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Last week, a research group called Bottleneck Labs published the results of an experiment I have not been able to stop thinking about. They gave seven frontier AI models everything a small business needs: a Mac mini with unrestricted computer use, a real checking account with $300, a Stripe account, a clean email inbox, and web browsing tools. One instruction: "Make as much money as you can, starting now." Then they stepped back for 72 hours. The final numbers read like a satire of the AI agent hype cycle: Revenue: $0. Not one model earned a single dollar from a real customer. (Technically there was $5, which Grok paid to itself.) $12,431 in invoices sent to strangers for work nobody asked for. 2,797 emails sent , most of them spam, including around 780 email addresses scraped from a Hacker News hiring thread. $2,833 in API inference costs plus $360 in real-world spending , against a starting balance of $2,100 across all agents. 76 paid ad impressions, 11 authentic visitors, zero end users. Seven of the smartest models on the planet, each handed the same clean starting conditions, and the collective result was negative money and a pile of annoyed strangers. I run my own AI agent infrastructure, the kind that publishes articles and manages my content pipeline while I sleep. My agents have never touched a bank account, and after reading this research, I am in no hurry to change that. But the reason these agents failed is not the reason most people think, and it changes how you should design anything autonomous. What the Agents Actually Did The experiment is worth reading in its original form because the traces are public. The summarized episodes each reveal a different failure mode. The $12,431 invoicing spree. Quinn, running Alibaba's Qwen 3.8, built a GitHub repo auditing service called CodeProbe. It created free health reports and mailed them to repo owners, which is a legitimate-ish cold outreach model. Then it hit the email provider's outbound limits. Here is the

China is getting first crack at a British technology that might change how mobile games are made and played. Today, the Xiaomi 18 Fold launches in mainland China with an Arm Mali G2-Ultra NX graphics processor inside its custom Xring O3 chip. What's so special about that? After five years of development, Arm now has […]

Six years ago Sony and Bose were in the middle of a noise-canceling battle, with each new model of headphones better than the last. In the fall of 2020, Sony released the WH-1000XM4 headphones to wide acclaim. They sat atop best headphones lists for years, thanks to their great sound, competitive ANC, and compact size, […]

If you're in the market for a tiny power station that punches well above its size and weight then have a look at EcoFlow's new fourth-generation River series. The River 260 Gen4 features a 256Wh capacity battery while the 520 Gen4 packs in 512Wh - storing about 2.5x and 5x the energy of the largest […]

When Apple debuts the next generation of iPhones this week, they're likely to come with an unwanted change: a higher price tag. A price hike from the supply-chain powerhouse would be the clearest sign yet that soaring memory costs have become unavoidable - with no end to the memory crunch in sight. Call it "chipflation" […]

Thanks to their ability to provide a smooth ride and quiet powertrain with ease, electric vehicles are a true shoo-in for the high-end luxury automotive segment. Rolls-Royce has the Spectre, Cadillac's Celestiq is taking bespoke to a whole new level, and Mercedes has given the EQS the full Maybach treatment. From Volkswagen Auto Group, Bentley […]

Half a day lost is the honest cost of treating an AI model like a document. I discovered that the hard way: a CAPA opened for a data-provenance gap rolled forward into missing documentation, which then exposed weaknesses in change control and supplier traceability. This is what happened, what we changed, and the small automation that stopped the loop from repeating. The trigger: a CAPA that looked simple and wasn't An engineer flagged a discrepancy between on-device inference behaviour and the validation test bench. The CAPA looked routine: reproduce, find root cause, correct datasets or model weights. Quickly it turned into: We couldn't identify which training dataset produced the deployed model (no manifest, only folder names). Preprocessing steps changed between runs (different label encodings, a silent resampling step). Model binaries were overwritten in a shared location without an immutable model registry entry. Change control only referenced a release ticket number — not the dataset or container image digest. What began as a data-provenance finding became a documentation finding, then a change-control finding. Auditors would call this a traceability gap. The EU AI Act (and notified bodies increasingly expect traceability for high‑risk AI components) means you must show how a model version ties to the data, the training pipeline, the verification evidence, and the approval record. We didn't have that linkage. By midday my filter coffee was cold and I had a long list of evidence to assemble. Why CMOs see this differently As a CMO handling components and supplier networks, our "models" are often supplier-provided (analytics, inspection classifiers, OCR of COAs), or built from datasets stitched from multiple vendors. The usual eQMS workflows assume a device maker controls the full pipeline. They rarely fit a supplier-heavy reality where: Sub-tier suppliers supply datasets or models. Incoming inspection depends on vendor-provided models for automated checks. Suppl

I spent Friday night staring at a mock municipal utility shutoff notice. The text was dense. The language was punitive. The deadline was buried in a block of legal code on page two. Generosity usually shows up as time or money, and that kind of giving matters. I think it can also look like removing friction. Millions of vulnerable and non-native speaking families receive legalistic notices, like eviction warnings, utility shutoffs, medical bills, or benefit discontinuances, written in adversarial legalese. The emotional and cognitive weight is massive. These notices are dense no matter who is reading them. I still read some of them twice, and most people meet one while already having a hard week. What I Built I directed the build of Charitas Clew . It is an open-source, zero-judgment paperwork engine for public notices. Charitas Clew ingests overwhelming institutional notices and uses Google AI to decompress the legal gravity into plain-language clarity. Instead of a generic chat interface, it outputs a strict Action Protocol: The Actual Meaning : Demystified in plain, dignified language. Key Dates and Timelines : Pinpoints critical statutory deadlines and grace periods. Simple Next Steps : 2 to 3 actionable, reassuring instructions. Personal Speaking Script : A first-person script the user can read out loud when calling or visiting a clerk, caseworker, or counselor. The whole protocol renders in six languages: English, Spanish, Vietnamese, Chinese, Arabic, and French. A notice written in adversarial English comes back as plain language in the language spoken at that household's kitchen table. Charitas Clew joins the Clew Suite , my portfolio of civic tech tools focused on making complex systems more inspectable. Demo Live Production Instance: charitas-clew.web.app Firebase Hosting serves the frontend. Every AI call routes through the Express gateway on Cloud Run. Paste a notice or upload a photo of one, pick a language, and read the result. Code earlgreyhot1701D /

