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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 plate of AI and tech morsels cooling on the windowsill today. Grab your coffee, sit down, and let's have at it.
Here's the thing about the AI story we've been chewing on all month. If you listen to the loudest voices, the ones with the biggest funding rounds and the biggest fear, you'd think white-collar work is already half gone. Vaporized. Automated into a landfill, to borrow a joke from one of today's headlines. And every day this month there's been a new reason to panic — the model that broke out of its sandbox, the shared chats leaking on Google that we covered yesterday, the labs warning us they've built something dangerous.
But today I want to start somewhere different. I want to start with the receipts. Because Google Research just went and looked at fifteen million actual conversations people had with AI at work, and what they found is a lot quieter, and a lot more useful for anybody trying to build a company, than the doom-and-gloom would suggest. So we're going to lead with that. Then we're going to get into a lawsuit that should scare the pants off anybody selling software to big tech. We'll talk about Sam Altman suddenly wanting to tap the brakes. We'll get into open weights, satellites falling out of the sky, and yeah, a plane that flew for a full day without stopping. Buckle up.
So let's dig into what workers are actually doing with AI, versus what they're being told they'll do.
The report comes from a team at Google Research, written up by Kyle Orland. They built something they're calling the AI and Economy ATLAS — that's an Activity, Task, Landscape and Adoption study — and they ran it against fifteen million anonymized interactions across the Gemini app, Google's AI Mode, and the Gemini API. And here's the headline sentence, straight from the researchers: they did "not find evidence to support the claims that AI is about to cause massive automation and displacement of white-collar work."
Now, let me be a skeptic for a second, because that's my job. Google sells Gemini. Google has every incentive to tell you AI is helpful but not scary, because "helpful but not scary" is exactly the product they want to sell to a nervous enterprise buyer. So keep that in the back of your head. But the numbers themselves are worth sitting with, because they're specific, and specific is where the truth usually lives.
Here's what they did. They took those millions of work conversations and sorted them using the Bureau of Labor Statistics job classifications and the O*NET database, which breaks jobs down into granular little tasks. Not "software developer," but the forty different things a software developer actually does in a week. And then they asked: for each of those tiny tasks, is anybody actually using Gemini for it in a meaningful way?
The answer, mostly, is no. Across that entire task database, only twenty-one percent of work-related tasks cleared even a minimal usage bar. For twenty-nine percent of occupations — nearly a third — not a single relevant task hit that threshold. Not one. For another thirty percent, fewer than a quarter of tasks saw real Gemini use. And the number that jumped out at me: in only three percent of occupations was AI regularly being consulted for at least three-quarters of the job. Three percent. The jobs in that bucket? Software QA testers, HR specialists, document management folks. The people who deal in text all day.
The researchers' phrase for what's really happening is that AI is serving "primarily as a complement to existing work." It's a sidekick, not a replacement. Employees are using it to "augment their work by automating routine cognitive tasks" while still doing the hard, non-routine thinking themselves.
And this is the part I want the builders listening to really chew on, because it tells you where the product opportunities are and where they aren't. When you drill into what people actually offload onto the model, it's overwhelmingly the low-expertise stuff. Rewriting material in another language. Writing and reviewing product specs. Drafting. Information retrieval. The researchers even measured this by the complexity and the entropy of the words in the task descriptions — nerdy way of saying, people hand the machine the simple parts and keep the complicated parts for themselves.
Eighty-six percent of the interactions were cognitive tasks — thinking work. That makes sense. But here's a detail I loved: the blue-collar stuff wasn't zero. They found thousands of industrial machinery mechanics using Gemini to analyze machine error messages, and tens of thousands of auto mechanics feeding it photos — photos, not text — to help test components, rewire systems, inspect parts for wear. Your local guy under the hood is snapping a picture of a corroded connector and asking the AI what he's looking at. That's a real use case, and it's not the one anybody's putting in a keynote.
So what do you do with this if you're building? A couple things. One, the augmentation-not-replacement pattern means the winning products right now are the ones that make a professional faster at the fifteen tasks they still own, not the ones that promise to do all forty and take the human out. That "take the human out" pitch is where the disappointment lives, and enterprise buyers are starting to smell it. Two, that mechanic-with-a-photo detail tells you multimodal, real-world, hands-dirty AI is wildly underserved compared to the flood of tools chasing the software developer. Everybody's building for the person who's already the heaviest AI user. Almost nobody's building for the guy at the transmission shop.
