I've found that everything that works stops being called AI.
Logic programming? AI until SQL came out. Now it's not AI.
OCR, computer algebra systems, voice recognition, checkers, machine translation, go, natural language search.
All solved, all not AI any more yet all were AI before they got solved by AI researchers.
There's even a name for it: https://en.m.wikipedia.org/wiki/AI_effect?utm_source=perplex...
On an amusing note, I've read something similar: Everything that works stops being called philosophy. Science and math being the two familiar examples.
We've kind of done it in reverse with AI though - 'chatbots' have been universally shit for ages, and now we have good ones, but they're 'AI'.
I remember the original Lisp manual describes it as a symbolic language for “AI”
Lisp's inventor, John McCarthy, was an AI researcher. (The US government started funding AI research in the 1950s, expecting progress to be much faster than it actually was.)
> Logic programming? AI until SQL came out. Now it's not AI.
logic programming is not directly linked to SQL, and has its own AI term now: https://en.wikipedia.org/wiki/GOFAI.
The original SQL was essentially Prolog restricted to relational algebra and tuple relational calculus. SQL as is happened when a lot of cruft was added to the mathematical core.
I think when things get really good, which I've no doubt will happen, we'll call it SI; Synthetic Intelligence
But it may be a while.
Bert is already not an LLM and the vector embedding it generates are not AI. It is also first general solution for natural language search anyone has come up with. We call them vector databases. Again I'd wager this is because they actually work.
Totally agree!
That pretty much expounds and articulates my thoughts on that subject.
Just don't use the word Champaign anywhere near a Miller Highlife in France. I think they're digging up old guillotines for that.
Edit: Your Dijkstra quote may be missing an "is":
..the question whether machines can think is as relevant
That's a very interesting phenomenon!
It seems very difficult to build a moat around a product when the product is supposed to be a generally capable tool and the input is English text. The more truly generally intelligent these models get the more interchangeable they become. It's too easy to swap one out for another.
This article, and all the articles like it, are missing most of the puzzle.
Models don’t just compete on capability. Over the last year we’ve seen models and vendors differentiate along a number of lines in addition to capability:
- Safety
- UX
- Multi-modality
- Reliability
- Embeddability
And much more. Customers care about capability, but that’s like saying car owners care about horsepower — it’s a part of the choice but not the only piece.
One somewhat obsessive customer here: I pay for and use Claude, ChatGPT, Gemini, Perplexity, and one or two others.
The UX differences among the models are indeed becoming clearer and more important. Claude’s Artifacts and Projects are really handy as is ChatGPT’s Advanced Voice mode. Perplexity is great when I need a summary of recent events. Google isn’t charging for it yet, but NotebookLM is very useful in its own way as well.
When I test the underlying models directly, it’s hard for me to be sure which is better for my purposes. But those add-on features make a clear differentiation between the providers, and I can easily see consumers choosing one or another based on them.
I haven’t been following recent developments in the companies’ APIs, but I imagine that they are trying to differentiate themselves there as well.
To me, the vast majority of "consumers" as in B2C only care about price, specifically free. Pro and enterprise customers may be more focused on the capabilities you listed, but the B2C crowd is vastly in the free tier only space when it comes to GenAI.
You may be forgetting that ChatGPT has 10M paying customers. Not to mention everyone that pays for Claude Pro, Perplexity Pro, and so on.
The math on this doesn't work.
…go on?
> OpenAI has 10M paying customers
According to who?
> OpenAI COO Says ChatGPT Passed 11 Million Paying Subscribers
https://www.theinformation.com/articles/openai-coo-says-chat...
Not OP but what is your guess? Bloomberg says 1M customers in the business plan [1].
[1] https://www.bloomberg.com/news/articles/2024-09-05/openai-hi...
This is like when VCs were funding all kinds of ride share, bike share, food delivery, cannabis delivery, and burning money so everyone gets subsidized stuff while the market figures out wtf is going on.
I love it. More goodies for us
> when VCs were funding all kinds of ride share, bike share, food delivery, cannabis delivery, and burning money so everyone gets subsidized stuff while the market figures out wtf is going on
I’m reminded of slime molds solving mazes [1]. In essence, VC allows entrepreneurs to explore the solution space aggressively. Once solutions are found, resources are trimmed.
[1] https://www.mbl.edu/news/how-can-slime-mold-solve-maze-physi...
VC is the worst possible way to fund entrepeneurs.
Except for all the others.
VC is good for high-risk, capital-intensive, scalable bets. The high risk and scalability cancel out, thereby leaving the core of finance: lending to enable economics of scale.
