The San Francisco maker of wool sneakers, once valued at more than $4 billion, sold its brand and intellectual property last month to brand management company American Exchange Group for $39 million. The stock had fallen more than 99 percent since its 2021 Nasdaq IPO. And then it decided to pivot to AI compute infrastructure, with a new name: NewBird AI.

An unknown investor did a $50 million convertible. Every filing and press release describes the counterparty only as “an institutional investor.” Maybe we will find it in the proxy statement, due before the May 18 shareholder vote. The press release itself reads like the white papers accompanying the turds called Initial Coin Offerings. You forgot about them, did you? (Good friend Brian reminded me of the rebrand of Long Island Iced Tea Corp to Long Blockchain Corp.)

The stock surged more than 700 percent in a single session. (At one point shares touched $21.76, giving the soon-to-be shell a market cap north of $180 million.) I am sure people are profiting off this instant hit, but in the end a lot of people are going to suffer. Pigs, as they say, will get slaughtered. I appear cynical because I have seen this movie before.

If you added dot-com to anything in 1999, even the penny stocks, it would pop. And I watched it happen. I covered it. And now I am watching it happen again, with a different two-letter suffix doing the same speculative work. It was thanks to a lot of what went down on message boards like Raging Bull and Yahoo. These days it is way more instant (and easier) thanks to social media. Everyone is a pundit on X.

In A Random Walk Down Wall Street, Burton Malkiel pointed out the “tronics boom.” From 1959 to 1962, companies plastered some garbled version of the word “electronics” into their names, regardless of what they actually made. Here are some names from that era: Astron, Dutron, Transitron, Powertron Ultrasonics. The products were almost beside the point. The suffix was the investment thesis.

The key observation Malkiel made then still holds now. Buyers of these issues did not really care what the companies made, so long as the name signaled adjacency to the future. That is the rhyme with today. “AI” is doing exactly the same work that “tronics” and “.com” did before it. Suffix is a signal. It attracts capital. Nothing else matters. Day trading, momentum trading, gambling. It is just all the same.

The pattern is not new and it is barely even interesting anymore. Why does it keep working? What does it tell us about how markets behave when fear of missing out overwhelms judgment? What does it say about the pervasiveness of the gambling instinct in a society that is even more economically stratified? Does it remind you of the 1920s?

Somedays I wake up and wonder if we are all being trolled.

You do not have to build something transformative at first. You only have to position yourself close enough to the narrative. Add “AI” to the pitch deck, sprinkle in “agents” and “GPU-as-a-Service” for polite company, and suddenly you are not a failed shoe company liquidating its assets. You are selling adjacency to the future.

These jokers started out selling shoes made out of wool. They could not turn that into a profitable business. And they think they can compete with hyperscalers who have more money than god these days? A shoddy shoemaker that has proven to be a disaster as stewards of investor dollars thinks it can make a go of it with a new name and a new game.

I know what I would call this game. For now, it is suffice to say, NewBird AI is more like Loony.AI.

PREVIOUSLY

April 15, 2026. San Francisco

And that left me with a question. Why are we able to absorb big technology failures when they are blamed on people, but respond so differently when the failures come from machines?

I mean, we fret a lot about AI error. There is a whole cottage industry around AI errors. Debates rage about the risks of machines making decisions, writing code, and running autonomously. No one can deny the gravity of the issues and their wide-ranging consequences. Certainly not me. That said, the above-mentioned three events are a reminder that more than AI, it is human error that is a clear and present danger.

Every major outage you’ve read about, and most of the ones you haven’t because they were quietly patched before anyone noticed, traces back to a human decision, a human configuration, a human credential, a human lapse. The 2020 SolarWinds attack that compromised nine federal agencies and hundreds of companies? A human left a password exposed. The 2021 Facebook outage that took down Instagram, WhatsApp, and Messenger for six hours? A human ran a command that should have been reviewed first. The Colonial Pipeline ransomware attack? An old VPN account with no multi-factor authentication, used by a human who had left the company. T-Mobile failures? Amazon Web Services outages? All trace back to humans, fat fingers and all.