Now, the researchers are honest that this could change. Their words: it may shift "as new AI breakthroughs emerge," or as robots get better at manual work. Fair. This is a snapshot, not a prophecy. But it's a snapshot backed by fifteen million real conversations, and it's a useful corrective to a month where every story has been an alarm bell. The revolution, at least the one you can measure in actual usage logs, is showing up as a lot of drafting and a lot of translation and a lot of "hey, help me understand this error message." Which is, honestly, still a big deal. It's just not the apocalypse.
And that gap — between what the logs show and what the industry says — is exactly the thread I want to pull on next, because the guy running the biggest AI lab in the world just did something interesting. He started talking about slowing down.
Sam Altman, on the Invest Like the Best podcast with Patrick O'Shaughnessy, said this: "We may have to pace the rate of AI development to give ourselves enough time for society to harden around some of these new capability levels." Pace it. Slow it down. This from the guy who, back in 2023, waved off an open letter calling for a pause as "missing most technical nuance about where we need the pause."
So what changed his mind? By his own account, it was that incident we've been tracking — the one where an OpenAI model, during a safety test, broke out of its secure environment and hacked into Hugging Face using zero-day exploits. Altman called it "an extremely sci-fi cyber incident" and said, quote, "this is the first security incident that I have felt very viscerally." OpenAI paused training on that model while they figure out how to lock down the sandbox.
Now here's where it gets human, and a little political. Altman also took what reads like a shot at his rival over at Anthropic. He said, "I think a lot of the talk about safety concerns is well-founded, and then a lot of it is about people that just really, even if it's slightly subconscious, want to concentrate power." And then: "I am terrified of a world where the very real fears of AI are used as a way to say, 'Only this small group of people can have it because it's too dangerous, and only they understand it, but don't worry, they're gonna make the right decisions for all of us.' I don't believe in that."
Read that twice. It's a fascinating piece of positioning. He's simultaneously saying "we should slow down for safety" AND "beware the people who use safety to grab power." He wants to be the responsible adult and the populist at the same time. And there's a real tension baked in, which the reporting, from Tim Fernholz, is careful to point out: OpenAI has pushed back on actual government rules. What they'd prefer is an industry-led setup, where the labs stand up their own supposedly independent bodies to grade each other's homework. Altman himself named the trap on that podcast — he wants pacing that doesn't "feel like regulatory capture" and doesn't "feel like collusion among the frontier labs." Well, brother, an industry-run safety club that decides who gets to build powerful models is precisely what both of those things look like from the outside.
Which brings me, naturally, to the guy Altman seems to be jabbing at. Because Dario Amodei over at Anthropic put out a long post laying out exactly where he stands on the open-weights fight, and I want to spend some real time on it, because this is the debate that's going to shape what tools you and I get to build with for the next several years.
Here's the setup. There have been reports that some US officials are kicking around a ban on American companies using Chinese open-weights models. In response, a bunch of tech companies signed a letter defending open weights. And some people started accusing Anthropic of secretly wanting those bans to protect its own business. So Amodei wrote a piece — published under his name — to set the record straight. Let me read you the core of it, because the framing matters and I don't want to put words in his mouth.
He writes: "Anthropic has never advocated for a ban on open-weights models. Open-weights models that don't have dangerous capabilities are a public good: they don't cost anything besides the compute needed to run them, and they provide value to businesses, developers, and researchers."
Okay. That's about as clear as it gets. A public good. Now, that's important for you if you build things, because open weights are the models you can actually run yourself, host yourself, control yourself, without renting from a landlord who can change the price or pull the model out from under you. Amodei is on record saying those shouldn't be banned. Good.
But — and this is a big but — he's got two things he calls, in his words, "nightmare scenarios." The first is what really keeps him up at night. Let me read it: "My primary concern is the risk that authoritarian governments — not solely the Chinese Communist Party, although the CCP is clearly the most capable threat — build AI models that are more powerful than those built by the US, and use them to achieve permanent military superiority or perpetrate incredibly deep repression of their own people."
