ChatGPT became publicly available in November 2022, and Claude followed in March 2023. It's year 4. It's foolish to think we know who the big names will be 20 years down the line, which is the kind of timeline bond investors are thinking about. A lot of people probably wish they could have invested (or invested more) in Google or Facebook in 2006, but they are probably very glad they did not invest in Yahoo or MySpace (or Friendster or Tumblr .. you get the idea) at the time, despite looking very promising (MySpace was beating Google on pageviews on some days in '06 and '07). Instagram and Snapchat and TikTok and WeChat didn't even exist yet.
My favourite example is amazon.com and pets.com. In hindsight amazon looks like an obviously good investment and pets.com doesn't, however amazon was heavily invested in pets.com itself.
But the idea in early e-commerce was sane. Undercut retail on cost, increase profit. The ones that failed, failed on execution. Pets.com failed because you can't ship massive bags of food below cost and expect to make a profit.
Setting fire to cash to have the best model that anyone else can also do isn't a business model. They're never going to be able to stop setting fire to cash or they'll fall behind immediately. Their user base will not pay the bills. It's an obviously bad investment.
I don't see anything obvious about that at all for amazon in the book selling days.
dog food is heavy, and a repeat purchase that often requires its own store and brings no joy. Its the ideal thing to get delivered to your house on a schedule.
books are something you choose differently everytime, and the people that like them read recreationally.
Bezos was on another level, launching a successful company(online books) that was effectively just a way to boot strap into an everything store.
It was actually even smarter than that. People also forget about "media postage" rates that existed at the time. (They still exist, just fyi)
In 1999. To ship a book using media mail cost of $1.13 for the first pound and $0.45 for each pound up to 7lbs. It got cheaper per pound above that.
This meant even heavy books were relatively cheap to ship. Way cheaper than random goods.
Wasn’t one of Amazon’s biggest advantages over brick and mortar stores the fact that you could effectively avoid sales tax for a lot of the purchases?
I heard that's why Washington state was chosen. Close to tech talent (Microsoft was there) and fewer people living there than other tech-heavy states such as California, so the US tax-exempt market was larger from Washington state.
I believe ChatGPT or Anthropic are both trying to be the "everything store" - whatever that may look like - of this brave new world. Call it a "business OS" and/or "life OS", they're clearly aware that research alone won't win dominance, and signs have been cropping up for the last year: Cowork, Research to Artifact pipelines, Dots/Instinct/Muse-esque personal assistant alternatives.
But the counterpoint still translates here: Amazon did not start with 1000 warehouses (i.e. Anthropic's debt obligations) and _then_ race to build a marketplace that would fill them with everything under the sun. It started lean and mean, and I suspect the Anthropic-killer will do the same.
And despite all of that ... Chewy is successful.
CHWY is -7% per year since Sep 2019, while SP500 is +16% per year, and CHWY has 2% profit margins, give or take. I don’t know what success means, but I would not have wanted to buy CHWY stock anytime after IPO.
My favorite example is WebVan vs. Instacart. What a difference a decade makes.
Uber / gig economy seems to be the biggest difference
In some ways, the biggest "new form of business/commerce" that ubiquitous smartphones lead to was the entire gig economy.
your point is well-taken enough, but the idea that a sharp investor was investing in Yahoo or Myspace in 2006 is a bit silly. Myspace was bought out in 2005, and it had been dead for years before that. and I don't think I had gone to yahoo.com for any reason other than testing my internet connection anytime in the prior 10 years before that. also, investing in Google or Facebook back then would be enough to retire on, much less had you invested in a basket of tech-but-not-obviously-dead-tech stocks. from what I recall from that era, the one things I knew for sure at the time were (1) Google is gonna be around in 20 years, bigly and (2) Facebook might be DoA in terms of social media endgame, but operationally they are very strong and executing.
what I really push back on is the idea that there are magically going to be all sorts of entrants into the space that meaningfully compete with Anthropic or OpenAI in the near or even medium term. for as much as the reddit set continues to put their collective heads in the sand while screaming "there's no moat"... there is a moat, and one so prominent as to be self-evident. not once has an open-source model come even close to genuinely reaching frontier performance for anything other than cost, and cost is by definition the easiest thing to improve on-- just distill bigger model into smaller model: boom, cheaper!
