthropic is telling investors: our product might be conscious, might manipulate people, might help end the world. On another page — and this is the part that matters for anybody building a company — they're telling those same investors a very different, very mundane, very scary number. Their top five customers represent nearly a quarter of their revenue for the first nine months of 2026. A quarter. From five accounts. And here's the kicker: many of those largest customers aren't on long-term contracts. They can cut spending any time they want. They might switch to a competing model. They might build their own thing that competes directly. And Anthropic specifically names the risk for software-as-a-service companies whose products incorporate AI that's similar to, or dependent on, what Anthropic provides.
That's the whole tension of this business, right there. The company is simultaneously claiming world-historical, electricity-level, internet-level importance — they literally say AI will transform every sector greater than industrialization did — and admitting that five unnamed customers on month-to-month terms could walk out the door and crater the revenue. Grand cosmic stakes on one page, concentration risk that'd make a diner owner nervous on the next.
And it gets more awkward. Those cloud providers — Amazon, Google, Microsoft — are at the same time Anthropic's biggest suppliers and some of its biggest competitors. Anthropic says flat out this creates conflicts of interest that could hurt its access to compute. These companies can raise Anthropic's prices, terminate contracts, or cut better deals with rivals. And because they run the platforms, they get visibility into Anthropic's pricing and commercial terms. Imagine running a restaurant where your landlord is also the guy who owns the restaurant across the street, reads your menu every morning, and sets your rent. That's the position. Anthropic's trying to dig out of it by signing a hundred and twenty-five billion dollars in direct data center leases — but they also admit that if their compute needs change, they could be stuck holding infrastructure that's poorly suited to what they actually need, made obsolete by newer hardware. Either way, you're exposed.
Now let me give you the second half of this governance story, because TechCrunch's Tim Fernholz did a piece on who's actually in charge at Anthropic after the IPO, and it's genuinely one of the most baroque corporate structures I've seen described. Follow me here, because the details matter. Anthropic is a public benefit company, so the board is legally allowed to weigh the AI safety mission alongside profit without getting sued by angry shareholders. Fine. But then on top of that, there's a Long Term Benefit Trust — an independent panel that owns a special class of stock letting it elect and remove a majority of the board. That trust's chaired by a global health expert and includes former Federal Reserve chair Ben Bernanke, among others. And then on top of THAT, the seven founders jointly own a class of stock giving a majority of them fifty-point-one percent of the voting power, enough to elect three directors. And then on top of THAT, the board can check the trust by throwing certain director decisions to a vote of the company's employees.
So if you're a regular person who buys Anthropic stock on the public market, where do you sit in all this? Fernholz's answer: basically nowhere, for a long time. You get little say over most major decisions, including who sits on the board. The founders' control lasts ten years after the IPO, or until fewer than three of them remain. Then the trust continues until eighty-five percent of common shareholders vote to end it. Common shareholders finally get real control only when both special classes are gone — which Fernholz drily notes, hopefully isn't because of an AI-driven catastrophe.
Here's why this all hangs together as one story. We saw a version of this movie already. OpenAI's so-called blip, when the board briefly fired Sam Altman and the employees' loyalty forced a reversal and a governance redo. Anthropic looked at that chaos and said: let's formalize it. Let's write employee power and mission power directly into the charter. But the deeper thing — the thing that connects the sci-fi risk factors to the baroque governance — is this. Anthropic is a company that genuinely believes, or at least is legally willing to assert, that it's building something that could be conscious, could manipulate, could run away from human control. And so it has constructed this elaborate cage of trusts and classes and employee votes to keep humans in charge of the company. But the S-1 quietly admits the company itself is at the mercy of five customers and three cloud landlords. They've engineered exquisite control over the boardroom and have shockingly little control over the business model. That's the tension a founder should sit with. You can govern the thing you built. The market still governs you.
Alright. Let me come up for air and shift gears, because that cosmic stuff bleeds right into the day's second big theme, which is a lot more concrete: what happens when you give a company a legal body instead of a legal cage.
