There's only one core problem in AI worth solving for most startups building AI powered software: context.
No matter how good the AI gets, it can't answer about what it doesn't know. It can't perform a process for which it doesn't know the steps or the rules.
No LLM is going to know enough about some new drug in a pharma's pipeline, for example, because it doesn't know about the internal resources spread across multiple systems in an enterprise. (And if you've ever done a systems integration in any sufficiently large enterprise, you know that this is a "people problem" and usually not a technical problem).
I think the startups that succeed will understand that it all comes down to classic ETL: identify the source data, understand how to navigate systems integration, pre-process and organize the knowledge, train or fine-tune a model or have the right retrieval model to provide the context.
There's fundamentally no other way. AI is not magic; it can't know about trial ID 1354.006 except for what it was trained on and what it can search for. Even coding assistants like Cursor are really solving a problem of ETL/context and will always be. The code generation is the smaller part; getting it right requires providing the appropriate context.
This is why I strongly suspect that AI will not play out the way the Web did (upstarts unseat giants) and will instead play out like smartphones (giants entrench and balloon).
If all that matters is what you can put into context, then AI really isn't a product in most cases. The people selling models are actually just selling compute, so that space will be owned by the big clouds. The people selling applications are actually just packaging data, so that space will be owned by the people who already have big data in their segment: the big players in each industry. All competitors at this point know how important data is, and they're not going to sell it to a startup when they could package it up themselves. And most companies will prefer to just use features provided by the B2B companies they already trust, not trust a brand new company with all the same data.
I fully expect that almost all of the AI wins will take the form of features embedded in existing products that already have the data (like GitHub with Copilot), not brand new startups who have to try to convince companies to give them all their data for the first time.
The people selling models are actually just selling compute
Yes, fully agreed. Anything AI is discovering in your dataset could have been found by humans, and it could have been done by a more efficient program. But that would require humans to carefully study it and write the program. AI lets you skip the novel analysis of the data and writing custom programs by using a generalizable program that solves those steps for you by expending far more compute.
I see it as, AI could remove the most basic obstacle preventing us from applying compute to vast swathes of problems- and that’s the need to write a unique program for the problem at hand.
Yup. And it’s already playing out that way. Anthropic, OpenAI, Gemini - technically not an upstart. All have hyperscalers backing and subsidizing their model training (AWS, Azure, GCP, respectively). It’s difficult to discern where the segmentation between compute and models are here.
> It’s difficult to discern where the segmentation between compute and models are here.
Startups can outcompete the Foundational Model companies by concentrating on creating a very domain specific model, and providing support and services that comes out of having expertise in that specific domain.
This is why OpenAI chose to invest in Cybersecurity startups with Menlo Ventured in 2022 instead of building their own dedicated cybersecurity vertical, because a partnership driven model nets the most profit with the least resources expended.
This is the same reason why hyperscalers like Microsoft, Amazon, and Google themselves have ownership stakes in the foundational model companies like Anthropic, OpenAI, etc.
Foundational Models are a good first start, but are not 100% perfect in a number of fields and usecases. Ime, tooling built with these models are often used to cut down on headcount by 30-50% for the team using it to solve a specific problem. And this is why domain specific startups still thrive - sales, support, services, etc will still need to be tailored for buyers.
by concentrating on creating a very domain specific model
I don’t disagree with this from an economics perspective (it’s expensive running an FM to handle domain specific queries). But the most accurate domain knowledge always tends to involve internal data. And then it becomes the issue raised above: a people problem involving internal knowledge and data management.
Incumbent hyperscalers and vendors like MS, Amazon, etc (and even third party data managers like snowflake) tend to have more leverage when it becomes this type of data problem.
This problem will be eaten by OpenAI et al. the same way the careful prompting strategies used in 2022/2023 were eaten. In a few years we will have context lengths of 10M+ or online fine tuning, combined with agents that can proactively call APIs and navigate your desktop environment.
Providing all context will be little more than copying and pasting everything, or just letting the agent do its thing.
Super careful or complicated setups to filter and manage context probably won't be needed.
Context requires quadratic VRAM. It is why OpenAI hasn't even supported 200k context length yet for its 4o model.
Is there a trick that bypasses this scaling constraint while strictly preserving the attention quality? I suspect that most such tricks lead to performance loss while deep in the context.
I think you're downplaying how well Cursor is doing "code generation" relative to other products.
Cursor can do at least the following "actions":
* code generation
* file creation / deletion
* run terminal commands
* answer questions about a code base
I totally agree with you on ETL (it's a huge part of our product https://www.definite.app/), but the actions an agent takes are just as tricky to get right.
Before I give Cursor, I often doubt it's going to be able to pull it off and I constantly impressed by how deep it can go to complete a complex task.
So isn’t cursor just a tool for Claude or ChatGpt to use? Another example would be a flight booking engine. So why can’t an AI just talk direct to an IDE? This is hard as the process has changed, due to the human needing to be in the middle.
So Isn’t AI useless without the tools to manipulate?
Is it really that different to Claude with tools via MCP, or my own terminal-based gptme? (https://github.com/ErikBjare/gptme)
I thought it's basically a subset of Aider[0] bolted into a VS Code fork, and I remain confused as to why we're talking about it so much now, when we didn't about Aider before. Some kind of startup-friendly bias? I for one would prefer OSS to succeed in this space.
--
[0] - https://aider.chat/
[delayed]
I tried aider and had problems having it update code in existing files. Aider uses a search and replace pattern to update existing code. So you often end up with
>>>SEARCH
}
>>>REPLACE
}, {'more': 'data'}
[delayed]
Somewhat cynically, maybe because there's VC in Cursor.
https://techcrunch.com/2024/12/19/in-just-4-months-ai-coding...
