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> “The bubble doesn’t want cheap useful things,” Doctorow said. “It wants expensive ‘disruptive’ things
That about sums it up. I challenge anyone to name five things that have gotten cheaper or better in their life thanks to AI.
I can't think of anything that I consume that has gotten better, and the only thing that has gotten cheaper is the value of your skills to your employer, as it wants you to offload more of your work to a machine they own or rent. But perhaps someone here can find some tangible improvement.
1 - I worked abroad and wasn't really familiar with the systems there. Gemini made me aware of a kind of pension account that I could withdraw from when I left the country netting me a few thousand dollars.
2 - Working as a tech contractor, charging by deliverable, Codex/Claude Code speed me up and it doesn't seem to have significantly dropped rates in the market.
3 - Also contractor related: I had Claude do a quick legal sanity check of my contracts, and it warned me of some clauses that I'd be better off removing/changing/refining. I was not aware of these nuances and would not have paid a lawyer for this as the contract was too small, but the changes were accepted by the client and reduced my risk exposure meaningfully.
4 - Learning a foreign language, I use it to check my draft emails and messages. It corrects them but also serves a tutoring role providing feedback, improving both the accuracy of my communication and my rate of language acquisition.
5 - Gemini Deep Research helped me narrow down tent models that met my fairly specific set of requirements. Very happy with the tent I ended up buying, from a brand that was not on my radar before.
It's allowed me to learn things more quickly. For example, I haven't written any C++ professionally for 20 years, and found myself in the position of needing to know it again. The existing material online for learning it is thick; sometimes I have a question that isn't answered directly or clearly in any way I can find with a standard search. I can grill the LLM much more quickly and pointedly.
However, this does not answer the usefulness-at-work question. Does anyone care if I know how the initializer list braces in constructor `Foo{1,2}` work? Today for hiring managers smitten with AI, seems like they don't.
Personally, I'm just trying to stay sharp on the off-chance that things crash out and people who know how to engineer *and* code become highly valuable. If not, I'll be doing something else, anyway.
- better speech to text,
- better auto translation,
- better image to text.
Other then that, I hate how AI is inserted into everything, how I cant tell anyone I did something without them telling me I could have use AI for it, the doom trolling , the AIG singularity bullshit as if it was new incoming god rather then a set of technologies.
While I agree with your sentiment and observatin by and large, ideally you could have added a few constraints as a lot of people here have had "Holy shit" moments with AI, but often it's most pronounced in a very specific personal context. Learning topics faster, fixing things without having to call a repair man, life-saving medical tips (somewhat more of a thing because of a handicapped health system, but that's another discussion).
Funnily enough though, the benefits don't seem to scale to the work environment as smoothly across the board. It's almost as if, to use pg's famous line, they built things that don't scale.
The moment you scale AI beyond the personal context, the noisiness and non-determinism of the technology starts wrecking havoc in ripples through the industry, damn the productivity. Managing the slop you've created, but more importantly (I would normally put an emdash here) managing the slop others created turns out to be a problem as big as the tech that created it.
What is the evidence of this? Isn't it strange no revolutionary product has yet
been created from the coding output of this technology? More to the point, the only thing that comes close is the Claw. So what we have as the first breakaway product is something to help orchestrate the slop generators, instead of say, a new Phone OS that rivals Android and iOS, or a Metaverse that is actually worthy of its name. Nothing to see here, just more shovels to shovel the shit essentially.
We are still in the grasp of the electric and information revolution. Peter Thiel said more than a decade ago that if you compare life in the 70s and now the only major visible difference would simply be more screens in various forms nowadays. This he presented as evidence that no new revolutionary technology has been created in the interim. At present, the same is more true of AI. It has made access to information faster than before. However, unlike other processes of learning, it has made it more difficult to ascertain the correctness of the information presented. It is doing this by actively removing the direct connection between the information it presents and the primary sources responsible for that information (think Google replacing its search field with an AI field).
The non-LLM AI stuff in the medical field is what I find most promising. Not the banal AI doctors and nurses replacing real doctors and nurses, but more the research end of things, where hallucinating and pattern matching to create new compounds to test seems promising. Of course, any technology that is able to routinely aid discoveries in applied maths and physics will also be welcome.
If this half decade saw an explosion of new bespoke hardware being built and sold aided by LLMs (think a tech shop of 10 employees being able to build and sell robust smartphones from scratch to a niche market sustainably etc), or jumpstarts an era of reverse engineering on crack that pierces through the tech oligopoly then that would be evidence of disruption on a macro scale... But no, alas, we are still waiting.
I've been enjoying the industrial revolution parallels. Have the economics of bespoke products changed? Not really. But is it now easier to get a crude, but functional, approximation of an idea? Yes. The main issue appears to be users conflating B for A (and not realising their AI creation is fundamentally a mess).
I find the industrial revolution parallels interesting too. I figure we're in the steam age and this is a bit like the railway bubble. Or maybe a bit before that - I'm not sure current AI is as useful as railways.
Yeah, we saw this with a client project. They "built an MVP" with AI in a weekend, then asked us to make it real. Took 3 months, not because the idea was bad but because a demo and a system that handles edge cases and doesn't fall over are different products entirely.
The “reverse centaur” is a natural product of capitalism wanting workers to be as replaceable as possible in order to drive down wages. An ordinary “centaur” is counter to companies’ goals, they don’t want workers empowered.