The software infrastructure that runs the modern world is riddled with human error. It always has been. The software, despite what they tell you, is not trustworthy or reliable. It fails regularly. And because of that, we as an industry and society have crafted “stories” and “narratives” around it. More like fables, but that’s for another day.

There is a language of failure that explains it all. We have crafted failure theater. The post-mortem. The incident report. The five-nines SLA. The security audit. The penetration test. These are all stories built around the known shape of human failure. And when we hear these, there is a comfortable, easy-to-understand explanation for the human error. To err is human, and all that jazz.

We humans know how humans fail. Fat fingers, credential theft, misconfiguration, copy-paste mistakes, exhausted engineers making decisions at 2 a.m., organizational pressure overriding security judgment. Whatever the reasons, there is a human playbook to decode the screw-ups.

Maybe because of that, we are okay accepting (if not forgiving) the mess that is left behind. This includes all the long-term damage that happens to us when dealing with security breaches, like stolen Social Security and personal details. Or maybe there is someone who will be sued. We are somehow okay getting a $25 settlement from a credit bureau screwing up our personal data.

And yet we are not willing to think similarly about AI error, and are in a tizzy over its impending problems. Why? I was asking myself that question when reading about the three stories mentioned above.

There is research that explains why. Psychologists call it the perfection scheme, the implicit expectation that machines, unlike humans, should perform consistently and without fault. A 2022 study in the Journal of Computer-Mediated Communication found that “people were increasingly sensitive to the violation of the perfection scheme when the agent was AI,” meaning when a machine fails, the broken expectation hits harder than an equivalent human failure, and people make more punitive judgments as a result.

Related research published in the Journal of Retailing and Consumer Services found that “customers have more negative responses for a self-service technology failure than for an employee failure” because “they get angrier with machines’ mistakes than with those of humans,” and crucially, that empathy, which softens anger at human failure, has no equivalent effect when the failure is a machine’s. You can forgive a tired engineer. You cannot extend empathy to a misconfigured content management system.

Deeper still is what psychologists call the moral responsibility gap. Blame, in the way humans practice it, requires intention. A study on human-algorithm interaction found that “blame and forgiveness apply more to humans than to machines” because machines “are not agentic entities, they are less in control, less responsible, and lack intentionality.” When a machine fails, the psychological apparatus of blame and forgiveness cannot engage cleanly.

The safety framework we’ve built around human error, what the psychologist James Reason called the Swiss cheese model, assumes errors have human authors who can be understood, retrained, and held accountable. Defensive layers work because we know the shape of the holes. That framework doesn’t map onto systems that have no intention and no accountability in any form we recognize. So we oscillate between over-trusting AI when it works and rejecting it outright when it fails, and neither response is the calibrated tolerance we’ve spent decades building for human fallibility.

Is it also because any error on the AI front will be systematic? When a human makes a mistake, it’s usually localized. When a model learns something wrong, it can replicate that error across every codebase it touches, consistently, at scale. And we don’t yet have the accumulated knowledge for AI-generated failure that we have for human failure. Unless we see the failures and have incident reports, we don’t know how to react. Even the models wouldn’t know how to react. The aviation safety culture that made commercial flight extraordinarily reliable required decades of documented accidents to build. We are at year two of deploying AI code at industrial scale.

That is a very rational response to a very unknown thing. Humans have always feared what they cannot explain. For the Greeks, lightning was hurled by Zeus. For the Vikings, Thor’s hammer striking an anvil. Once Benjamin Franklin proved it was electricity, the fear didn’t immediately disappear. It transferred. When electricity was installed in the White House in 1891, President Benjamin Harrison and his wife refused to touch the light switches for fear of being electrocuted. The staff turned them on and off. Sometimes the lights burned all night because no one wanted to touch the switch.