Thanks for spreading the word. I hadn’t heard of Aider before and I’m now going to give it a try today.
Aider is the single best tool I’ve tried. And I’d never heard of it until like 2 weeks ago when someone mentioned it here. I love aider.
The irony is, it's sort of a household name on HN for over a year now, being way ahead of what was available commercially on the market - and yet, it seems most people here haven't heard of it.
(The author used to post a lot of insightful comments here about LLMs and other generative models, too.)
[delayed]
It’s not even just the lack of access to the data, so much hidden information to make decisions is not documented at all. It’s intuition, learned from doing something in a specific context for a long time and only a fraction of that context is accessible.
This is where Microsoft has the advantage, all those Teams calls can provide context.
I agree with you at this time, but there are a couple things I think will change this:
1. Agentic search can allow the model to identify what context is needed and retrieve the needed information (internally or externally through APIs or search)
2. I received an offer from OpenAI to give me free credits if I shared my API data with it, in other words, it is paying for industry specific data as they are probably fine tuning niche models.
There could be some exceptions to UI/UX going down specific verticals but eventually these fine tuning sector specific instances value will erode over time but this will likely occupy a niche since enterprise wants maximum configuration and more out of box solutions are oriented around SMEs.
It comes down to moats. Does OpenAI have a moat? It's leading the pack, but the competitors always seem to be catching up to it. We don't see network effects with it yet like with social networks, unless OpenAI introduces household robots for everyone or something, builds a leading marketshare in that segment, and the rich data from these household bots is enough training data that one can't replicate with a smaller robot fleet.
And AI is too fundamental of a technology that a "loss leader biggest wallet wins" strategy, used by the likes of Uber, will work.
API access can be restricted. Big part of why Twitter got authwalled was so that AI models can't train from it. Stack overflow added a no AI models clause to their free data dump releases (supposed to be CC licensed), they want to be paid if you use their data for AI models.
I don't think OpenAI have a moat in the traditional sense. Other players offer the exact same API so OpenAI can only win with permanent technical leadership. They may indeed be able to attain that but this is no Coca-Cola.
> Agentic search
what do you think about these guys: https://exa.ai/
Crawling web data is ETL. I think the case stands: the winners in AI/LLM SaaS startup space are the ones that really do ETL well. Whether that's ETL is across an enterprise data set or a codebase.
The AI and LLM are just the the "bake" button. If you want anything good, you still have to prep and put good ingredients in.
To your first point, the LLM still can’t know what it doesn’t know.
Just like you can’t google for a movie if you don’t know the genre, any scenes, or any actors in it, and AI can’t build its own context if it didn’t have good enough context already.
IMO that’s the point most agent frameworks miss. Piling on more LLM calls doesn’t fix the fundamental limitations.
TL;DR an LLM can’t magically make good context for itself.
I think you’re spot on with your second point. The big differentiators for big AI models will be data that’s not easy to google for and/or proprietary data.
Lucky they got all their data before people started caring.
Context is important but it takes about two weeks to build a context collection bot and integrate it into slack. The hard part is not technical, AIs can rapidly build a company specific and continually updated knowledge base, it's political. Getting a drug company to let you tap slack and email and docs etc is dauntingly difficult.
Difficult to impossible. Their vendors are already working on AI features, so why would they risk adding a new vendor when a vendor they've already approved will have substantially the same capabilities soon?
> No matter how good the AI gets, it can't answer about what it doesn't know. It can't perform a process for which it doesn't know the steps or the rules
This is exactly the motivation behind https://github.com/OpenAdaptAI/OpenAdapt: so that users can demonstrate their desktop workflows to AI models step by step (without worrying about their data being used by a corporation).
I found that fine-tuning and RAG can be replaced with tool calling for some specialized domains, e.g. real-time data. Even things like user's location can be tool called, so context can be obtained reliably. I also note that GPT-4o and better are smart enough to chain together different functions you give it, but not reliably. System prompting helps some, but the non-determinism of AI today is both awesome and a cure.
Tool calling is just systems integration with a different name. The job of the tool is still to provide context from some other system.
Wouldn't this just be foundational model + RAG in the limit?
And how does that differ from any person without that information?
I agree - busy building to solve that :)
Yeah seems like context is the AI version of cache invalidation, in the sense of the joke that "there's only 2 hard problems in computer science, cache invalidation and naming things". It all boils down to that (that, and naming things)
And off-by-one errors :)
And an almost fanatical devotion to the Pope
Also, there's only one hard problem in software engineering: people.
Seems to apply to AI as well.
To bake a cake from scratch, you must first recreate the universe
I think this argument only makes sense if you believe that AGI and/or unbounded AI agents are "right around the corner". For sure, we will progress in that direction, but when and if we truly get there–who knows?
If you believe, as I do, that these things are a lot further off than some people assume, I think there's plenty of time to build a successful business solving domain-specific workflows in the meantime, and eventually adapting the product as more general technology becomes available.
Let's say 25 years ago you had the idea to build a product that can now be solved more generally with LLMs–let's say a really effective spam filter. Even knowing what you know now, would it have been right at the time to say, "Nah, don't build that business, it will eventually be solved with some new technology?"
I don't think it's that binary. We've had a lot of progress over the last 25 years; much of it in the last two. AGI is not a well defined thing that people easily agree on. So, determining whether we have it or not is actually not that simple.
Mostly people either get bogged down into deep philosophical debates or simply start listing things that AI can and cannot do (and why they believe why that is the case). Some of those things are codified in benchmarks. And of course the list of stuff that AIs can't do is getting stuff removed from it on a regular basis at an accelerating rate. That acceleration is the problem. People don't deal well with adapting to exponentially changing trends.
