Other technologies like surveillance (and, perhaps, AI) are more clearly centralizing and enabling of power.

The difference matters a lot if you're having mixed feelings about working in technology.

A small app, or a task that touches one clear smaller subsection of a larger codebase, or a refactor that applies the same pattern independently to many different spots in a large codebase - the coding agents do extremely well, better than the median engineer I think.

Basically "do something really hard on this one section of code, whose contract of how it intereacts with other code is clear, documented, and respected" is an ideal case for these tools.

As soon as the codebase is large and there are gotchas, edge cases where one area of the code affects the other, or old requirements - things get treacherous. It will forget something was implemented somewhere else and write a duplicate version, it will hallucinate what the API shapes are, it will assume how a data field is used downstream based on its name and write something incorrect.

IMO you can still work around this and move net-faster, especially with good test coverage, but you certainly have to pay attention. Larger codebases also work better when you started them with CC from the beginning, because it's older code is more likely to actually work how it exepects/hallucinates.

That said, there is now much better competition with Codex, so there's only so much rope they have now.

Trading billions worth of idle compute, in exchange for a high-strike call option on the #3 player in the most-promising-vertical for AI, plus (presmuably) some access to their data, starts to sound like not a bad trade. Especially if you're pre-committed to betting your entire rocket company on winning in AI, and you're currently in sixth or seventh place.

And yet - Anthropic is still struggling to have enough capacity to serve demand - they are virtually sold out.

And yes, are almost-as-good open models, on part with the closed models from 6 months ago (at worst), that are just a single Openrouter API call away, and yet Anthropic is still selling out. So people are paying for the premium product anyway, for whatever reason - maybe the last bit of intelligence is worth it, maybe they like the harnesses/products around the models, maybe it's a brand/enterprise sales thing.

Put aside your feelings about the AI industry and imagine we are talking about thingamajigs. Prices for thingamajigs are going up. They are still selling out about as fast (or faster) than the company selling them can build factories. There are more cost-effective competitors already in the market, but thingamajigs are selling out anyway.

Would you, looking at the thingamajig industry, conclude the "jig is almost up"? That "the returns aren’t anywhere close to what investors expect" and that the impending IPO is all some desperate hail mary to save things before the collapse?

a) effective price-per-token is rising b) there is insufficient compute to meet the demand.

And your conclusion is that the industry is circling the drain and due to collapse?

I think when we do get AI that can perform as well as a human at functionally all tasks, they will be multi-paradigm systems; some components will not resemble anything in any commercial system today, but one component will be recognizably LLM-like, and act as an essential communication layer.

For example, I don't pull in tons of third-party skills, preferring to have a small list of ones I write and update myself, but it's not at all obvious to me that pulling in a big list of third-party skills (like I know a lot of people do with superpowers, gstack, etc...) would cause quota or cache miss issues, and if that's causing problems, I'd call that more of a UX footgun than user error. Same with the 1M context window being a heavily-touted feature that's apparently not something you want to actually take advantage of...

I am thinking of situations where one of those aren't true - where testing a proposed update is expensive or complicated, that are in systems that are hard to physically push updates to (think embedded systems) etc

The biggest issue is legacy systems that are difficult to patch in practice.

The economic incentive is overwhelming to corrupt these signals, either directly (link sharing schemes, upvote rings, bots to like your content) or indirectly (shaping your content itself to have the shape of what will be promoted, regardless of its quality).

What you almost want is to use any of these ideas and hope for it to catch on widely enough in your small niche to be useful, but not so much that it comes an optimization target.

But I also see the potential value. This is an entertaining and highly influential podcast, a lot of top VC's and founders watch it; it definitely punches well above it's audience KPI's in strategic value. I've seen many interviews or op-eds on the platform pretty clearly shape the startup discourse on X.

I also think it should run mostly autonomously, it'll only be as much of a distraction for OpenAI execs as they want it to be.

OpenAI just raised $122 billion (including future commitments), so whatever the purchase price was (we have no diea) is not going to even be a rounding error on their financial resources or their ability to pay their datacenter bills.

They get a lot of guests to do interviews that they wouldn't do elsewhere, in part because they are unabashedly and unapologetically cheerleaders - pro-tech, pro-VC, pro-startup, pro-Big-Tech, etc. They don't grill you like an old-school journalist would about whatever the latest political controversy is, they ring a giant gong when their guest brings up a cool traction or fundraising number.

I would never use it as my only source of news for what's going on in tech, but with a lot of other tech journalism covering the downsides or problems with the industry, there is definitely a niche for them.

Wow, what’s next?

Ecommerce giants owning major newspapers? An aerospace company owning a microblogging platform? Startup accelerators owning tech news aggregators?

And has roughly 2.7 billion monthly active users. This means the average YouTube user brings in around $1.23 per month. When you consider that CPM's can easily swing by 20X based on how wealthy the user demographic is, and willingness to pay a subscription is a strong signal for purchasing power, I would not be at all subscribed if a YouTube premium subscription was revenue-neutral for Google.