Plenty of entrepreneurship is low to moderate risk, bootstrappable and/or unscalable. That describes where VC is a raw deal. It also does not describe AI.
No, it means creating a bunch of unprofitable businesses that make it really hard for folks trying to build a sustainable business without VC money.
Yep, you will probably lose. The VCs aren't out there to advance the technology. They are there to lay down bets on who's going to be the winner. "Winner" has little to do with quality, and rides much more on being the one that just happens to resonate with people.
The ones without money will usually lose because they get less opportunity to get in front of eyeballs. Occasionally they manage it anyway, because despite the myth that the VCs love to tell, they aren't really great at finding and promulgating the best tech.
> that make it really hard for folks trying to build a sustainable business without VC money
LLMs are capital intensive. They’re a natural fit for financing.
I'm already keeping an eye on what NVidia gets into next... because that will inevitably be the "Next big thing". This is the third(ish) round of this pattern that I can recall, I'm probably wrong about the exact count, but NVidia is really good at figuring out how to be powering the "Next big thing". So alternatively... I should probably invest in the utilities powering whatever Datacenters are using the powerhungry monsters at the center of it all.
There's a saying in the stock market that probably applies here: past performance does not indicate future performance.
Getting lucky twice is a row is really really lucky. Getting lucky three times in a row is not more likely because they were lucky two times in a row
It may be getting lucky, or it may be that they have really great leadership.
Very few other large tech companies have deep technical competence at CEO level leadership
What goodies can I get from AI companies though?
Your own GPL unencumbered regurgitations of popular GPL libraries and applications.
Except that copyright law doesn’t work that way.
At least OAI and MSFT offer "copyright shields" for enterprise customers.
Say what you want about the morals associated to said offerings, but it sounds very interesting for companies that may want to use a plagiarizing machine for copying GPL code while covering their asses in some way (at least in terms of cost).
Where I live the ridesharing/delivering startups didn't bring goodies, they just made everything worse.
They destroyed the Taxi industry, I used to be able to just walk out to the taxi rank and get in the first taxi, but not anymore. Now I have to organize it on an app or with a phone call to a robot, then wait for the car to arrive, and finally I have to find the car among all the others that other people called.
Food delivery used to be done by the restaurants own delivery staff, it was fast, reliable and often free if ordering for 2+ people. Now it always costs extra, and there are even more fees if I want the food while it's still hot. Zero care is taken with the delivery, food/drinks are not kept upright and can be a total mess on arrival. Sometimes it's escaped the container and is just in the plastic bag. I have ended up preferring to go pickup food myself over getting it delivered, even when I have a migraine, it's just gone to shit.
That's exactly the short term thinking they're hoping they can use to distract.
Tech companies purchased television away from legacy media companies and added (1) unskippable ads, (2) surveillance, (3) censorship and revocation of media you don't physically own, and now they're testing (4) ads while shows are paused.
There's no excuse for getting fooled again.
Agree completely.
Monetizing all of this is frankly...not my problem.
>Therefore, if you are OpenAI, Anthropic, or another AI vendor, you have two choices. Your first is to spend enormous amounts of money to stay ahead of the market. This seems very risky though
Regarding point 6, Amazon invested heavily in building data centers across the US to enhance customer service and maintain a competitive edge. it was risky.
This strategic move resulted in a significant surplus of computing power, which Amazon successfully monetized. In fact, it became the company's largest profit generator.
After all, startups and businesses is all about taking risk, ain't it?
No, this an incorrect analogy on most points. Amazon retail marketplaces are absolutely not correlated with distributed data centers. Most markets are served out of a single cluster, which is likely to be in a different country; eg historically all of Europe from dublin, the americas from virginia, and asia pacific from seattle/portland.
The move of the retail marketplace hosting from seattle to virginia happened alongside, and continued long after, the start of AWS.
It is an utter myth, not promoted by Amazon, that AWS was some scheme to use “surplus computing power” from retail hosting/operations. It was an intentional business case to get in to a different B2B market as a service provider.
The top of the nasdaq is full of companies that build computers (from phones to data centers), not companies that only do algorithms.
A clever AI algorithm run on rented compute is not a moat.
> If the proprietary models stop moving forward, the open source ones will quickly close the gap.
This is the Red Queen hypothesis in evolution. You have to keep running faster just to stay in place.
On it's face, this does seem like a sound argument that all the $$ following LLMs is irrational:
1. No matter how many billions you pour into your model, you're only ever, say, six months away from a competitor building a model that's just about as good. And so you already know you're going to need to spend an increased number of billions next year.