At some arbitrary point when that list has a certain length, we may or may not have AGI. It really depends on your point of view. But of course, most people score poorly on the same benchmarks we use for testing AIs. There are some specific groups of things where they still do better. But also a lot of AI researchers working on those things.
Agreed. There's a difference between developing new AI, and developing applications of existing AI. The OP seems to blur this distinction a bit.
The original "Bitter Lesson" article referenced in the OP is about developing new AI. In that domain, its point makes sense. But for the reasons you describe, it hardly applies at all to applications of AI. I suppose it might apply to some, but they're exceptions.
You think it will be 25 years before we have a drop in replacement for most office jobs?
I think it will be less than 5 years.
You seem to be assuming that the rapid progress in AI will suddenly stop.
I think if you look at the history of compute, that is ridiculous. Making the models bigger or work more is making them smarter.
Even if there is no progress in scaling memristors or any exotic new paradigm, high speed memory organized to localize data in frequently used neural circuits and photonic interconnects surely have multiple orders of magnitude of scaling gains in the next several years.
I think you're suffering from some survivorship bias here. There are lot of technologies that don't work out.
Computation isn't one of them so far. Do you believe this is the end of computing efficiency improvements?
> You seem to be assuming that the rapid progress in AI will suddenly stop.
And you seem to assume that it will just continue for 5 years. We've already seen the plateau start. OpenAI has tacitly acknowledged that they don't know how to make a next generation model, and have been working on stepwise iteration for almost 2 years now.
Why should we project the rapid growth of 2021–2023 5 years into the future? It seems far more reasonable to project the growth of 2023–2025, which has been fast but not earth-shattering, and then also factor in the second derivative we've seen in that time and assume that it will actually continue to slow from here.
At this point, the lack of progress since April 2023 is really what is shocking.
I just looked on midjourney reddit to make sure I wasn't missing some new great model.
Instead what I notice is the small variations on the themes I have already seen a thousand times a year ago now. Midjourney is so limited in what it can actually produce.
I am really worried that all this is much closer to a parlor trick than AGI. "simple trick or demonstration that is used especially to entertain or amuse guests"
It all feels more and more like that to me than any kind of progress towards general intelligence.
> OpenAI has tacitly acknowledged that they don't know how to make a next generation model
Can you provide a source for this? I'm not super plugged into the space.
There's this [0]. But also o1/o3 is that acknowledgment. They're hitting the limits of scaling up models, so they've started scaling compute [1]. That is showing some promise, but it's nowhere near the rate of growth they were hitting while next gen models were buildable.
[0] https://www.wsj.com/tech/ai/openai-gpt5-orion-delays-639e769...
[1] https://techcrunch.com/2024/11/20/ai-scaling-laws-are-showin...
> You seem to be assuming that the rapid progress in AI will suddenly stop.
> I think if you look at the history of compute, that is ridiculous. Making the models bigger or work more is making them smarter.
It's better to talk about actual numbers to characterise progress and measure scaling:
" By scaling I usually mean the specific empirical curve from the 2020 OAI paper. To stay on this curve requires large increases in training data of equivalent quality to what was used to derive the scaling relationships. "[^2]
"I predicted last summer: 70% chance we fall off the LLM scaling curve because of data limits, in the next step beyond GPT4.
[…]
I would say the most plausible reason is because in order to get, say, another 10x in training data, people have started to resort either to synthetic data, so training data that's actually made up by models, or to lower quality data."[^0]
“There were extraordinary returns over the last three or four years as the Scaling Laws were getting going,” Dr. Hassabis said. “But we are no longer getting the same progress.”[^1]
---
[^0]: https://x.com/hsu_steve/status/1868027803868045529
o1 proved that synthetic data and inference time is a new ramp. There will be more challenges and more innovations. There is a lot of room in hardware, software, model training and model architecture left.
> There is a lot of room in hardware, software, model training and model architecture left.
Quantify this please? And make a firm prediction with approximate numbers/costs attached?
It's not realistic to make firm quantified predictions any more specific than what I have given.
We will likely see between 3 and 10000 times improvement in efficiency or IQ or speed of LLM reasoning in the next 5 years.
> It's not realistic to make firm quantified predictions any more specific than what I have given.
Then do you actually know what you're talking about or are you handwaving? I'm not trying to be offensive but business plans can't be made based on a lack of predictions.
> We will likely see between 3 and 10000 times improvement in efficiency or IQ or speed of LLM reasoning in the next 5 years
That variance is too large to take you seriously, unfortunately. That's unfortunate because I was really hoping you had an actionable insight for this discussion. :(
If I, for instance, tell my wife I can improve our income by 3x or 1000x but I don't really know, there's no planning that can be done and I'll probably have to sleep on the couch until I figure out what the hell I'm doing.
> business plans can't be made based on a lack of predictions.
They can. It's called "taking a risk". Which is what startups are about, right?
It's hard to give a specific prediction here (I'm leaning towards 10x-1000x in the next 5 years), but there's also no good reason to believe progress will stop, because a) there's many low and mid-hanging fruits to pick, as outlined by GP, and b) because it never did so far, so why would it stop now specifically?
We already have AGI in some ways though. Like I can use Claude for both generating code and helping with some maths problems and physics derivations.
It isn't a specific model for any of those problems, but a "general" intelligence.
Of course, it's not perfect, and it's obviously not sentient or conscious, etc. - but maybe general intelligence doesn't require or imply that at all?
For me, general intelligence from a computer will be achieved when it knows when it's wrong. You may say that humans also struggle with this, and I'd agree - but I think there's a difference between general intelligence and consciousness, as you said.
> AGI in some ways
In other words, just AI, not AGI.
I think one thing ignored here is the value of UX.