>Myspace was bought out in 2005, and it had been dead for years before that.
Myspace's peak popularity (in terms of monthly unique pageviews) came in 2007. Yahoo was ahead of Google for several years, though I am struggling to find good data on the specifics there. Your understanding seems to be based in your own personal experience of those sites, which is fine, but I don't think it quite matches the big picture.
Your proposal that there won't be any new entrants is puzzling to me because, in the grand scheme of things, OpenAI and Anthropic ARE new entrants. The entire space is new. It feels like ChatGPT became public eons ago, but it hasn't even been 4 years. Imagine saying in 2008 that no photo-sharing site would ever surpass Flickr - it's been dominant for 4 years! Why would you go to something new when everyone's photos are already on there?
One absolutely enormous advantage new entrants have in the AI space is that they can start on third base. Anthropic had to spend billions and billions of dollars to get to Opus 4.6. A new entrant could distill its way into a model roughly on par with that far more cheaply, and spend its VC cash on things like marketing and price cutting rather than burning it on actually creating a good model. The existing players need to service mountains of debt and are locked into deals for compute based on outdated assumptions about efficiency. I'm not saying it would be EASY for a new entrant to waltz in, especially to the enterprise market. But it's not impossible especially on the consumer side: Meta was essentially a non-factor until Muse came out, and now most people think of Muse when they think of an AI agent (as opposed to Claude, which is just a chatbot in the public imagination, because that's what it was when it became popular).
i agree that they are, objectively, new entrants, but that does somewhat mischaracterize, in the same sense that calling Ford a new entrant into the car industry after they came out with the Model T. like... they are creating the market into which they are entering. ChatGPT galvanized this entire phenomenon.
i do agree that there is a good chance that one of these post- OpenAI/Anthropic competitors probably will get some sort of unanticipated foothold, if only because enough talent will drain out over time. what i push back on is the idea that being 90% as good as frontier models is actually good enough to matter in even the slightest. especially since we know that the "real" internal models are so much further advanced than what we get access to publicly.
i absolutely agree that it isn't impossible-- if the only thing stopping a new entrant is capital (as in: they have the trillion-dollar idea necessary to be competitive) that will absolutely never be a true impediment to a worthy competitor.
It's not even clear that the "frontier AI lab" is going to be the best, most profitable business model to evolve out of this tech. Especially if models are easy to distill, a new lab can start on 3rd base, and China open weights aren't far behind.
The winning business model may be 1 or 2 abstractions removed from "build a really good LLM model", and yet to be revealed in the marketplace.
Most people do not think of Muse as an the ai agent. Most think OpenAI or Claude. The people using Muse are using it as a facebook chatbot.
> Most people do not think of Muse as an the ai agent. Most think OpenAI or Claude. The people using Muse are using it as a facebook chatbot.
Yeah, I was with the case bring made, until that point. A very bizarre claim, especially given the questions around safety:
https://www.malwarebytes.com/blog/privacy/2026/10/metas-muse...
I disagree, Yahoo was actually seen as a technological leader and, unlike Google that was purely a search engine in its early years, Yahoo was a full platform that seemed poised for a while to become a landing page for the internet.
When it comes to AI there simply isn't a moat - Anthropic would be my best guess out of the current entrants due to them trying to construct one via value-adding code assistance tools that might create enough friction to prevent customers from jumping ship but the models themselves are a commodity that depreciates extremely quickly. If you want to retain the frontier premium you need to constantly be improving due to the ease of distillation.
Your comment makes the timeline seem very different from the way I remember it. Yahoo was dominant much earlier than Google, and was one of a handful of big search engines. Google was a relative newcomer at the time, which had the benefit of being way better at search, which is why it eventually dominated the competition.
