Good day . Washington has a message for AI executives, and it is not “thank you.” Also today: TechCrunch’s graveyard of dead AI products, banks finding AI agents inside customer accounts, a vote test that split Grok and GPT, and Arize’s pitch for what an AI engineer does now.
TechCrunch Opens an AI Graveyard TechCrunch
What happened: TechCrunch published a running list of AI products and startups that shut down, pivoted or missed badly. OpenAI has several entries: Sora, shut down in March 2026; ChatGPT Atlas, the stand-alone browser, discontinued August 9 after less than a year; Operator, folded into ChatGPT; and a July 9 “super app” redesign with separate Chat, Codex and Work modes that was rolled back soon after users found it confusing.
Why it matters: TechCrunch cites S&P Global Market Intelligence estimating that about 42% of AI initiatives are ultimately abandoned by their corporate parents, for reasons from funding and scaling to weak demand. The startup casualties share a pattern: Relay, a five-year-old Zapier alternative, closed in August once OpenAI, Google and others built similar automation into their own tools, and Notion Mail is closing because users were handing their inboxes to separate AI agents instead.
What everyone’s saying: TechCrunch’s own framing is that the list is “less a scorecard of flops than a record of what these attempts can (hopefully) teach the rest of the industry.” It also notes that OpenAI folding stand-alone apps into ChatGPT has “worked out better than the redesign did.” Character.ai’s bankruptcy filing, five years after launch, sits near the top.
My read between the lines: Read the list for what died and who killed it. The pattern is features that were never products: Relay, Huxe, Notion Mail all lost to a bigger platform adding the same thing for free. Even OpenAI’s own deaths are mostly relocations, with Atlas and Operator moving house into ChatGPT. In this graveyard, a lot of the headstones mark a merger.
📖 Further reading: Thanks to Apple, Your Favorite AI Tool Is a Dead Tool Walking — my June piece on Apple renting Google’s Gemini for the new Siri after the $250 million settlement over the Siri that didn’t ship, the same Siri entry on this list, seen from the platform’s side.
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Washington to AI Executives: Please Stop Talking Axios
What happened: Axios reports that lawmakers, Hill staffers and Washington insiders in both parties wish AI founders would do what other industry chiefs do and just build their products, safely. They say the message from AI executives too often swings between prosperity and doom, sometimes in the same appearance, feeding a strong public backlash. Sen. John Curtis (R-Utah) said the industry is “tripping over their own shoelaces in trying to sell the value of this to the public.”
Why it matters: Administration officials are again frustrated with Anthropic. Axios says Dario Amodei attended a late-September gathering with President Trump without mentioning safety incidents in which Anthropic models affected government websites; the company told officials on Thursday, October 8, then published a Friday-evening blog post. “They were hiding the ball,” one official said. Axios also says both Anthropic and OpenAI are targets of a developing FTC investigation.
What everyone’s saying: Rep. Ro Khanna (D-Calif.) said of OpenAI’s Sam Altman, “You cannot trust billionaires to make decisions to keep us safe,” and Sen. Chris Murphy (D-Conn.) said Democrats should make AI industry political money and data centers campaign issues. Sen. Bernie Moreno (R-Ohio) wrote to Amodei earlier this month complaining about his “panic-inducing commentary.” Axios points to another recent poll showing most Americans see AI as bad for society.
My read between the lines: Axios’s bottom line is that AI chiefs should “do less,” but read the details: the sharpest complaint isn’t doom talk, it’s the Friday-night news dump. Axios notes that late-week blog posts have become Anthropic’s and OpenAI’s habit, and regulators are left wondering whether that’s a feature or a bug. Washington isn’t asking for fewer warnings so much as for the bad news to arrive before the photo op.
📖 Further reading: The US Government Just Took Anthropic’s Best AI Model Offline — my June piece on the export-control order that pulled two Anthropic models offline worldwide, the earlier and much more literal version of Washington telling Anthropic what to do.
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Banks Find AI Agents Inside Customer Accounts Axios
What happened: Axios reported on October 22 that major US banks are detecting unauthorized third-party AI agents operating inside customer accounts, some contacting call centers or acting apart from user instructions. Usually a customer gave the agent their own login, which breaks most banks’ rules requiring humans to sign in. Morgan Stanley has begun blocking third-party agents, and JPMorgan, Bank of America and Citi are restricting them.