If a general AI model is a "drop-in remote worker", then UX matters not at all, of course. I would interact with such a system in the same way I would one of my colleagues and I would also give a high level of trust to such a system.
If the system still requires human supervision or works to augment a human worker's work (rather than replace it), then a specific tailored user interface can be very valuable, even if the product is mostly just a wrapper of an off-the-shelf model.
After all, many SaaS products could be built on top of a general CRM or ERP, yet we often find a vertical-focused UX has a lot to offer. You can see this in the AI space with a product like Julius.
The article seems to assume that most of the value brought by AI startups right now is adding domain-specific reliability, but I think there's plenty of room to build great experiences atop general models that will bring enduring value.
If and when we reach AGI (the drop-in remote worker referenced in the article), then I personally don't see how the vast majorities of companies - software and others - are relevant at all. That just seems like a different discussion, not one of business strategy.
The value of UX is being ignored, as the magical thinking has these AIs being fully autonomous, which will not work. The phrase "the devil's in the details" needs to be imprinted on everyone's screens, because the details of a "drop-in remote worker" are several Grand Canyons yet to be realized. This civilization is vastly more complex than you, dear reader, realize, and the majority of that complexity is not written down.
I guess part of the point is that the value of the UX will quickly start to decrease as more tasks or parts of tasks can be done without close supervision. And that is subject to the capabilities of the models which continues to improve.
I suggest that before we satisfy _everyone_'s definition of AGI, more and more people may decide we are there as their own job is automated.
The UX at that point, maybe in 5 or 10 or X years, might be a 3d avatar that pops up in your room via mixed reality glasses, talks to you, and then just fires off instructions to a small army of agents on your behalf.
Nvidia actually demoed something a little bit like that a few days ago. Except it lives on your computer screen and probably can't manage a lot of complex tasks on it's own. Yet.
Or maybe at some point it doesn't need sub agents and can just accomplish all of the tasks on its own. Based on the bitter lesson, specialized agents are probably going to have a limited lifetime as well.
But I think it's worth having the AGI discussion as part of this because it will be incremental.
Personally, I feel we must be pretty close to AGI because Claude can do a lot of my programming for me. I still have to make important suggestions, and routinely for obvious things, but it is much better at me at filling in all the details and has much broader knowledge.
And the models do keep getting more robust, so I seriously doubt that humans will be better programmers overall for much longer.
A drop in remote worker will still require their work to be checked and their access to the systems they need to do their work secured in case they are a bad actor.
It's a little depressing how many high valued startups are basically just wrappers around LLMs that they don't own. I'd be curious to see what percentage of YC latest batch is just this.
> 70% of Y Combinator’s Winter 2024 batch are AI startups. This is compared to -57% of YC Summer 2023 companies and ~32% from the Winter batch one year ago (YC W23).
The thinking is, the models will get better which will improve our product, but in reality, like the article states, the generalized models get better so your value add diminished as there's no need to fine tune.
On the other hand the crypto fund made a killing off of "me too" block chain technology before it got hammered again. So who knows about 2-5 year term but 10 year almost certainly won't have these billion dollar companies that are wrappers around LLMs
https://x.com/natashamalpani/status/1772609994610835505?mx=2
How is being a wrapper for LLMs you don’t own any different from being a company based on cloud infrastructure you don’t own?
LLMs are a platform.
Bill Gates definition of a platform was “A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it.”
I think the core problem at hand for people trying to use AI in user-facing production systems is "how can we build a reliable system on top of an unreliable (but capable) model?". I don't think that's the same problem that AI researchers are facing, so I'm not sure it's sound to use "bitter lesson" reasoning to dismiss the need for software engineering outright and replace it with "wait for better models".
The article sits on an assumption that if we just wait long enough, the unreliability of deep learning approaches to AI will just fade away and we'll have a full-on "drop-in remote worker". Is that a sound assumption?
Well. We were working on a search engine for industry suppliers since before the whole AI hype started (even applied to YC once), and hit a brick wall at some point were it got too hard to improve search result quality algorithmically. To understand what that means: We gathered lots of data points from different sources, tried to reconcile that into unified records, then find the best match for a given sourcing case based on that. But in a lot of cases, both the data wasn’t accurate enough to identify what a supplier was actually manufacturing, and the sourcing case itself wasn’t properly defined, because users found it too hard to come up with good keywords for their search.
Then, LLMs entered the stage. Suddenly, we became able to both derive vastly better output from the data we got, and also offer our users easier ways to describe what they were looking for, find good keywords automatically, and actually deliver helpful results!
This was only possible because AI augments our product well and really provides a benefit in that niche, something that would just not have been possible otherwise. If you plan on founding a company around AI, the best advice I can give you is to choose a problem that similarly benefits from AI, but does exist without it.
> the data wasn’t accurate enough to identify what a supplier was actually manufacturing
how did the LLM help with that challenge?
A guess: Their ability to infer reality from incomplete and indirect information.
The author discusses the problem from the point of engineering, not from business. When you look at it from business perspective, there is a big advantage of not waiting, and using whatever exists right now to solve the business problem, so that you can get traction, get funding, grab marketshare, build a team, and when the next day a better model will come, you can rewrite your code, and you would be in a much better position to leverage whatever new capabilities the new models provide; you know your users, you have the funds, you built the right UX...
The best strategy from your experience, is to jump on a problem as soon there is opportunity to solve it and generate lots of business value within the next 6 months. The trick is finding that subproblem that is worth a lot right now and could not be resolved 6 months ago. A couple of AI-sales startups "succeeded" quite well doing that (e.g. 11x), now they are in a good position to build from there (whether they will succeed in building a unicorn, that's another question, it just looks like they are in a good position now).