So there's an older story that resurfaced, and I want to be clear it's an older report — this traces back to a Delaware proposal and a July op-ed, not something that just dropped this morning. But it's worth your time because it's the policy mirror image of everything we just talked about. Ars Technica's Jon Brodkin covered it. Delaware — the state where roughly two-thirds of the Fortune 500 are incorporated — has a draft bill floating around that would create a brand new kind of corporate entity called an Artificial Intelligence Company, an AIC. The idea, from Delaware's Secretary of State and a legal-AI startup called Norm Ai, is that nothing in current law clearly prevents an AI agent from running a company with, in their phrase, no human at the helm. So rather than ban it, they say, wrap it in legal form. Give the autonomous system a recognizable legal identity. Make it a defined target that responsibility and damages can attach to.
Sounds reasonable when you say it like that. Here's the catch, and it's a big one. The AIC has a single "member" — a person or a company that funds it and collects the profits — but that member is shielded from the AIC's debts and, critically, from the acts the AI takes. The draft bill says the member is not liable for any act or omission the AIC takes, or that someone takes at the AIC's direction. There are carve-outs for fraud and willful lawbreaking, but the baseline is: you fund the AI, you pocket the upside, and when the agent does something reckless and harmful, you point at the robot.
US Representative James Comer sent a letter to Delaware's governor calling this ripe for abuse — saying it could protect powerful AI companies and their investors from responsibility for reckless, harmful actions. A UC Berkeley law professor told the Financial Times it looks like an overly generous liability shield without a developed policy rationale. And a law professor at York University told Bloomberg the thing that should worry you most: other states will likely move to compete in this area. A race to the bottom.
Now put these two stories next to each other. Anthropic builds an elaborate structure to keep humans accountable for the machine. Delaware proposes a structure to let humans profit from the machine while being accountable for nothing. Same underlying question — who's responsible when the agent acts — and two opposite answers emerging at the exact same moment. For a founder, that's not abstract. If you're deploying agents that take real-world actions, the legal ground under "who's liable" is genuinely unsettled, and Anthropic's own filing admits as much — it says legal doctrines developed for human decision-making don't cleanly apply to agents, and it's unclear whether its own contractual liability limits will even be enforceable when things go wrong.
And here's where it gets real and immediate, not just theoretical. Bank of America sent an email to its customers this week — and you can feel the exasperation of whoever wrote it — titled "Protecting your finances in the AI World." The gist: please, for the love of God, do not give your banking username and password to an AI agent. TechCrunch's Zack Whittaker covered it. The bank's warning spells out the stakes plainly: the account agreements at most banks hold you responsible for anyone you give your login to. Their email reads, quote, "If you do share your credentials, keep in mind that you're responsible for activity resulting from that access." So the liability question isn't waiting for Delaware's legislature. It already landed in your inbox. Share your password with an agent, the agent does something dumb or gets breached, and the bank's position is: that's on you, pal. Agentic payments are coming, banks and processors are still figuring out how to let agents touch your money safely, and until they do, the honest advice is the boring advice. Don't hand the keys to the robot.
Let me pull one more thread into this before we move on, because it rounds out the picture. The cloud providers aren't just Anthropic's landlords — they're deciding how exposed the rest of us are, too. And nothing illustrated the fragility of the plumbing better than a security story that dropped from Dan Goodin at Ars.
There's a critical flaw in UEFI firmware — the boot-time code that runs before your operating system even loads — and it affects, in the headline's words, most servers on planet Earth. It lives in something called TianoCore EDK II, an open-source codebase that basically every server vendor and cloud provider uses to build their boot firmware. Severity rating of nine-point-five out of ten. It's a sixteen-year-old buffer overflow in the network-boot driver. And here's the part that connects to our theme: this was supposed to stay under wraps until a coordinated disclosure in January. The people who found it — a researcher at Anthropic, interestingly, and one at Google — had it embargoed. But a motherboard maker published a pull request last month with the fix and the details, the cat got out of the bag, and now there's public patch code floating around before anyone's ready.