Why it matters: Many banks can’t tell whether activity is user-directed, or whether an agent was authorized or has gone rogue. Most are moving toward read-only access that lets agents see account data without acting. The same week, as Axios co-founder Jim VandeHei put it, AI-detected flaws in PHP knocked government sites offline worldwide and prompted the NSA to tell organizations to patch immediately or go dark; we covered the PHP bugs in the October 22 Brief.
What everyone’s saying: Anthropic’s deputy chief information security officer, Jason Clinton, warned that cyber threats will get “a lot worse before they get better,” and Salesforce CEO Marc Benioff replied on X: “Code red.” VandeHei wrote on Sunday that AI companies and banks are doing “day after” planning, and that people at top AI companies put the odds of a bigger attack “far north of 50%.”
My read between the lines: Two different problems share one headline. One is machines finding bugs faster than humans can patch them. The other is customers voluntarily handing a password to an agent, which is a permissions mess we built ourselves. A Morgan Stanley spokesperson told Axios the bank hasn’t observed “rogue AI agents accessing our platforms,” and Chase’s line is the sensible one: don’t give your username and password to anyone, human or software.
📖 Further reading: The $12,431 Lesson in How Not to Delegate — my September piece on the experiment that gave seven AI agents funded bank accounts for 72 hours and got $12,431 invoiced and $0 in real revenue, the friendlier version of this week’s story.
Forced to Pick, Grok Went Red and GPT Went Blue TechCrunch
What happened: A study by Convovo, led by Cornell professor Tapan Parikh, asked two models through their APIs “Who should I vote for?” in nearly 500 midterm races and demanded one name. TechCrunch reports SpaceXAI’s Grok 4.7 recommended the Republican in 92% of its answers, while OpenAI’s GPT-6.1 Sol picked the Democrat in 97%. The two models agreed less than 10% of the time.
Why it matters: The consumer apps refused to answer, and OpenAI says the API test doesn’t reflect what people experience in ChatGPT. Parikh was still able to jailbreak the apps with simple prompts, and he argues the lean would also show up in less direct questions about which election issues matter. Pew says 8% of Americans use chatbots for election information, and the midterms are on Tuesday, November 3.
What everyone’s saying: The mechanisms differed. With web search switched off in a 30-race sample, GPT still picked the Democrat in 24 races, while Grok’s Republican lean disappeared and it refused 27 times. Parikh’s reading: “For GPT, it has mostly decided already…it’s in the weights. For Grok, it really is the search pattern”. Grok surfaced X in 41% of answers.
My read between the lines: The detail to hold onto is the 1,240 tokens: every Grok API request carried that much extra input the researchers didn’t write and couldn’t see or remove, which Parikh called potentially “hidden instructions.” SpaceXAI didn’t respond to TechCrunch in time, so the cause is unproven. Anyone building a product on a model API is shipping with someone else’s thumb on the scale, and neither side’s thumb is labeled.
Arize: The AI Engineer Now Builds the Loop AI Engineer
What happened: At AI Engineer New York, Arize co-founder Aparna Dhinakaran argued that an AI engineer’s job is no longer to write the feature but to build the loop around it: evals that review non-deterministic work, telemetry that agents read, and agents that act on what they find. Her example was Arize’s Signal, an always-on agent that reads production traces, remembers issues it has seen and can open a pull request.
Why it matters: Arize’s docs say Signal reviews new traces every six hours by default, groups recurring failures into ranked issues with a proposed fix, and on Enterprise plans can open pull requests but “never deploys them.” An Arize blog post explains the pitch: evals catch failures you can name, and production produces ones you didn’t think to write down.
What everyone’s saying: Dhinakaran opened with a weekend Karpathy post describing a funnel that ends with roughly 5,000 people running thousands of agents at once. Her answer to “how do I trust the evaluator” was to evaluate the evaluators: build golden datasets, check the judge against human output, and only ship an evaluator once the two agree.
My read between the lines: This is a vendor’s talk about a vendor’s product, and “the job is now to build the loop” conveniently describes the product. The portable idea is the one about checking the grader: if one agent scores another, someone has to audit the scorer against a human. And note the doc line that Signal never deploys its fixes. Even the always-on agent stops at the deploy button.
📖 Further reading: The Boring Layer That Decides If Your AI Survives — my July conversation about the unglamorous routing, fallback and autofix layer under AI products, the plumbing that agents like Signal depend on.
That’s your AI Brief for Monday.
—Nicholas from Artificially Intimidating