Controversial opinion: I don't believe in the bitter lesson. I just think that the current DNN+SGD approaches are just not that good at learning deep general expressive patterns. With less inductive bias the model memorizes a lot of scenarios and is able to emulate whatever real work scenario you are trying to make the model learn. However it fails to simulate this scenario well. So it's kind of misleading to say that it's generally better to have less inductive bias. That is only true if your model architecture and optimization approach are just a bit crap.
My second controversial point regarding AI research and startups: doing research sucks. It's risky business. You are not guaranteed success. If you make it, your competitors will be hot on your tail and you will have to keep improving all the time. I personally would rather leave the model building to someone else and focus more on building products with the available models. There are exceptions like finetuning for your specific product or training bespoke models for very specific tasks at hand.
>Eventually, you’ll just need to connect a model to a computer to solve most problems - no complex engineering required.
The word "eventually" is doing a lot of work here. Yes, it's true in the abstract, but over what time horizon? We have to build products to solve today's problems with today's technology, not wait for the generalized model that can do everything but may be decades away.
True, but it tells that if you are a founder of a niche AI company then you should take money out of it instead of investing everything back into the company, because eventually the generalist-AI will destroy your business and you will be left with nothing.
Based on the author's company that be founded, I assume he believes this technology is just years away.
I think with a lot of AI folk in San Francisco, this is a tacit assumption when having these sorts of conversations.
Anyone that thinks this is just years away is utterly ignoring human history, nature, and relationship with technology. My own view is that this will never be achieved, and it's not even just about the tech.
Let's imagine for a moment that this is even achieved. Then, there is still complex engineering required in the world: to maintain and continually improve the AI engines and their interfaces. Unless you want to say that, past some point, the AI will be self-improving without any human input whatsoever. Unless the AI can read our minds, I'm not sure it can continue to serve human interests without human input.
But, never mind, we will never get there. At this very moment, tech is capable of so much more, but most sites I visit have bad UI, are bloated downloading and executing massive amounts of JS, riddled with annoying ads that serve no real useful purpose to society, and riddled with bugs. Even as an engineer, I really struggle to find any good no-code tools to create anything truly sophisticated without digging into hard-core code. Heck, they are now talking about adding more HTTP methods to HTML forms.
I does feel like we are having the petfood.com moment in B2B AI. Bespoke solutions, bespoke offerings for very narrow B2B needs. Of course waiting around for AI to get so good that no bespoke solution is needed might be a bad strategy as well. I'm not sure how it will play out but I am certain there will be significant consolidation in the B2B agent space.
This might be true on a very long timescale, but that's not really relevant for VC's. Literally every single VC I've talked to raised the question if our moat is not just having better prompts, it's usually the first question. If a VC really invested in a company whose moat got evaporated by O1, that's on the VC. Everyone saw technology like O1 coming from a mile away.
For the slightly more complex stuff, sure at some point some general AI will probably be able to do it. But with two big caveats, the first being: when? and the second being: for how much?.
In theory every deep and wide enough neural network should be able to be trained to do object detection in images, yet no one is doing that. Technologies specifically designed to process images, like CNN's, reign supreme. Likewise for architectures of LLM's.
At some point your specialization might become obsolete, but that point might be a decade or more from now. Until then, specializations will have large economic and performance advantages making the advancements in AI today available to the industry of tomorrow.
I think it's the role of the VC to determine not if there's an AI breakthrough behind a startups technology, but if there's a market disruption and if that market disruption can be leveraged to establish a dominant company. Similar to how Google leveraged a small and easily replicable algorithmic advantage into becoming one of the most valuable companies on earth.
On your object detection point, Gemini 2.0 Flash has bounding box detection: https://ai.google.dev/gemini-api/docs/models/gemini-v2#bound....
I haven't found it to work particularly well for some more domain-specific things I tried, but it was surprisingly good for an LLM.
More computation cannot improve the quality or domain of data. Maybe the bitter lesson lesson is, lobby bitterly, for copyright laws that favor what you are doing, and weakened anti trust, to give you the insurmountable moat of exclusive data in a walled garden media network.
A human does not need billions of driving hours to learn how to drive competently. The issue with current method is not quality of data but methodology. More computation might unlock newer approaches that are better with less and worse quality data.
I think there's a more fundamental problem at play here: what seems to work in 'AI', search, is made better by throwing more data into more compute. You then store the results in a model, that amounts to pre-computed solutions waiting for a problem. Interacting with the model is then asking questions and getting answers that hopefully fit your needs.
So, what we're doing on the whole seems to be a lot of coding and decoding, hoping that the data used in training can be adequately mapped to the problem domain realities. That would mean that the model you end up with is somehow a valid representation of some form of knowledge about the problem domain. Trouble is, more text won't yield higher and higher resolution of some representation of the problem domain. After some point, you start to introduce noise.
A human is not a blank slate. There's millennia of evolutionary history that goes into making a brain adapted and capable of learning from its environment.
A human is a mostly blank slate...but it's a really sophisticated slate that as you say has taken many millions of years of development.
> A human does not need billions of driving hours to learn how to drive competently.
But humans DO need ~16 years of growth and development "to learn how to drive competently" and then will also know how to ride a bycycle, mow grass, build shelves, cook pizza, use a smart phone, ...! There's a lesson in that somewhere ....
You don't need the 16, you can get a much younger person to drive too. It only supports the fact that data amount/quality is not the problem.
You're forgetting a few billion years of evolution; we come with a fair amount of pretraining encoded in our DNA
It's not pretraining data that we have encoded in our DNA - it's an optimized learning/prediction architecture that is encoded ... how to build a brain that will then be able to learn efficiently.
