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I think Anthropic and OpenAI have found product-market fit
681–690 of 1001 posts
Re: I think Anthropic and OpenAI have found product-market fit
#682They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down. This means we're going to need $1t+ per year in spending, per year, on tokens. 200m knowledge workers in the world, 30m developers. We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer. That's a _huge_ shift. Most people…
I was in college in the late 1990s/early 2000s and I distinctly remember an econometrics professor state the following: "As cable TV and Pay Per View came out, there were studies done about how many movies people would watch if given unlimited access to films. The results were bandied about as proof that we should build out all this infrastructure to support this line of business. When the data was further analyzed b…
> it turned out that people claimed they were going to watch films 10-12 hours a day, every day of the week. Impossible."
I realized it long ago: one needs output to make meaning. Input can only be the cherry on a cake in one's life. That, actually, makes FIRE or Fat FIRE not so sustainable unless one has other hobbies.
Re: I think Anthropic and OpenAI have found product-market fit
#683I’m happy with spending zero dollars on ai lol..
Re: I think Anthropic and OpenAI have found product-market fit
#684So how do openai and anthropic plan to keep customers when GLM-5.1 is just as good and open source and a lot cheaper? I don't see the business model working. My closest friend actually does automation software for large companies. He does not use Claude or openai at all. He primarily uses gpt 120b on cerebras and glm-5.1 for heavy thinking work. And some other small models for various tasks. All open source. And thes…
GLM-5.1 isn't just as good. It is no match for Opus running in Claude Code. Please try it yourself. Open source models are about a year behind at least.
In the second half of last year, I found that agentic coding with proprietary models (≈ vibe coding) reached the point where it actually speeds up my ability to deliver useful code at work. Before that, AI-based autocomplete definitely helped, but (despite the claims of the people selling AI coding tools) letting an agent author more than a file or so at a time (often a function or so at a time) required a very intricate plan or it would create a mess. Creating that plan or cleaning up the mess would take longer than just doing everything myself.
For me, it feels like widely available open models have recently crossed that same canyon. Are they as good as e.g. late-model Claude Opus? I don't think so. But they have absolutely gotten past the point where they are beneficial. This means that, for me, they are about six months behind.
Re: I think Anthropic and OpenAI have found product-market fit
#685The amount of caveats and vibes that this article is powered by could run New York City for a decade.
Re: I think Anthropic and OpenAI have found product-market fit
#686I feel like there's a bit of AI psychosis in this particular post. >"These are tools which burn vastly more tokens, but are also quickly becoming daily drivers for the work carried out by extremely well-compensated professionals." >"Somehow this fragment turned into headlines like Uber’s COO says it’s getting harder to justify the money spent on AI tokenmaxxing, because the market for stories about AI failures remain…
Being pedantic, but I don't want to lose the meaning of the term: "AI psychosis" doesn't refer to someone who thinks AI is really good. It refers to someone who develops symptoms of psychosis from talking to an LLM, e.g. believing they have developed a new Grand Unified Theory of physics.
Re: I think Anthropic and OpenAI have found product-market fit
#687Anyone actually making money paying all of these monthly fees? Or just hobbyists? I have yet to see any real ROI posted anywhere.
Ironically, we had more new interesting things launched daily 5 years ago before AI.
Re: I think Anthropic and OpenAI have found product-market fit
#688Earlier quoted context omitted.
I work for a tiny little company ($150MM annual rev with 9% net) and we are already looking at dropping $100k on hardware to run local models because, for us, they're "good enough." Our estimated spend for AIaaS would exceed that cost in less than a year. In a few years, there will be hardware capable of running frontier models good enough for most things at accessible prices for even tiny companies.
Yeah, that's the part that just seems to be wildly under-discussed to me. If open source models are ~3-6 months behind SOTA, and ~opus4.6 capabilities are good-enough for product market fit, do the frontier labs have half a decade to catch up on their prior burn? AI cost ballooning faster than companies can afford is becoming a very common topic in my circles right now. The era of "I'll pay infinitely more for margin…
> If open source models are ~3-6 months behind SOTA, and ~opus4.6 capabilities are good-enough for product market fit, do the frontier labs have half a decade to catch up on their prior burn?
They know they do not and that’s why they’re all trying to IPO right now, so they can pass the bag to consumer investors
Re: I think Anthropic and OpenAI have found product-market fit
#689My employer went from the Max plans to Enterprise this month which was utterly stupid. We went from paying $200 per head to like $500 for some people, even more for others. For the same product. Oh well, guess we’re doing our part to prop up this bubble.
Re: I think Anthropic and OpenAI have found product-market fit
#690Earlier quoted context omitted.
>The bottleneck has moved from producing a thing that works to knowing that the thing was the right thing to build I would argue that that's been the case for quite some time before AI. As an example, what innovative amazing world-changing products have Google or Meta launched in the past decade with their very high numbers of very talented and highly-compensated engineers? The issue with most big tech companies are…
Google's internally developed and sometimes even launched plenty of innovative new products in the past decade. Stadia, Fuchsia, federated learning, and the whole transformer architecture that underlies this AI boom are good examples. The problem is they get killed by some other executive who is afraid of their department looking bad by comparison. I think this is fairly illustrative of the challenges in AI becoming…
Google does not follow through in the long run on many of their pet me-too follow projects, however they do not stray away from their core remit making their real customers happy the ones who buy the ads…
Obviously that includes whatever needs to be done to hoover in data from their marks and Meta also does the same thing without fail and both are really good at it. But outside their remit not so much.