2. Like the gambler who tries to beat the house by doubling his bet each time, at some point there must be a number where that many billions is considered irrational by everybody.
3. Therefore it seems irrational to start putting in even the fewer billions of dollars now, knowing the above two points.
> it seems irrational to start putting in even the fewer billions of dollars now, knowing the above two points
This doesn’t follow. One, there are cash flows you can extract in the interim—standing in place is potentially lucrative.
And two, we don’t know if the curve continues until infinity or asymptotes. If it asymptotes, being the first at the asymptote means owning a profitable market. If it doesn’t, you’re going to get AGI.
Side note: bought but haven’t yet read The Red Queen. Believe it was a comment of yours that lead me to it.
Why would being the first at the (horizontal) asymptote mean owning a profitable market? In six months, somebody else is at the asymptote as well. If different models are mostly interchangable quality-wise, then the decision will mostly be made on price.
For a market to be both winner-take-all as well as lucrative, you need some kind of a feedback cycle from network effets, economies of scale, or maybe even really brutal lock-in. For example, for operating systems applications provide a network effect -- more apps means an OS will do better, a higher install base for an OS means it'll attract more apps. For social networks it's users.
One could imagine this being the case for LLMs; e.g. that LLMs with a lot of users can improve faster. But if the asymptote has been reached, then by definition there's no more improvement to be had, and none of this matters.
> In six months, somebody else is at the asymptote as well
But you were there first. That gives you all of a first mover’s advantages plus the profits from that period to set the terms of the market sharing.
None of this requires winner-takes-all economics. And given OpenAI’s revenues, it’s unclear they need to be the sole winner to make an investment thesis work.
ChatGPT benefits from network effects, where user feedback on the quality of its answers helps improve the model over time. This reduces its reliance on external services like ScaleAI, lowering development costs.
Larger user base = increased feedback = improved quality of answers = moat
The competition for big LLM AI companies is not other big LLM AI companies, but rather small LLM AI companies with good enough models. This is a classic innovator dilemma. For example, I can imagine a team of cardiologists creating a fine tune LLM model.
What on earth would cardiologists use a Large Language Model for, except drafting fluff for journals?
Safely and effectively, that is. Dangerous and inappropriate is obviously a much wider set of possibilities.
Companies in the business of building models are forced to innovate on two things at once.
1. Training the next generation of models
2. Providing worldwide scalable infrastructure to serve those models (ideally at a profit)
It's hard enough to accomplish #1, without worrying about competing against the hyperscalers on #2. I think we'll see large licensing deals (similar to Anthropic + AWS, OpenAI + Azure) as one of the primary income sources for the model providers.
With the second (and higher margin) being user facing subscriptions. Right now 70% of OpenAI's revenue comes from chatgpt + enterprise gpt. I imagine Anthropic is similar, given the amount of investment in their generative UI. At the end of the day, model providers might just be consumer companies.
Having been there for the dotcom boom and bust from pre-IPO Netscape to the brutal collapse of the market, it’s hard to say dotcoms don’t work. There was clearly something there of immense value, but it took a lot experimentation with business models and maturation of technology as well as the fundamental communications infrastructure of the planet. All told it feels like we’ve really only gained a smooth groove in the last 10 years.
I see no reason why AI will be particularly different. It seems difficult to make the case AI is useless, but it’s also not particularly mature with respect to fundamental models, tool chains, business models, even infrastructure.
In both cases speculative capital flowed into the entire industry, which brought us losers like pets.com but winners like Amazon.com, Netflix.com, Google.com, etc. Which of the AI companies today are the next generation of winners and losers? Who knows. And when the music stops will there be a massive reckoning? I hope not, but it’s always possible. It probably depends on how fast we converge to “what works,” how many grifters there are, how sophisticated equity investors are (and they are much more sophisticated now than they were in 1997), etc.
AI as a work force could be comparable to interns. They work in a 7x24 shift but fail the task from time to time.
I get the sense that the value prop of LLMs should first be cut into two categories: coding assistant, and everything else.
LLMs as coding assistants seem to be great. Let’s say that every working programmer will need an account and will pay $10/month (or their employer will).. what’s a fair comp for valuation? GitHub? That’s about $10Bn. Atlassian? $50Bn
The “everything else” bin is hard to pin down. There are some clear automation opportunities in legal, HR/hiring, customer service, and a few other fields - things that feel like $1-$10Bn opportunities.
Sure, the costs are atrocious, but what’s the revenue story?
> I get the sense that the value prop of LLMs should first be cut into two categories: coding assistant, and everything else.