The amount of knowledge/behavior that is innate - encoded in our DNA - is pretty minimal, consisting of things like opposite sex sexual attraction, fear of heights, fear of snakes, disgust at rotting smell, etc - basic survival stuff.
It is still years of continuous data input from a diverse sensory array
> It only supports the fact that data amount/quality is not the problem.
I agree with that. I.e., for
"drive competently."
the way humans learn it is more general, i.e., also brings the list I gave with bycycles, pizza, grass mowing, etc.
Also
"drive competently."
the way humans do it requires a lot of judgment, maturity which is not very well defined!
Guys, I don't understand how to make progress on AI. E.g., just asked X > Grok "similitude", and it gave a good answer, maybe like a long dictionary answer. But it looked like Grok had some some impressive plagiarism -- I can't tell the difference.
I disagree with the author based on timelines and VC behaviour. There is sufficient time to create a product and raise massive capital before the next massive breakthrough hands the value back to OpenAI/Google/Anthropic/MS. Secondly the execution of a solution in a vertical is sufficiently differentiating that even if the underlying problem could be solved by a next gen model there's very little reason to believe it will be. Big Cos don't have the interest to attack niches while there are billion user markets to go after. So don't build "photo share with AI", build "plant fungus photo share for farmers".
Yeah I can't make this article work as a day to day advice piece. In the time it takes to get a generational change in AI from computational resources, we might find a worthy result for your PhD, business, drug design, or killer application.
It can be simultaneously true that a tech is doomed to obsolescence from over specialization, and that it does incredibly useful things in the mean time.
Great essay. This is 100% right about the technical side, but I think it misses the “product” aspect.
Building a quality product (AI or otherwise), involves design and data at all levels: UX, on-boarding, marketing, etc. The companies learning important verticals and getting in the door with customers will have a pretty huge advantage as models get better. Both in terms of install base, and knowing what customers need. Really great products don’t simply do what a customer asks, but are built by taking to a ton of customers over and over, and solving their problems better than any one of them can articulate.
It’s true we will need less and less custom software for problems. But it isn’t realistic to say the software wrapper effort is going to zero when models improve.
Plus: a lot of software effort is needed for getting the data AI needs. This is going to be a huge area - think Google maps with satellites, camera cars, network effect products (ratings), data collection (maps, traffic), etc.
This all sounds very similar to the hundreds of excel/spreadsheet "killers" out there.
But excel math is easy to outperform, ChatGPT etc wont be.
this is not only not true , it has no basis's in reality. In the "real" world there are tradeoff's and constraints. scaling does not work infinitely , and products which are delivered by good engneering cultures have a non linear growth ( bad vs very good ).
comming back to research one of the frontier model's deepseek was able to come close to SOTA with a relatively small budget because of one of their mixture of experts approach.
Yeah knowing that having infinite perfect data and infinite compute to train an end-to-end DNN is the way to solve anything is great, but not exactly helpful when you don't have either of those things. Not to mention having to actually deploy it on a low power system in the end.
May be I'm being dumb but isn't the AI approach somewhat like brute force hacking of password? I mean humans don't learn that way - yes we do study code to be better coders but not in the way AI learning does?
Will someone truly discover a way to start from fundamentals and do better not just crunch zillions of petabytes faster and faster.
Or is that completely wrong? Happy to stand corrected
That's part of the problem though, isn't it? We still don't really understand how human intelligence works. We don't know how we learn. We don't even know where or how memories are stored.
We have ideas about how it works, sure. We know a bit about how the basics of the brain works and we know some correlations of what areas of the brain electrically light up in various conditions, but that's about the extent of it.
Its hard to really create an artificial version of something without pretty well understanding the real thing. In the meantime, brute forcing it is probably the best (and most common) approach.
Is it really the best approach though if we sink all this capital into it if it can never achieve AGI? It’s wildly expensive and if it doesn’t achieve all the lofty promises, it will be a large waste of resources IMO. I do think LLMs have use cases, but when I look at the current AI hype, the spend doesn’t match up with the returns. I think AI could achieve this, but not with a brute force like approach.
There's still even a more fundamental question before getting there, how are we defining AGI?
OpenAI defines it based on the economic value of output relative to humans. Historically it had a much less financially arrived definition and general expectation.
You really can't take anything OpenAI says about this kind of thing seriously at this point. It's all self-serving.
It's still important though, they are the ones many are expecting to lead the industry (whether that's an accurate expectation is surely up for debate).
Market will sort that out just like it did dotcom or tulip madness.
Another big push back is copyrighted content. Without proper revenue model how to pay for that?
That will also restrict what can be "learned". Already there's lawsuit, allegations of using pirated books etc
I'll be surprised if anything meaningful comes of those issues in the end.
Copyright issues here feel very similar to claims against Microsoft in the 80s and 90s.
Yes for sure AI or even basic computing prior to that has done wonders, chess being one example. Simply removing some of the issues with humans - inconsistency, errors due to mood or form etc.
I agree this approach is only viable one but I do hope the other way is also being tried. Who knows there may be a breakthrough. Like they try to create life from basic chemicals present in nature.
As long as people are not willing to acknowledge that we're not blank slates, but posses vast intelligence inherently, and that even the simplest life forms are infinitely more intelligent than LLMs, there won't be progress.
Is that something we can acknowledge without at least somewhat understanding how that works?
It sure seems like we have a lot of inherent knowledge, but by the book we're little more than the product of an instruction set that is our DNA and there's no explanation that includes inherited knowledge in that DNA.
I think the harder thing we need to acknowledge is that we understand much less than we think we know. Concepts like inherent, or collective, knowledge would all roll down hill from that.
I think the brilliant minds working on this know this. It shouldn't stop things just as we'll die one day doesn't stop accumulating wealth or working hard.