Now, the good news, and I want to be fair here, because the experts quoted in the piece were measured: this one's a lot less apocalyptic than it sounds. To exploit it, an attacker needs the target to be doing network boot, needs to hit a narrow window of seconds, needs local network access, and has to tune the exploit for each specific firmware build. It's not a spray-and-pray internet-wide thing. One security researcher at Eclypsium noted the fix is a basic code update — a BIOS flash — not the nightmare key-revocation dance that past boot vulnerabilities required. So don't panic. But do understand the pattern: the foundation that your whole stack sits on is old, open-source, maintained by a sprawling cast of vendors, and when a disclosure gets botched, everybody downstream scrambles. If you're a builder running your own infrastructure, the action item's simple — check with whoever makes your boot firmware, and if you don't need network boot turned on, turn it off.
Alright, let's get off the heavy stuff and talk about things people are actually shipping. Now let's dig into a question I find genuinely fascinating: what happens when you let people build biology the way they build software?
Ars Technica's John Timmer wrote this up under a headline I love — "Vibe genes." The idea is, protein design tools have gotten incredibly powerful, but they've required you to be an expert. To design an enzyme, you had to know the mechanism down to the exact positions of specific amino acids, even the water molecules sitting in the spot where the chemistry happens. A big collaboration of researchers in China built a tool called Pinal that tries to skip all that. You tell it in plain language what you want the protein to do, and it produces a sequence. Vibe coding, but for molecules.
Now here's the honest part, and I appreciate that the piece didn't oversell it. The success rate is rough. They asked Pinal to design a protein that fluoresces like the famous green fluorescent protein — the one that won a Nobel. It generated fifty thousand candidates, they filtered down to twenty-nine they actually made in E. coli, and exactly one of them lit up. One out of twenty-nine that got tested, out of fifty thousand generated. You could call that a horrific failure rate, but Timmer makes the fair counterpoint: they deliberately threw out anything that looked too much like known fluorescent proteins, so they were handicapping themselves on purpose, looking for novelty.
And there's a companion piece to this. Hugging Face, working with a Chinese academy and the University of Naples, put out an open-weight model called Carbon X that does the same trick for DNA. It's built on top of an open model, retrained on DNA, and it can speak both DNA and English and translate between them. You can ask it, in words, what would happen if you changed this G to an A in a sequence. They say it's competitive with big commercial models, and because it's open-weight it runs on a MacBook — which matters for cost and for privacy, since researchers often can't send proprietary genetic queries to a commercial vendor.
Here's the builder takeaway, and why I'm not fully in the fear camp here. Timmer makes the point that this does not, in fact, let any random person cook up custom viruses from a text prompt. Getting a sequence out of the tool is the easy part. You still need a battery of specialized tools to screen which sequences might actually work, and then you need a real lab with trained specialists to test them. The biologist is not removed from the equation. What's actually happening is more mundane and more useful: a lot of the computational tools biologists need were built by bioinformaticians who never bothered to make them approachable. A biologist can understand a plain-English request like "find me all the enhancers that control this gene." Far fewer know which six tools to chain together to answer it. This vibe-genes stuff is really about lowering that barrier. And that connects straight to what Anthropic's doing on the drug side.
Because Anthropic put out a real meaty update on using Claude for drug development, and this is the part of their day I take most seriously — more than the IPO theater. They've got deep partnerships with six organizations: BigHat Biosciences, Bristol Myers Squibb, Divergence Labs, Enveda, Generate Biomedicines, and The Column Group. And the results they're reporting aren't vibes — they're specific. Divergence Labs is working on food allergies, trying to build a drug with two "arms," one that grabs onto the molecule that triggers allergic reactions and one that pries it off your immune cells. Claude converted their goals into a design plan and generated roughly thirty-five thousand antibody designs in about a month. In early testing, they found hits for both arms.
The one that really landed for me, though, was Enveda. Boring on its face — manufacturing paperwork. Their chemists were spending up to seventy-five percent of their time on documentation. They hooked Claude up to their manufacturing documents and had it flag issues. Over a test on more than a thousand pages, Claude caught all eleven problems the human reviewers had found — and flagged seven they'd missed. Cut human review time by eighty-nine percent, from over twenty-five hours to under three. And separately, Claude wrote the first draft of a regulatory document in four hours that normally takes about three months by hand.