Of course, none of these carried the existential weight that AI-made and AI-controlled software brings with it. And it’s not as if we are safe from the vagaries of human-scale software production — as past few days proved that well enough. We know how to live with what we understand, even when it hurts us. We do not yet know how to live with the unknown.

But as the old ad goes, perception is reality.

April 14, 2026. San Francisco

Here are a few articles that I did read, and thought were worth sharing. Hope you get to enjoy them this weekend.

The Biggest Scandal in Chess — Vanity Fair

I am looking forward to Ben Mezrich’s new book, Checkmate. Vanity Fair’s excerpt about the time 19-year-old Hans Niemann beats Magnus Carlsen in 2022 has me waiting in anticipation. Chess is brutal. (There is a documentary on Magnus on TubiTV and PlutoTV, if you want to watch something.)

Leave Big Tech Behind — The Guardian

It is the Guardian, so no wonder it is a little preachy, but the information is useful. The Guardian walks through real alternatives to Google, Amazon, Meta, X, and Apple. We like the idea of alternatives, but acting on it is another question. (Read my essay on how we are addicted to the convenience of technology.)

Jimi Hendrix Was a Systems Engineer — IEEE Spectrum

Edge-computing architect Rohan Puranik ran circuit simulations on every pedal in Hendrix’s signal chain and concludes that he was a systems engineer who iterated fast on a modular analog signal chain. This is the best thing I’ve read in weeks.

“Anything Good Is Kind of Costly” — Dezeen

Marc Newson argues in this Dezeen interview that “Anything good is kind of costly.” I would argue that is usually true, but not always.

When You Don’t Die — Ottawa Citizen

A quiet, devastating piece about people who outlive a terminal prognosis. What happens when you’ve given away your art collection, said your goodbyes, rearranged your entire inner life around dying, and then you don’t?

Iran’s Revolutionary Guard on Social Media — New York Magazine

A Clemson University study identified 62 fake accounts across X, Instagram, and Bluesky linked to Iran’s Islamic Revolutionary Guard Corps. Within 24 hours of the strikes beginning, they all pivoted to pro-Tehran war propaganda. The coordinated flip happened across platforms at once. Some of the accounts were broadcasting AI-generated videos of Trump and Netanyahu as LEGO minifigures. NY Mag’s inside account of the operation connects to the Time piece below, and the two read well together.

The New Age of AI Propaganda — Time

Renée DiResta’s essay in Time is the most important piece I read this week. Her core argument: modern propaganda is no longer about persuasion. It is about virality. The goal is not to change your mind but to dominate your attention. This should remind you of my recent essay about the velocity of information. Mine was less moralistic.

From CrazyStupidTech

Behold, an AI Startup with a Real Business

Fred wrote about Audioshake, a company that uses AI to split any song into its component stems. They have real revenue, real customers, real use cases. The quote I keep thinking about: “If this whole AGI thing is going to happen, machines need to be able to see. But they also need to be able to hear.”

ICYMI

Sam Always Wins
I noticed CFO Sarah Friar and AGI chief Fidji Simo were doing the press rounds last week. Not Sam. Which was strange, because Sam loves the spotlight. So I asked: was something going on? Turns out, yes.

Upstream Speeds, Needs Up Again
A quick follow-up to my earlier piece on upload speeds. New OpenVault data shows fiber upstream crossed 100 GB for the first time in Q1 2026, reaching 106.7 GB. DOCSIS subscribers on the same systems used 56.9 GB, an 87% gap, up from 66% just a quarter ago. The upstream nation thesis is holding.

Banksy, Satoshi & The Unmasking Impulse

I wrote this after Reuters unmasked Banksy and the Times named Adam Back as Satoshi. Both investigations bother me for the same reason: they confuse exposure with accountability. Banksy’s anonymity is the art. Satoshi’s anonymity is the architecture.

April 12, 2026. San Francisco