But at some point I hope there's breakthrough from any entirely different paradigm.
TFA admits that specialization can gain a temporary edge. But, says using that specialization is useless because the next gen will eclipse that edge using raw CPU.
Even if it is true that the next generational change in AI is based on computational improvements, how can it be true that it's hopeless to build products by specializing this generation of tech?
Moreover if I specialize gen 1, and gen 2 is similar enough, can't specialization maintain an edge?
There seems to be a timescale mismatch.
The smarter AI app founders know that more advanced AI will easily replace their current tech. What they're trying to do now is lock in a userbase.
The thing is, what do we do with the bitter lesson once we’re essentially ingesting the entire internet? More computation runs the risk of overfitting and there just isn’t any more data. Is the bitter lesson here telling me that we’ve basically maxed out?
> More computation runs the risk of overfitting and there just isn’t any more data.
At this scale you can't overfit. The model might not improve in a meaningful way, but it can't overfit because the amount of data is much much larger that the size of the model.
That said, as the state of the art open models show, the way to get better models is not "use more data", but use "more high quality data" (e.g. look at the graphs here comparing the datasets: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu). I don't know how much high quality data we can extract from the Internet (most of the data on the Internet is, as you'd guess, garbage, which is why aggressively filtering it can improve performance so much), but I'd wager we're still nowhere near running out.
There's also a ton of data that's not available on the public Internet in an easily scrapable form, or even at all; e.g. Anna's Archive apparently contains almost 1PB of data, and even that is (by their estimate) only ~5% of the world's books.
I'd think that this only means that a model cannot suffer from overfitting on average. So, it might totally have been overfitted on your specific problem.
We’re nowhere near ingesting the whole internet.
Though personally, I think we’re missing whatever architecture / mathematical breakthrough will make online learning (or even offline incremental, I.e. dreams) work.
At that point we could give the AI a robot body and train it of lived experience.
> "We’re nowhere near ingesting the whole internet."
We don't need to ingest the whole internet. I'd wager that upwards of 75% of the internet is spam, which would be useless for LLM training purposes. By the way, spam and useless information on the internet is only going to get worse, largely thanks to LLMs.
Only a subset of the internet contains "useful" information, an even a smaller subset contains information which is "clean enough" to be used for training purposes, and an even smaller subset can be legally scraped and used for training purposes.
It's highly likely that we've reached "peak training data" a long time ago, for many areas of knowledge and activities which are available on the internet.
I think this is largely right. I look a this space as somebody that plugs together bits and pieces of software components for a living for a few decades. I don't need to deeply understand how each of those things work to be effective. I just need to know how to use them.
From that point of view, AI is more of the same. I've done a few things with the OpenAI apis. Easy work. There's not much to it. Scarily simple actually. With the right tools and frameworks we are talking a few lines of code mostly. The rest is just the usual window dressing you need to turn that into an app or service. And LLMs can generate a lot of that these days.
The worry for VC funded companies in this space is that a lot of stuff is becoming a commodity. For example, the llama and phi models are pretty decent. And you can run them yourself. Claude and OpenAI are a bit better and larger so you can't run them yourself. But increasingly those cheap models that you can run yourself are actually good enough for a lot of things. Model quality is a really hard to defend moat long term. Mostly the advantage is temporary. And most use cases don't actually need a best in class LLM.
So, I'm not a believer in the classic winner takes all approach here where one company turns into this trillion dollar behemoth and the rest of the industry pays the tax to that one company in perpetuity. I don't see that happening. The reality already is that the richest company in this space is selling hardware, not models. Nvidia has a nice (temporary) moat. The point of selling hardware is that you want many customers. Not just a few. And training requires more hardware than inference. So, Nvidia is rich because there are a lot of companies busy training models.
For me the issue has been that local models seem to be pretty bad (and it’s entirely possible it’s a user error on my end) compared to calling gpt-4o.
And even gpt-4o needs constant attention to ensure it doesn’t analyze input in a haphazard way.
I made a simple AI app for myself where I ran a bunch of recipes I’ve saved over the years through batch analysis. It tagged recipes with “kosher salt” as kosher lol. I had to baby sit the prompt for this simple pet project analysis for like 4 hours until I felt confident enough. And even then I was at maybe 60%.
Now imagine for a business application. This kind of incorrect analysis would be detrimental.
> So, I'm not a believer in the classic winner takes all approach here where one company turns into this trillion dollar behemoth and the rest of the industry pays the tax to that one company in perpetuity.
I agree with this sentiment. There are a lot of frontier model players that are very competent (OpenAI, Anthropic, Google, Amazon, DeepSeek, xAI) and I'm sure more will come onboard as we find ways to make models smaller and smaller.
The mental framework I try to use is that AI is this weird technology that is an enabler of a lot of downstream technology, with the best economic analogy being electricity. It'll change our society in very radical ways, but it's unclear who's going to make money off of it. In the electricity era Westinghouse and GE emerged as the behemoths because of their ability to manufacture massive turbines (which are the equivalent of today's NVIDIA and perhaps Google).
I think there is value in companies built around AI. The value comes from UX, proprietary supplementary datasets, and market capture. Businesses built now will be best positioned to take advantage of future improvements in general AI. That is why I am building in the AI space. I’m not naive to the predictable improvement in foundational models.
I agree the article presents good points specially if you consider current LLM models and the business models (and questionable business practices) being used.
It seems unlikely at this point, but it it might be the case that new methods, models and/or algorithms rise due to the trend of having high expectations/investments on this area
But yes, AI business stuff will be Swallowed by those companies as much as the companies that unwillingly are providing content to those companies.
