Core PPI is considered a leading indicator. Over the past two years it has been gradually rising and continues to be at a rate inconsistent with a 2 percent inflation target. The inflation charts of PPI, CPI, PCE are telling the same story, be concerned about inflation.

For most of history, finding out what you’re good at required the right mentor, firm or zip code. AI collapses all three into the same chat window. The constraint on mastery is no longer access. It’s finding the domain where your learning rate is steepest—and building.

AI agents moving money need real controls. Catena delivers a banking platform that enforces them across all meaningful rails, protocols, and on-chain contracts. That's how agentic payments stay safe across diverse networks, and why we're glad to support @Mastercard’s Agent Pay for Machines.

When @bpmilne writes anything more than a couple paragraphs long - read it. You'll always end up smarter than when you started. Congrats to @brale_xyz , @Visa @CantonNetwork on the launch. benmilne.com/visa-stablecoin…

RIP to a real one

Over the years @trailofbits has become a household name in cybersecurity. In this conversation with Trail of Bits' founder @dguido, we dive into the complete journey, from how they got started, to nearly going out of business during the government shutdown, to becoming a well respected pioneer in the industry. We also touch on a variety of other topics, from Lazarus Group's activities to the impact of AI on cyber from both an offensive and defensive perspective. Timestamps: (0:00) Intro (0:23) Altitude (1:14) What is Trail of Bits and how did it get started (6:49) Dan's upbringing and developing a passion for computer science and hacking (16:25) Working at the NSA (22:51) North Korean hacks on crypto (28:22) The incident response team at the Fed (33:05) The motivation behind Trail of Bits, and DARPA's role (47:23) Starting Trail of Bits (58:22) Government shutdown, losing all revenue, and surviving with 1 paycheck (1:04:36) Hiring people for mastery and building a high agency team (1:15:34) Bitcoin post-quantum cryptography (1:20:12) The culture and expectation at Trail of Bits (1:38:42) Mythos and AI's role in cybersecurity (2:05:48) San Bernardino iPhone incident, and Dan's proudest moments (2:12:12) Learning to become a CEO (2:19:59) Peiter "Mudge" Zatko

Kevin Warsh has pledged "regime change" at the fed. Normally changes at the Fed move quite slowly, but regime change is not usually gradual. For June FOMC - will there be a dot plot and SEP? Will the statement be recast? Will there be a press conference? Will minutes change?

Maybe the best VC call I've ever had was early on with @lessin. Within the first 3 minutes he told me he was out. And spent the next 30 minutes of his time walking through his framing of our thesis, why he thought it was the wrong way to look at things, and how he'd improve it.

Everyone got the economic implications of the Bitter Lesson backwards. A little awkward for the labs: the theory they cite to explain their dominance is the same one that says their core product can't be a defensible prize. The lesson gets quoted as if it were universal — enough compute, and general methods win, everywhere. The fine print is that this only works where "good" is cheap to verify. Point compute at a crisp objective with a reasonable feedback loop and it eats the task, which is why math and code fell first. And "the lesson applies here" turns out to be the same as "this domain has no margin left in it" — compute isn't scarce enough to lock anyone out (every hyperscaler will have enough of it), and open source distills the frontier on a short lag regardless. Capabilities converge, and the pressure shifts to better routing across models to minimize cost. Winning a verifiable domain comes with the curse of being commodified on the other end of the race. But here is where things get interesting, and why @satyanadella is playing one move ahead of many: the other half of the economy is less accommodating. Is this the right strategy, the right early-stage investment, the right hire, the campaign that actually captured a new trend — here "good" is expensive, contested, and slow to learn, so there's no cheap objective to point compute at, and the Bitter Lesson isn't binding. Hard-to-verify domains stay valuable for an almost embarrassing reason: nobody can tell, cheaply, what counts as a good answer. Compute will hand you a thousand of them and do a sloppy job at telling you which one is the one you should ship. Taste, judgment, curation based on experience: that's the surface where the Bitter Lesson, verification being the true bottleneck, delivers defensible businesses. Which tells you where value will accrue. The models — mostly compute, data, and increasingly tooling and architecture — are the inputs that commoditize first, so putting your moat there is choosing, deliberately, the race with no margin. The interesting move is one level down, in the sectors the lesson can't reach, and there the only durable asset is whatever defines "good" where good isn't easily verifiable: your own verification stack. The proprietary record of what actually counted as right — the "this was wrong, here's why" that has to come from outside the loop, or the model will Goodhart itself into confident, sycophantic nonsense. That moat is two things, and the second is load-bearing: the ground truth your work generates, and the expertise that keeps improving it. The trace is a record — copyable, perishable, only as current as your last correction. The expert is the renewable source: the standing ability to look at the model's next thousand answers and say which one is right, in a domain where that expertise lives in nobody else's head. Compute can't automate a signal it can't measure, and open source can't distill weights that are still inside your people. What stays scarce is the firm that can keep climbing up the intelligence value chain. @satyanadella makes the operating version of the case below — your own hill-climbing machine, with the models learning inside it. The polite way of saying: bet where there is no cheap verifier yet (and ideally there never will be), and hire and retain the experts who are the top verifiers in their domain. Which leads us back to the labs and that awkward spot. They can't defend the model — the theory they live by commoditizes it first, and competition is thorough that way — so they have to come for your eval and verification layer instead: a complement worth absorbing, the kind that arrives bundled into the platform as a thoughtful free feature, for your benefit, naturally. "the model alone is no longer the product"— Greg Brockman To succeed, the one input they have to quietly fold in is the ground truth your own operation throws off every day. Your moat was always the private traces — your failure cases, your taste, the benchmarks only your own work produces. Outsource your traces and you've outsourced judgment, which was, the job.

Welp, that happened faster than I predicted. 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. radar.cloudflare.com/traffic…

3.89M tps.

RT @rynesaxe: Thought Leadership they say. "Faster settlement" has important consequences for market structure. Eco delivers orchestrat…

The WSJ article on hyperliquid is a good overview. The world will be quickly moving to efficient low cost 24-7 trading of real world financial assets not just crypto assets. The liquidity is moving to decentralized exchanges and away from more costly centralized exchanges .

If this is done right the US merchant and consumer are going to save entire %'s of their annual spend.

Worst of all worlds: "disadvantages of centralization and most of the disadvantages of decentralization at the same time."

Lets go @bc1beat !!!

Proxy-voting is one of the largest unsolved problems of the American financial system.

Giving an agent a wallet or account isn't the hard part. Creating a robust, governed way to trust it is. Today Catena announces product access, OCC news, and Series A investment from wonderful partners: catena.com/blog/banking-gove…

"I think [AI agents] may be the only actors that we trust with our assets and the only actors that are capable of generating meaningful return on our assets." @psneville, cofounder of @catena_labs, makes the case for an agent-native internet built on stablecoin rails: "Once we have stablecoins and we have the ability to represent dollars on internet rails, what kind of new opportunities does that unlock? At the same time we were contemplating that, you could see clearly the web, the internet itself, is going agent-native." "As AI actors become economic participants, they will ultimately be the primary dominant economic participants in the world for all kinds of activities, payments, and otherwise." "Flash forward in a few years, I think they may be the only actors that we trust with our assets and the only actors that are capable of generating meaningful return on our assets."