As a business owner I assure you I am not overly obsessed with labor costs. I obsess on all costs, and all revenues. VC funded and public companies may or may not take a similarly holistic view.
Ironically I had a job figuring out what he meant by strike at its roots so I asked an LLM. Gemini seems to think he means labour bargaining/strikes, attacking the stock bubble with better accounting regulations and open source.
I'm not sure any of those will make much difference beyond what's already happening?
"If that’s the case, AI will let them wire the toy steering wheel directly into the drivetrain. So you can have an amazing idea as a corporate visionary, and you don’t have to have any ego-shattering confrontations with people who know how to do things, who tell you you’re actually an idiot"
Usually when talking about bubbles bursting its about a stock market bubble. P/E ratios now are approaching/passing the P/E ratios during the dot com boom/bust. Another reference point with high P/E ratios was around 1929-1930 (there are others too).
The dot com boom can be thought of as ecommerce which enabled investing in a lot of silly ideas at the time. A key technology that underpinned it all and won out was search. Every ecommerce site felt like they needed search, there were search engine companies, lots of competition across google, yahoo, etc.
An interesting lens to put on AI and the current stock market is that the software will be commoditized, its an eventuality. Its trending towards being able to run LLMs locally and get decent output. Decent is subjective but output similar to Q4 of 2025 models is when we started seeing more consistently usable output.
I believe that will a potential the inflection point for a bubble bursting the stock market: local or DIY LLMs producing "good enough" output and companies publicly backing away from enterprise contracts, lowering their AI spend if they can find cheaper ways to do it.
It's not, though. The banking system holds onto everyone's money in a fractional reserve system, if you let a run on the banks happen everyone's money is gone. That's "too big to fail".
If anthropic and openAI fail, the top 10% lose half their money in a stock sell-off. That's perfectly tolerable. Maybe congress bails them out anyways but they don't have to.
The bubble is weirdly being kept alive by the promise it can replace software developers, which is ironic because all it has really achieved is a deluge of slop.
Social media has also gone utterly crazy. The last time I saw a gaslighting operation on this scale and volume (including accounts who "hate" AI "because its so scary good") was during the start of the Gaza war.
These accounts becoming easier to distinguish too, because whether they boost AI or feign criticism they all categorically refuse to use the s word.
I guess there is a few trillion riding on perpetuating this mass psychosis so it makes sense they'd try to use every trick to keep it going as long as possible.
Internet bubble was nothing like this. The scale is greater, the promises are more insane and the pop is going to be much more devastating.
Been running Copilot and Claude Code across our team for months now. It genuinely speeds up boilerplate and small refactors, but the "replaces devs" pitch falls apart the second you hit a gnarly bug or need real architectural judgment. Every VC deck cites the same handful of cherry-picked demos, never the actual failure rate on messy codebases.
People seem to think it's all smoke and mirrors. IDK. My employer, in an industry as far removed from Silicon Valley as you can probably get, makes more and better use of it all the time. There's enormous amounts of work done every day in corporate America that amounts to "I need X, but X involves some data from legacy system Y and legacy system Z, and that's going to take me an hour to glue together because our entire enterprise runs on a system cobbled together over the past 50 years". You know what Codex/Cowork/etc can do really stunningly good these days? Take the files you provide, listen to what you want from them, ask clarifying questions as necessary, then write a script that programmatically does exactly what you ask for, checks its own work and then gives you the result.
We also realized that a project we thought was going to involve thousands of man-hours of very expensive, senior draftsperson labor to backport a feature into decades of CAD files we could, after years of procrastination, just forget! AI has got to the point where it can see and count what we need for us with greater accuracy, in our tests, than our very best humans, so we can just make the change going forward and let AI read the old files as needed.
There's incremental commercial adoption of that nature that I'm sure is happening slowly across the corporate spectrum, and that kind of thing is durable demand, not a bubble.
Note though that I'm talking about real revenue, etc. Not stock market bubbles. The feds been inflating that junk since the housing bust. We'll all pay for that eventually.
Used one of these for a first pass on a rental property tax situation this year. It got the federal stuff right but completely missed a state depreciation quirk that would've cost me a few grand. Fine as a drafting tool, still need someone who's seen the specific edge cases.
While that's a bit cruel, I do have to wonder how exactly Doctorow got in the position he has -- he isn't somebody who has done great things and is now a tech critic like someone like Geoffrey Hinton. He seems a pleasant enough fellow, but basically he got fame from writing SF books that he gave away for free. How does this translate into being an expert that journalists fawn over?
He's also been a vocal advocate about copyright reform and against DRM, and his arguments are coherent and generally well-regarded among the tech crowd. In context, "giving away books for free" is a particularly strong form of putting his money where his mouth is.
Journalists fawn over many people who write for public consumption in some way (book authors, academics, etc.), and who already agree with the point of view the journalists want to promulgate. I don't think Doctorow has particularly more insight about AI than anyone else does, but I also don't think that "insight about AI" is the main reason Ars Technica chose to publish an interview with him - they did this because Ars Technica has an anti-AI editorial position and so they find it useful to promote Doctorow's anti-AI-book. A different group of journalists who did not have an anti-AI editorial position would not have interviewed and published Doctorow, or at least not done so under as friendly circumstances as Ars Technica did.
Seen it before - Minsky bashing neural nets in the 80s, then decades later everyone's a "godfather" prophesying doom or salvation. Age doesn't cause it, fame does. Once you're the guy reporters call, hedging never makes headlines, so you stop hedging.