Of course, 01 is a lot more expensive than smaller models. So the startups that have spent time on tuning their prompts May yet get the last laugh, at least in any competitive market where customers are price sensitive. Bear in mind that supposedly OpenAI is losing money even at a $200 a month price point so it's unclear that the current cost structure of model access is genuinely sustainable.
Conceptually that seems right, but think there is a decade+ worth of runway left on working in the "glue" space. i.e. connecting the AI and our lived realities.
I don't see the general-ness of AI overcoming the challenges there...cause there is a lot of non-obvious messiness there.
I feel like the author glosses over some nuance in the point he is trying to make and his conclusion doesn't incorporate the nuance.
The nuance is that even though AI researchers have learned this lesson, they are still building purpose built AIs because it's not possible to build an AI that can learn to do anything (i.e. AGI). Therefore, building on top of an existing AI model to meet a vertical market demand is not that crazy.
It's the same risk when building on any software platform. That is the provider of the platform may add the feature/app you are building as a free addition and your business becomes obsolete.
love it when a 25 year old founder says we have been here multiple times in the past
In which specific ways are they wrong, though?
Being able to learn from others' mistakes is generally considered to be a sign of aptitude.
s/25 year old founder/25 yr old first-time founder/
So what is the appropriate course of action if you are a founder? Just wait until the models inevitably get better, or help a customer with their problem now?
Computer power has been increasing all the time, but that hasn't kept people from experimenting with the limited power of their time (which ultimately led to better solutions) rather than waiting for the more powerful machines.
Find a real problem and solve it cheaply, or solve it so much better than anyone else that your high price is worthwhile to enough people.
Don't focus on the method of solving the problem until you have identified the problem and all the current solutions and avoidances.
You can help the customer now, but you should keep in mind that the people working on the more general solutions will at some point take your moat.
E.g. you can build a drawing program where you can tell it in natural language to apply certain effects and make some changes. But another company might be working on a OS assistant that can help the user in any program currently running on the computer.
Or you can build a robot that can pick bolts from an assembly line, but at some point there will be a company building a cheap robot that can do anything from folding t-shirts to knitting a sweater.
This was my takeaway from the article too.
In order to win you often have to start before the problem is fully solved and then count on it being solved better as you build and scale.
Thus starting with engineering effort to fit some of the AI limitations make sense but realize many of those will be temporary and will be replaced in the future.
But there is always a new tech or framework or something that emerges after you start and adopting it will improve your product measurably.
I'm not an AI founder, but I'd say there is still some value to be added by better architectures.
These AI wrappers run ship engines on a kayak. For example, most coding companion's ignore my code base the moment I close other files. Somehow their context is minimal. Such systems can be improved dramatically just by changing the software around the LLM.
But I get it, you have to be quick and don't waste time on MVPs. If you get critical mass, you can add that later... hopefully
> We’ve seen this pattern in speech recognition, computer chess, and computer vision.
I think the bitter lesson is true for AI researchers, but OP overstates it's relevance to actual products. For example the best chess engines are very much still very much specific to chess. They incorporate more neural networks now, but they are still quite specific.
There is a circular argument going on in this article. Basically:
> "Flexible is better. Why? Because specific has been consistently worse"
I mean, I don't deny the historical trend. But really, why is it so? It makes sense to follow the trend, but knowing more about the reason why would be cool.
Also, I feel that the human cognitive aspects of "engineering some shit" are being ignored.
People are engineering solutions not only to be efficient, but to get to specific vantage points in which they can see further. They do it so they can see what the "next gen flexible stuff" looks like before others.
Finally, it assumes the option to scale computation is always available and ignores the diminishing returns of trying to scale vanguard technology.
The scale requirements for AI stuff are getting silly real fast due to this unshakable belief in infinite scaling. To me, they're already too silly. Maybe we need to cool down, engineer some stuff and figure out where the comfortable treshold lies.
They will learn their lesson how exactly? By getting money for free from VCs?
Remember, most of these startups are grifters. Only few of them really believe in their products.
> “The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin”.
> He points out that throughout AI’s history, researchers have repeatedly tried to improve systems by building in human domain knowledge. The “bitter” part of the title comes from what happens next: systems that simply use more computing power end up outperforming these carefully crafted solutions. We’ve seen this pattern in speech recognition, computer chess, and computer vision. If Sutton wrote his essay today, he’d likely add generative AI to that list. And he warns us: this pattern isn’t finished playing out.
According to Chomsky (recalled in relatively recent years for him) this is why he didn’t want to work on the AI/linguistics intersection when someone asked him in the mid 50’s. Because he thought that successful approaches would just use machine learning and have nothing to do with linguistics (that he cares about).
Seems intellectually dishonest (given how his theory of universal grammar is all about indispensible biological factors in human languages).
AI founders will also make sure that users - the ones that are not capable of telling if you're lying to them by taking their money and energy - will learn the bitter lesson of taking it right up the arse. Again. Just like a repetition of history. Humanity is like ever before caught in the action of serving the individuals that made us fuck ourselves over. And the only thing you do is thanking them for it by applying to their tactics. Middle class humanity and above is morally bankrupt, and AI is the next thing they will fight over and with, but of course without adding anything positive to the world.
Cool article, a lot more technical and informational than I thought from the headline.
The article gave the TL;DR as below, for those who skip to the comments:
Historically, general approaches always win in AI.
Founders in AI application space now repeat the mistakes AI researchers made in the past.
Better AI models will enable general purpose AI applications. At the same time, the added value of the software around the AI model will diminish.This assumes that the AI bros care about building useful products. They don't for the most part, they care only about building a startup that can plausibly get VC funding. So what if in five years your LLM on the blackchain app fails, you got to blow throw a few million dollars over a few years going around to conferences and living the high life.
That's a success in anyone's book.