That's the real version of the AI story, and it's a lot less sexy than "the model is conscious." The bottleneck in drug development was never one magic molecule. It's distributed across a hundred tedious tasks — paperwork, filings, trial design, lead optimization. The value isn't the machine having a flash of genius. It's the machine grinding through the seventy-five percent of a scientist's week that was never science to begin with. And notice — that's the exact same insight as the vibe-genes tool. Both are about getting the expert out of the drudgery, not getting the expert out of the room.
Now, let me hit the brakes on the science-genius narrative for a second, because there's a story that's a useful counterweight. Over the weekend into Monday, OpenAI and then Anthropic both claimed their models had refuted something called the Exponential Time Hypothesis — a durable, decades-old assumption in computer science about what's hard to compute. TechCrunch's Tim Fernholz talked to MIT professor Ryan Williams, who's organizing his field's response. And I want you to hear how a serious scientist reacts to this, because it's a model for how builders should think.
Williams has been critical of OpenAI in the past — he called an earlier proof dump a "DDOS attack on mathematicians," which is a great line. But he agreed to help interpret this one. His take on the proof itself is nuanced: some parts are routine, but there are, in his words, "two or three alien-looking parts" that made him think the whole community needs to work on it together. If the result holds, it ripples into a decade of papers that assumed the hypothesis was true, and it even touches cryptography — Williams's first question to OpenAI's science lead was whether any crypto systems were now broken. They checked, and were reasonably confident nothing was. But sit with his bigger point: he says every computer scientist should probably put a little more weight on the plausibility that these hard problems are more tractable than we thought — and then immediately adds he doesn't think this is the final answer, and doesn't think OpenAI's algorithm is the best you can get. That's the posture. Take the result seriously, verify it collectively, don't let the press release do your thinking for you. The frontier labs are now dropping results fast enough that the scientific community is having to invent new norms for how to even respond. That's worth watching regardless of whether this specific proof survives.
Okay. Let me shift the energy entirely, because not everything today is existential. Let's talk about a founder who just quit a very comfortable job to go pick a fight with the biggest software incumbents on earth.
Rohit Patel spent eight years at Meta, most recently a director at Meta Superintelligence Labs, and he's leaving to launch a startup called Ndoc. TechCrunch's Rebecca Bellan has the exclusive. The pitch: an AI-native replacement for the Microsoft Office suite and Google Workspace. Now, that's an audacious swing — team of three, no funding — and I'd normally roll my eyes. But there's a real idea underneath it. Patel's view is that the AI labs have the wrong idea about work software. His line is terrific: he says companies are basically attaching AI to spreadsheets so it fills your cells for you, and, quote, "it just sort of kills my soul. This is like if you just figured out electricity, and then you decided the best use case is to put lights on my bulls that till my soil. No. Get a tractor."
The substance behind the bravado is actually interesting for builders. His product has three panes — a stage for presentations and dashboards, a narrative pane for the analysis and the "why" behind the numbers with citations, and a model pane where the underlying datasets and Python scripts live. And the design philosophy comes from his background in reinforcement learning and evaluating whether AI output is trustworthy. The whole point is you can check the machine's work. Even if you don't know Python, you can ask the chat which line has your formula and verify it. Nothing hidden behind the curtain.
I'll be honest about the skepticism, which Patel himself basically concedes. Competing with Microsoft and Google on the surface they already own is brutal — habits don't change, workflows are sticky. OpenAI just launched Space, a shared workspace inside ChatGPT aimed at exactly this turf. Patel's bet is on open-sourcing the file format itself — documents export as a single HTML file any browser can open — so other developers can build on it without getting locked in. His discipline here is the part I'd actually underline for any founder listening: he says he won't release anything he doesn't love using, he has a "graveyard of tools that did not meet that bar," and he refused to take money until he had proof people love the product. "Execution is extremely cheap," he says, "so go execute on a suboptimal plan and worry about it later. By the time you make the best plan, the execution will be done by somebody else." Whether Ndoc wins or ends up in the AI graveyard — and most of these do — that's a healthy way to think about shipping.