If open source models stay consistently <12 months behind the frontier, that shrinks the moat considerably; not for all use cases, but maybe enough to scupper profitability
I was sad on the day when my.yahoo.com shut down. It had a bunch of RSS feeds, and news, I have not found a good replacement for it.
That argument swings both ways. There's no real reason to bet on OAI or Anthropic. Not with the rise of local models and the increasingly desperate attempts at regulatory capture.
That's what I'm saying. Either one could be Facebook. Or it could be Friendster. The rise of local models raises the even more daunting possibility that nobody becomes Facebook, but you have to imagine somebody will win the consumer market for people who just don't want to bother figuring out a local setup. That market is orders of magnitude less lucrative than the enterprise market, but a company that dominated it would still be one of the hundred or so largest in the world.
Someone can always optimize a model down to running on a typical 32 GB M6 or M5 Mac.
It doesn't need to be the best. It just needs to be good enough. I'm not smart enough, but if I was I'd be working on a locally ran interface engine. That's where you charge say 100$ a year. Said engine would allow you to use a large open source model locally to the point where it's good enough to rival Haiku.
If I had to guess someone is working on this
> Someone can always optimize a model down to running on a typical 32 GB M6 or M5 Mac. It doesn't need to be the best.
Two observations - one, there is some set of tasks where intelligence is already saturated, and sure, that threshold is rising by the month. So, sure, for some tasks you don’t need the best model. But there are other tasks that need models smarter than what we have now - like “make a cure for <disease>” that will yield billions of dollars of value, but likely require quintillions of tokens and higher IQ/token than anything we have now.
As long as we have non-saturated economically valuable tasks there will be incentive to train new and smarter models, and the frontier will always be on racks of GPUs not local distilled versions.
Two, it’s not clear that you can distill all the way down to 32GB even for the current frontier models. IIRC Karpathy has speculated that you can get an AGI into GPT-3 size, if you drop all the memorized facts and keep the reasoning core. But this is highly speculative! The best 32GB models right now are substantially dumber than even the large open-weight models, let alone the frontier closed models.
Perhaps I am missing something, what are you imagining of “a locally ran inference engine” that doesn’t already exist in a DGX workstation, Framework desktop, MacBook, etc?
>Perhaps I am missing something, what are you imagining of “a locally ran inference engine” that doesn’t already exist in a DGX workstation, Framework desktop, MacBook, etc?
Someone much smarter than me would figure this out.
One option would be an alternative bare metal OS which could allocate ram more carefully.
I'm sure with billions upon billions in VC funding a solution could be found.
Someone much smarter than me would figure this out.
Imagine if we had some kind of really intelligent AI model that we could pose problems like this to. It's obviously not the kind of thing an open weight model running on your local machine could do, but if we had some kind of really large, closed source model running in the cloud that could solve really difficult problems like this, I wonder if there'd be a market for it...
One way would be to generate a new model for every request. Currently we create an entire universe for each step when training if we predetermined what needed to be created it could be much smaller.
I stumbled upon this neural net compressor by Bellard that beats gzip on compression benchmarks
https://bellard.org/nncp/
> like “make a cure for <disease>” that will yield billions of dollars of value
I think it's important to qualify that while I will very much appreciate such an innovation and I think it'll provide significant value to society... I remain doubtful of the ability for any company to actually capture such an astronomical yield. Either there's enough of an investment required by the company to help get that to market that the yield will be significantly lower or everyone and their brother will just launch competing products two days later. And, as a society, I don't think it's logical for someone writing "Hey Alex, cure cancer" with billions of dollars so I doubt there's going to be much appetite for creating such a moat via legislation.
This gets to the same point of "If ChatGPT can make you a successful million dollar business in a few prompts why is OpenAI sharing it" paradox. It very well may be that AI tools can accomplish the labor that used to be required to create such a business, and I'm sure a few people will land a jackpot and get rich in the transitional phase but if it's so cheap to build it why would someone pay you money for yours instead of just running the same prompting themselves? For most people that's because they lack the knowledge that they can but you'll still have a significant number of instant impersonators.