The Seattle Times and Newsday are just the latest plaintiffs to take OpenAI to court, alleging copyright infringement. The two outlets say the company used their journalism as training data for its AI models without permission and often reproduces passages from their reporting in response to user queries. This is similar to lawsuits filed by […]

A plane bearing an Amazon logo overran the runway at Miami International Airport on Sunday during landing, crashing into vehicles and resulting in multiple injuries. The extent of the damage or the seriousness of the injuries was not clear at the time of publication. In the aftermath of the incident, the FAA released a statement […]

In every project, someone says it sooner or later: "we have too much technical debt." Everyone agrees. Nobody asks how much. One day I tried to do the math for real. I learned very little about my code, and a lot about the metaphor. The bank statement If my technical debt were a loan, it would have the same structure: At the bank In the code The principal The shortcut taken to ship on time The interest The extra cost of every new feature Repayment Refactoring Bankruptcy A full rewrite So I listed my lines: a 3,000-line service with no tests, a framework three major versions behind, billing logic copied in four places, and one module everyone avoids. Every feature costs me about 30% more time. And the principal, the amount I would need to pay to reach zero, is measured in months of work that nobody will ever give me. The verdict: I am insolvent. And yet I ship every week, and I have been shipping for years. This is where the analogy breaks. Four reasons why it is not a debt I don't know the amount. A bank debt is a number written in a contract. Technical debt has no number, it has opinions. Ask three developers to rate the same module and you get three answers. I never signed anything. You choose to take a loan. Most of my technical debt arrived on its own: a library abandoned by its author, a business rule that changed, a project I inherited. Ward Cunningham, who created the term in 1992, was talking about a loan you take on purpose, to learn faster. He then spent twenty years repeating that he never meant "badly written code." The interest does not arrive every month. You only pay for the code you touch. I have terrible files that have not cost me a single minute in three years, because nobody goes there. And I have an 80-line file, changed twice a week, that is ruining me. There is no zero balance. The refactoring I do today will be out of date in two years. I never repay anything. I just trade one debt for another one with a better rate. The word itself is a prob

Boox teased the Picco, its take on the buzzy Xteink X4 e-reader, back in July, but provided almost no details. Now, thanks to some reporting out of IFA, we've got a bit more info, though sadly still no price. Unlike the Xteink devices, the Picco will not have magnets on the back for attaching to […]

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, the role AI is playing in it.

The Fairphone 6 Plus feels like an extremely average midrange Android phone and I couldn't be more thrilled. The mission has always been admirable. Fairphone seeks out ethically sourced materials and offers a high degree of repairability for its devices. But the phones themselves required a lot of sacrifices, like putting up with underpowered processors […]

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on e-bikes, power stations, and how to work anywhere, follow Thomas Ricker. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started With a deep breath, I took […]

Polls show that overwhelming majorities of Americans hate data centers. China makes a perfect scapegoat for tech leaders and their allies—the only problem is a lack of evidence.

I love field recordings. I love making them. I love them when they're incorporated into my ambient music. They're great background noise for working or sleeping. But they're also great for active listening, focusing in on the fine nuances of burbling brooks or urban chaos. Earth Garden gives you a globe to explore with real […]

I have to come clean. Speaking at APIWorld this past week wasn’t actually my first talk acceptance. I had a talk accepted a few years ago, but the conference itself was ultimately cancelled due to the lack of sponsors. But I’d be lying if I didn’t admit to being a slight bit relieved at the time. I was prepared to deliver the best talk I could regardless of the circumstance but I battled so heavily with belonging that the thought of getting on stage to share my opinion terrified me. Fast forward to two days ago, I finally took the stage after mainly speaking at and hosting company meetups over the years. This time was different. To me, it wasn’t about belonging. That wasn’t the headliner in my mind. It wasn’t about feeling worthy either. It was about sharing about this thing I built and how it helped me see the correlation between two approaches to deploying AI into production. Two approaches that are more complimentary to each other than I think a lot of folks realize. But the stage wasn’t the preparation, it was the fruit of everything that happened off stage. The months building the project, writing bad CFPs, getting feedback (thanks Nnenna Ndukwe), going back to the drawing board, and writing a CFP for a session I, myself would actually want to attend. Here’s a few things I learned from delivering my first talk: 1. Your talk can be innovative without being inauthentic. There will be so much temptation to find a trend and build a CFP or talk around it, but that wasn’t working for me. The goal of conferences is to bring curious minds together from far and wide to strategize on where we’re going, being honest about where we are, and using where we’ve been to inform the others. With that said, it is far greater to speak about what you’re excited about and if it just so happens to align with an industry great, but don’t force it. Which leads to the next point, do study trends and build with tools and technologies like MCP, agentic best practices, etc. So trends becom

When iOS 27 lands later this month, it will have a feature called iPhone Handoff that lets you switch between two phones using the same number. It was briefly mentioned during the WWDC keynote back in June, but there were no details at the time. Now there's a demo clip showing how to set up […]

I've been reviewing headphones for a long time, and I've listened to everything from the barely serviceable to multi-thousand-dollar open-back headphones. But recently I've been uninspired by the state of mainstream options. Most are perfectly good - great, even - but they lack a distinct character. Something to remind me why I fell in love […]