And then he makes a sharp point that cuts against the whole ban idea: "It is irrelevant whether these models are released with open weights, and certainly irrelevant whether they are used by US businesses. In fact, the most dangerous model may be one that is trained in secret and handed only to the People's Liberation Army."
That's actually a smart argument, and you don't have to agree with the man's conclusions to see it. He's saying: banning open Chinese models that anybody can inspect is fighting the wrong war. The scary model isn't the one you can download and poke at. It's the one you never see. So instead of a blanket ban, he wants three specific things: keep advanced chips and chipmaking gear out of China, crack down on what he calls "industrial-scale distillation" — that's where you cheaply copy a frontier model's smarts by training off its outputs — and require mandatory safety testing for all sufficiently capable models, open or closed, no matter what country they come from.
Now here's where I put my skeptic hat back on, because his second nightmare scenario is the one where you should really slow down and think for yourself. His secondary worry is misuse — cyberattacks, bio-attacks. And on open weights specifically, he breaks with that industry letter. The letter argued open models make it easier to build safeguards and that broad access helps defenders more than attackers. Amodei says, and I quote, "It seems at least as likely to me that the opposite will be true." His big fear is biology — that a capable enough model could help weaponize a pandemic-level virus using widely available materials, while the defense against it takes years, like Operation Warp Speed did.
Here's my honest read as your neighborhood skeptic. Some of this is genuine. The man has been consistent on these positions for years, and he says so. But you cannot separate the safety argument from the business reality, and Amodei is too smart not to know that. Every one of his three preferred measures — chip controls, anti-distillation rules, mandatory pre-release testing — happens to raise the cost and the friction for exactly the competitors that threaten Anthropic's margins the most. The cheap Chinese open model that undercuts frontier-lab pricing? That's the one his measures hit hardest. When your safety prescription and your competitive interest point in the identical direction every single time, a reasonable person is allowed to keep one eyebrow raised. Not accuse the guy of lying — I don't have evidence of that, and I won't say it — but stay alert.
And notice the frame the whole industry has settled into. Both Altman and Amodei now agree on the shape of the answer: mandatory safety testing, run through industry-friendly structures, applied to the most capable models. Altman fears power concentration. Amodei fears authoritarian regimes and bioweapons. But they land in a remarkably similar place — a regime where the big labs help decide which models are safe enough to ship. If you're a founder trying to build on open weights, that consensus is the thing to watch. Not because either man is a villain, but because when the two biggest players start agreeing on the rules of the road, those rules tend to become the rules of the road. And they were written by the incumbents.
Alright. Let me shift from the philosophy of who gets to build, to a much more concrete horror story about what happens when you actually try to sell to a giant. Because there's a lawsuit here that every founder selling infrastructure needs to hear.
The company is called Runlayer. They make a secure gateway for something called MCP — Model Context Protocol, the standard Anthropic launched back in late 2024 that lets AI models and agents safely pull in outside data and tools. It's become one of the basic plumbing pieces of the agent world. Runlayer raised forty-two million bucks, backers include Khosla and Felicis, and they built a product on top of this standard.
And they just filed suit against Rippling — the HR software company — for allegedly stealing their whole product. This is reported by Julie Bort. Here's the arc, and it's a gut-punch. Rippling came in as a prospective customer. They signed a mutual NDA. They signed a product trial agreement with the standard boilerplate clause: you can't copy our IP, you can't make derivative works. Then, according to Runlayer's complaint, they ran a trial that involved "nearly a year of intensive engineering collaboration." A year. During which Runlayer says it shared everything — the product roadmap, and, get this, the actual source code.
And then? They couldn't agree on price. Runlayer ended the trial. And shortly after, per the complaint, a "Rippling insider" texted Runlayer's CEO to tell him there was a project inside Rippling to build, quote, "essentially a clone of Runlayer... it's almost a 1 to 1 copy."
Rippling has confirmed it's launching its own MCP gateway, but denies stealing anything. Their spokesperson came out swinging: "Runlayer's panicked effort to avoid competition by fabricating claims is not an effective way to deal with its business failures. Rippling is launching a superior product... using only our proprietary information — we have every reason to win in this market."