Replace coding assistant with artists and you have the vibe of AI 2 years ago.
The issue is that these models are easy to make (if expensive) so the open source community (of which many, maybe most, are programmers themselves) will likely eat up any performance moat given enough time.
This story already played out with AI art. Nothing beats SD and comfyUI if you really need high quality and control.
It is ironic that this article seems to focus on the business logic, considering that is the same myopia at these AI companies.
Not that physical/financial constraints are unimportant, but they often can be mitigated in other ways.
Some background: I was previously at one of these companies that got hoovered up in the past couple years by the bigs. My job was sort of squishy, but it could be summarized as 'brand manager' insofar as it was my job to aide in shaping the actual tone, behaviors, and personality of our particular product.
I tell you this because in full disclosure, I see the world through the product/marketing lens as opposed to the engineer lens.
They did not get it.
And by they I mean founds whose names you've heard of, people with absolute LOADS of experience in building a shipping technology products. There were no technical or budgetary constraints at this early stage, we were moving fast and trying shit. But they simply could not understand why we needed to differentiate and how that'd make us more competitive.
I imagine many technology companies go through this, and I don't blame technical founders who are paranoid about this stuff; it sounds like 'management bullshit' and a lot of it is, but at some point all organizations who break even or take on investors are going to be answerable to the market, and that means leaving no stone unturned in acquiring users and new revenue streams.
All of that to say, I do think a lot of these AI companies have yet to realize that there's a lot to be done user experience-wise. The interface alone - a text prompt(!?) is crazy out-of-touch to me. The fact that average users have no idea how to set up a good prompt and how hard everyone is making it for them to learn about that.
All of these decisions are pretty clearly made by someone who is technology-oriented, not user-oriented. There's no work I'm aware of being done on tone, or personality frameworks, or linguistics, or characterization.
Is the LLM high on numeracy? Is it doing code switching/matching, and should it? How is it qualifying its answers by way of accuracy in a way that aids the user learning how to prompt for improved accuracy? What about humor or style?
It just completely flew over everyone's heads. This may have been my fault. But I do think that the constraints you see to growth and durability of these companies will come down to how they're able to build a moat using strategies that don't require $$$$ and that cannot be easily replicated by competition.
Nobody is sitting in the seats at Macworld stomping their feet for Sam Altman. A big part of that is giving customers more than specs or fiddly features.
These companies need to start building a brand fast.
#2 is dead wrong, and shows that the author is not aware of the current exciting research happening in parameter efficient fine-tuning or representation/activation engineering space.
The idea that you need huge amounts of compute to innovate in a world of model merging and activation engineering shows a failure of imagination, not a failure to have the necessary resources.
PyReft, Golden Gate Claude (Steering/Control Vectors), Orthogonalization/Abliteration, and the hundreds of thousands of Lora and other adapters available on websites like civit.ai is proof that the author doesn't know what they're talking about re: point #2.
And I'm not even talking about the massive software/hardware improvements we are seeing for training/inference performance. I don't even need that, I just need evidence that we can massively improve off the shelf models with almost no compute resources, which I have.
I would say, without qualification, "no". https://www.techpolicy.press/challenging-the-myths-of-genera...
the hype will never die. all the smartest people in industry and government believe that there is a very high probability that this technology is near the edge of starting the AGI landslide. you dont need AGI to start the AGI landslide, you just need AI tools that are smart enough to automate the process of discovering and building the first AGI models. every conceivable heuristic indicates that we are near the edge. and because of this, AI has now become a matter of national security. the research and investment wont stop, because it cant, because it is now an arms race. this wont just fizzle out. it will be probed and investigated to absolute exhaustion before anyone feels safe enough to stop participating in the race. if you have been keeping up you will know that high level federal bureaucrats are now directly involved in openAI.
I am not among those smartest, so take my opinion with a mountain of salt. But I'm just not convinced that this is going in the direction of AGI.
The recent advances are truly jaw dropping. It absolutely merits being investigated to the hilt. There is a very good chance that it will end up being a net profit.
But intuitively they don't feel to me like they're getting more human. If anything I feel like the recent round of "get it to reason aloud" is the opposite of what makes "general intelligence" a thing. The vast majority of human behavior isn't reasoned, aloud or otherwise.
It'll be super cool if I'm wrong and we're just one algorithm or extra data set or scale factor of CPUs away from It, whatever It turns out to be. My intuition here isn't worth much. But I wouldn't be surprised if it was right despite that.
AGI is PR. The money is in automating low-level customer service, coding and data science.
can you share some of these heuristics you referred to?
the turing test