DebtDeflation

This is a use case issue.

Right now, the most effective use case for AI is coding agents. Software development is a profit center for the tech sector, so it's not surprising that they're seeing big gains. In Corporate America, software development is part of IT which is a cost center, and the software landscape is 30+ years of bandaid-ed legacy systems, maintained by offshore contractors who submitted the lowest cost bid, so attempts to gain productivity via AI have not been as successful.

Where Corporate America has seen gains from AI in my experience is with customer support chatbots, but the customer service function overall in Corporate America is an unmitigated disaster, so the gains are hard to measure.

Beyond these two use cases, most people in Corporate America are just using ChatGPT to create slop and copy/pasting it wherever, and then goofing off in their newly freed spare time.

We need to see new AI use cases in Operations, Finance, Marketing, etc. that can handle business processes end to end before the productivity gains start showing up in the financials.

In Chatrie v. United States (June 2026) the SCOTUS ruled that individuals maintain a reasonable expectation of privacy in their digital location history and that geofence warrants constitute a search under the 4th Amendment. It's only a matter of time until a similar case involving Flock comes before them and they rule that a search of the Flock database constitutes a search under the 4th Amendment and that every single search requires a specific warrant.

>they want to make sure the finalized articles are handwritten and aren't chockful of LLM slophistry

This seems like a pretty reasonable perspective for a magazine. Use AI for ideation and research but not to create the final version of articles for publication.

>trillions haven't been invested in AI as just another skill, but as the "skill killer"

Part of the trillions are being spent on deliberately obfuscating the difference between those two concepts so that society thinks they're getting the former until the latter is ready.

My kids' high school is developing an "AI Literacy Program" for all students by grade level. Since I work in tech, I was asked to participate. I attended one session and then decided it was a waste of time. Their objective seems to be to write a massive document that no one will ever actually read let alone implement. The primary focus was on prompt engineering. I told them it's 2026 and every kid in the school can write prompts just as effectively if not more so than the folks in the room. The secondary focus was on "safety" but really, for high school kids it boils down to 1) don't just copy pasta the output into your assignment and hand it in 2) verify everything the model tells you before believing it 3) don't put sensitive info into the prompt 4) don't use it to do anything illegal or stupid. Not sure this requires a committee/document/plan.

Honestly, outside of coding tasks, the AI Summary at the top of every Google search is adequate for 99% of what I need and I hardly even use ChatGPT any more.

It strikes me as more talking up their valuation for their upcoming IPO. "Our AI could destroy human civilization. It just escaped it's confinement. We believe it can feel pain."

Yeah, I don't want my coffee machine scanning my network continuously to see what other IOT devices I may have connected.

IMO, the far simpler explanation is that they are training models on chat logs and they do not want abusive conversations in their training data.

How are they defining "metadata"? 1TB of text in 10 days is........A LOT. Metadata about what exactly? What time you brew your coffee every morning, how many cups you drink per day, whether you like regular coffee or cappuccino? I'm having a hard time understanding how all that can add up to 1TB of data. If it's recording audio or packet sniffing, it becomes a lot easier to understand.

>When one tech YouTuber put Muse in charge of their Facebook Marketplace sales, it sold his stuff way below acceptable rates

Why are we depending on LLM "reasoning" to negotiate a price rather than just having the user enter a lowest acceptable price deterministically?

In today's installment of "totally not circular financing"...

This is the part that really surprised me: "Anthropic said nearly a quarter of its revenue came from two customers last year".

Who are these two customers? A quick Google search seems to indicate that Cognizant is their largest "enterprise" customer by number of seats, Microsoft is their largest API customer, and the $10B contract they recently signed with Meta is their largest contract ever. But those metrics don't necessarily translate directly into annual revenue.

Nvidia is hedging their bets against becoming too dependent on OpenAI, Anthropic, and the hyperscalers as their only customers. If on-prem deployments of open models starts taking off in corporate America, this acquisition positions them well.