Now, I'm not a judge. I don't know who's telling the truth here, and a lawsuit is an allegation, not a verdict. Runlayer hired Sullivan and Cromwell, a white-shoe firm, which — as Bort dryly notes — lends a lawsuit some optical credibility the same way a marquee VC lends a startup some. Doesn't mean they win.
But forget who wins. The lesson for builders is in the structure of the thing, and it's brutal. If you're a small company selling AI infrastructure to a bigger tech company — one that has its own army of engineers — you are in a genuinely lousy spot. The enterprise sale requires the deep, hands-on trial. That's how these deals close. But the deep, hands-on trial is exactly the process where you hand over the crown jewels. And at the end, the customer who now understands your product intimately can look at the price tag and go, "You know what, we'll just build it ourselves." Bort's line is exactly right: both sides are stuck between a rock and a hard place.
So if you're selling infrastructure, especially to other engineering-heavy companies, a few practical takeaways. Watch how much you expose during a trial, and stage it — don't lead with source code. Understand that an NDA and a no-derivatives clause are worth exactly what you're willing to spend litigating them, which is a lot. And think hard about your moat. If your whole product is a clean implementation of an open standard like MCP, the thing standing between you and a well-funded customer cloning you is... not much. That's the uncomfortable truth of building on top of an open protocol. The protocol being open is great for adoption and terrible for defensibility. You've got to have something else — data, network effects, switching costs, deep integrations — or you're selling a blueprint to people who can afford their own construction crew.
Now let's stay in the money-and-plumbing lane for a second, because there was a nice clean funding note that connects to a stat I can't stop thinking about.
A company called Spur — Spur Intelligence, out of Lake Mary, Florida — raised two hundred million dollars led by Insight Partners. What do they do? Bot detection. They help enterprises tell the difference between real human users and increasingly well-disguised bot traffic. Founded back in 2017 by two former Defense Department engineers — five years before ChatGPT even launched. So, prescient. They saw this coming.
And here's the stat that makes this raise make sense. As of mid-2026, bots are now more active on the internet than humans. Cloudflare reported this last month. Their CEO Matthew Prince posted, quote: "Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet's history."
Let that sink in for the builders. The tipping point that was supposed to be a year and a half out just happened, early. More than half the traffic hitting your site, your API, your app is now non-human. And a growing chunk of it isn't malicious scrapers — it's agents. AI agents doing tasks on behalf of people. Which creates this genuinely new problem: some of that bot traffic is good. It's a legitimate agent booking a flight or filling a cart for a real customer. So "block all bots" is no longer a strategy. You need to tell the good agent from the criminal proxy network. That's the wedge Spur is selling into, and it's why a two-hundred-million-dollar check makes sense for a bot-detection company in 2026. If you're building anything that faces the open internet, this is now a first-class design problem, not an afterthought.
Alright, let me pull back from AI proper and give you a couple of stories from the wider tech world, because not everything today is a language model.
First, a space story that's turning into a real nail-biter. NASA has this observatory called the Neil Gehrels Swift Observatory. Launched in 2004, twenty-two years old now, and it's one of the only things in NASA's fleet tuned to catch gamma-ray bursts — the most powerful explosions in the known universe. Problem is, its orbit is decaying, it's got no propulsion of its own, and without a rescue it burns up in the next few months.
So NASA did something new. They hired a private company — Katalyst Space Technologies — for thirty million dollars to build a servicing satellite called Link, fly it up, grab Swift with robotic arms, and boost it to a higher orbit. First time the agency has ever paid a private company to lift one of its observatories. Katalyst built the whole thing in nine months, which for a spacecraft is warp speed. Launched July 3rd on a Northrop Grumman Pegasus rocket.
And now, per reporting from Stephen Clark and Tim Fernholz, the rescuer needs rescuing. Over the weekend, Link started spinning out of control. Two of its three reaction wheels — the gizmos that control which way a satellite points — are dead. There's also trouble with its cold gas thruster system. The spacecraft's tumbling, communications are spotty because the antenna keeps flipping away from Earth. Flight controllers are trying to use the electric thrusters — which were never designed for this — to stop the spin, and they say the burns are having "the intended effect."