> This gets to the same point of "If ChatGPT can make you a successful million dollar business in a few prompts why is OpenAI sharing it" paradox
I agree fully, to be clear o was using that prompt as a shorthand, I don’t think _you_ can type that prompt and get $1B back.
It can’t do this now, obviously. You need a lot of scaffolding and domain expertise to get the most out of these models. (Eg Harvey.)
But eventually, the labs can just run the business themselves.
I’ve long held that as we see models become more capable, they will be less available to the public. It seems pretty obvious that vertical integration on private models (perhaps a per-industry fine-tune, eg a legal model) licensed to subsidiaries will let the labs capture more value.
Why would you publish your model, enable competitors, and enable distillation of your hard-won intelligence? Only antitrust law can prevent this outcome IMO, the business pressure is extremely strong.
Great comment, I'm working on my own Hermes/Mac Mini & the amount of sec ops/setting up its arms is a good challenge as a non-technical.
Building the iPhone-platform equivalent of this would be interesting.
The thing is, would the top AI minds rather charge end users 40 to 300$ a month for AI usage of sell a 100$ a year license to run it locally.
I'm far from an expert, but I don't think it's impossible. Future models won't give out vacation ideas, they'll focus on one or two things. This might allow us to get the size down.
Would AI companies rather have $100/yr of license revenue or $480/yr of token revenue at 50% margin? Obviously the latter, by more than 2x? And even larger dollar profit from the higher sub levels you mention.
The constraints will work against them long term. All this is doing is forcing people to look at how to run more with less. These companies are bloated beyond reality and have no incentive to optimize anything. They're starting out with access to everything and still producing very little tangible value comparative to the inputs. OAI and Anthropic represent the pure gluttony of the US tech bro market and it's not going to be the long term winner. It can't be lest the masses are cool at crowning these narcissists as kings.
I think the hardware makers are going to have the last laugh, but not sure if that would be NVIDIA
Rise of local models? what rise? I run qwen3.8 flash next on my 200GB/s 128gb Strix Halo and even that is barely acceptable, big models require so much more. Nobody's running a local model on their power sipping phone. It's just a nerd's wet dream.
That's true today, but what will it look like in 20 years? Over time, algorithms and hardware always gets faster and more efficient. Like if I wanted to run a fluid dynamics simulation, what would have required a supercomputer 20 years ago can now run on a single desktop computer.
256x speedup on FAT32 just earlier today; I expect we'll have a nice bump in performance from domain cross-pollination for a good while now.
barely acceptable for all use cases? like a speed issue?
Regardless, looking back even a year at SOTA local models that fit on a non-absurd system creates a crazy trendline toward the future. And I think model routing is the gap filler that can make/break now vs. Oct 2027
I suspect Anthropic is of this general thinking as well; they've clearly switched from a thoughtful progression toward a natural IPO to "omg sprint for it". Along with the regulatory capture attempts, it's hard to imagine what is breathing down their neck so hard outside of this.
Totally agree local inference of any major scale is a nerd's wet dream short of many stacked improvements in efficiency. But again, auto-routing smooths over the rough edges if you are ok with a little bit of API spend when needed.
Why would ASI still have companies around? Seems like an inefficient way to turn the planet into computronium.
Better comparison is probably the dotcom bubble, and companies like pets.com, webvan, kozmo, etoys, worldcom, global crossing, etc. It was not entirely clear to most investors in 2001 that google and amazon would make it.
I don't think pets.com, an online retailer, is an apt comparison to Anthropic, an bleeding edge research lab doing things that most people would consider science fiction a few years ago.
Also no telling that AI will be more lucrative than some "boring" industry like power generation.
anthropic has come up with a lot of the core successful consumer facing tooling/standards around ai.
they invented mcp, terminal coding agent, context files like claude/agents.md.
I have no real clue whos going to win the model race but they have repeatedly shown that their internal taste is on point, reminds me a lot of early google.
With one huge difference: you could never run the Google index on your local PC. But you can run a 'good enough' AI on your local PC. Good enough that Sam Altman thinks you shouldn't do it because he has better uses for your RAM than you do (oh, and besides, because it may hurt his IPO chances).