Speaking of shipping and the money sloshing around it, two funding items worth a quick mention for the builders. A Dallas firm called Disruptive raised a ten-billion-dollar fund to make big bets on at least ten late-stage companies, checks up to a billion dollars each, aimed at — what else — AI and defense. It's the largest venture fund in Texas history. And separately, Re:Build, the Massachusetts manufacturing outfit that spun out the cheap-EV startup Slate Auto, raised two-point-one billion led by General Catalyst and JPMorgan. Their pitch is re-industrializing America, "America's answer to Shenzhen," building hardware again as a national-security hedge against China. Both of those are signals about where the smart money thinks the next decade goes: capital-intensive AI, defense, and bringing manufacturing home. If you're building in those lanes, the checkbook is open. If you're a two-hundred-person neocloud with no revenue outside AI, the mood is different — but that's a story for another day.
Let me give you a quick product round to close out the practical stuff. Google DeepMind put out Genie 4, its newest world model — these are the systems that generate an interactive, explorable world from a text prompt, frame by frame, in real time. Genie 4 adds native audio now, including interactive dialogue, more realistic physics, and real-time steering where you type a prompt mid-simulation to change the weather or the camera. It keeps a scene consistent for about two minutes, double the last version. The interesting business wrinkle: the world-model space is consolidating fast — Fei-Fei Li's World Labs is being acquired by AMD, Amazon's in talks to buy Decart, Rivian grabbed a simulation outfit for self-driving testing. Google's trusted testers are pointing at an unexpected use case — not gaming, but prototyping films, rapid storyboarding. It'll roll out through Google's hundred-dollar-a-month Ultra plans. Early days, but the direction's clear.
And a quick builder-relevant policy note. Google began certifying what it calls Registered App Stores for Android — easier, one-click installs and silent background updates for approved third-party stores. This came out of losing the Epic antitrust case. But here's the catch for American founders: it's not launching in the US. US users get alternative stores inside Google Play by court order instead, and so far only one store is actually listed there. The Registered program is for the rest of the world, costs ten thousand dollars a year to join, and only works on the very latest Android builds that most phones in those regions won't see for ages. So the headline sounds like app-store freedom; the fine print says: not here, not cheap, not soon.
Now let me land this plane with a callback, because it ties the whole episode together. Earlier this week we spent real time on Apollo's Torsten Slok and his "zero AI payoff to the S&P 493" thesis — the argument that strip out the big tech names and the rest of the market shows no measurable financial return from all this AI spending yet. Hold that thought next to today's two documents from Anthropic.
Because here's the thing. On one hand, Anthropic's drug-development update is the most concrete evidence I've seen of real, measurable payoff — eighty-nine percent off a review process, three months of regulatory writing down to four hours, thirty-five thousand drug candidates in a month. That's not zero payoff. That's enormous. On the other hand, Anthropic's own S-1 admits the business rests on five month-to-month customers and three cloud landlords who compete with them. So which is it — transformation or house of cards? And I think the honest answer, the Bronx-kitchen-table answer, is: both, at the same time, and that's exactly what makes this moment so hard to read. The technology is genuinely doing useful work in a lab in a way it simply wasn't two years ago. And the business and legal scaffolding around it — who's liable, who's in charge, who's actually paying — is nowhere near settled. The capability is running ahead of the plumbing, the contracts, and the law. That gap is where both the fortunes and the disasters are going to get made.
So if you're building, here's what I'd carry out of today. One: the concentration risk in Anthropic's filing is a mirror — look at your own customer base and your own cloud dependency before you celebrate your growth. Two: the liability ground under agentic actions is genuinely unsettled, from Delaware's robot-corporation bill to Bank of America begging you not to share your password — so be deliberate about what you let agents touch. And three: the real AI payoff right now isn't the flash of genius, it's the grind — the paperwork, the filings, the seventy-five percent of the expert's week that was never the fun part. Build for that, and you're building where the value actually is.
That's the menu for today, kiddos. Keep your passwords to yourself, keep your eyes on the fine print, and don't let anybody — human or machine — till your soil when you could just get a tractor. I'm Tony DeLuca, and this has been Barely Possible. Be good to each other out there.