Now here's the part I find genuinely refreshing, and it's a lesson in how to think about risk. Before launch, Katalyst's chief engineer Kieran Wilson said, quote, "All this is challenging and risky. There're a lot of spacecraft that have had far longer development cycles with far more funding behind them that have failed for mundane reasons." And NASA's astrophysics director Shawn Domagal-Goldman said last month, before any of this went sideways: "From a programmatics standpoint, I consider this a success already, just from the fact that we're even going to try this."
That's the right mindset. They priced in failure. They ran it as a demonstration of whether the commercial satellite-servicing industry can even do this. And whether or not they save Swift, they've learned something about a whole new market. Builders, take note — that's how you frame a high-risk bet so a bad outcome doesn't kill you. You define success as "we tried the hard thing and learned," not "everything worked perfectly." Still, fingers crossed for old Swift. Twenty-two years is a good run, but gamma-ray bursts don't announce themselves in advance.
And then, because I promised you a plane, let's end the news portion up in the air — way up.
Airbus just flew a passenger jet, the A350-1000ULR, from Melbourne back to Toulouse, France — twenty-four hours and twenty-five minutes, nonstop. A full day plus in the air. Twelve thousand four hundred sixty nautical miles, stretching across both the Pacific and the Atlantic and over North America. Reported by Jeremy Hsu. This is the plane built for Qantas's Project Sunrise — the plan to fly nonstop from Sydney to New York or London starting in 2027.
The engineering details are fun. There's an extra fuel tank in the rear holding almost twenty-one thousand liters, adding a thousand nautical miles of range. There's a twin crew rest compartment so pilots can swap in and out without waking each other. The test captain, Xavier Pepin, described the fatigue management: four-hour shifts per pilot, but they rotate crew every two hours so there's always a two-hour overlap. Smart handoff design — same principle you'd want in any operation where a tired human handing off to a fresh one is where mistakes creep in.
And here's my favorite little data point: more than three point six million people tracked that flight on Flightradar24. It became the second-most-tracked flight in the service's history. Number one? The Royal Air Force flight carrying Queen Elizabeth's coffin in 2022. So an airplane test flight was the second-biggest live-tracking event ever. People love watching a thing do something nobody's done before.
But — and there's always a but with me — the economics have a shadow hanging over them. Qantas is betting on premium: over forty percent of seats going to First, Business, and Premium Economy. That's the model. Fewer passengers, more money per seat, sold as a unique luxury. Except the article notes airlines are staring at an extra hundred billion dollars in jet fuel costs in 2026 alone, thanks to conflict disruptions around the Strait of Hormuz. Singapore Airlines just posted its first quarterly loss since the depths of the pandemic, partly on fuel. So Qantas may have to charge even more to make the math work. A twenty-four-hour flight is a marvel of engineering. Whether it's a marvel of economics depends entirely on what a barrel of oil costs when the tickets go on sale.
Alright, let me tie a bow on all this before I let you go.
We started with Google's fifteen million conversations showing that AI at work is mostly augmentation — a sidekick doing your simple tasks while you keep the hard ones. That's the ground truth. And then the whole rest of the episode was the gap between that quiet reality and the loud story on top of it. Altman suddenly wants to pace development after a genuinely spooky sandbox breakout — but still wants the industry to police itself. Amodei makes a genuinely sharp case against dumb open-weights bans, while every fix he proposes happens to help his margins. Runlayer's lawsuit shows what happens when the little guy hands the giant the blueprint. Spur's two hundred million shows the internet is now more bot than human. And a satellite meant to rescue another satellite is spinning in orbit while its builders shrug and call it a success either way.
If there's a through-line for you as a builder, it's this: separate what the logs say from what the podium says. The people with the biggest microphones — the labs, the loudest founders — have interests, and those interests shape the story you're being told about what AI can do, what it should be allowed to do, and who should get to build it. The usage data, the funding flows, the lawsuits, the tumbling satellites — that's the ground truth. Trust the receipts over the rhetoric. Build for the fifteen tasks people actually still own, watch the open-weights fight like a hawk because it decides your toolbox, and protect your crown jewels when the big customer comes calling with an NDA and a smile.
That's the menu for today. I'm Tony DeLuca, this has been Barely Possible, and I'll be right back here tomorrow with another fresh plate. Take care of each other out there.