Why AI Infrastructure Startups Are Insanely Hard to Build
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VC pouring money in LLM infra is legitimately crazy to me. It's clear as day that there will be winners of this AI cycle, but, as always, they will be companies that provide actual, real, tangible value. Making shovels works for huge companies like Nvidia or Intel, but it won't work for you. It's sad to see so much capital funneled in frameworks upon frameworks upon frameworks instead of fresh new ideas that could revolutionize the way we interact with our devices. I know it's a bit of a meme, but I'd rather see more Rabbit R1 and less LangChain.
Even OpenAI doesn't really have a product. Just throwing data at a bunch of video cards isn't value-generating in itself. We need a Dropbox or a Slack or an Instagram: something people love that makes their life easier or better.
They are making a ton of money off subscriptions.
Specially given the rest of GP comment, OpenAI seems to be the Google, Facebook of the industry and will be the infrastructure company of it (already is kind of)
People seem to not mind ChatGPT or Claude and safe to say that a very large majority of AI products are using one of the APIs of those companies.
The UX of the AI applications is the moat and the infrastructure providers behind those applications is pretty much always OpenAI and Anthropic at the moment because running your own open source LLMs (which are inferior out of the box) at scale is not cheap or easy to do it right - same reason most companies use cloud. Agree that once you hit super scale then you can run your own infrastructure but there are thousand of companies who won’t get that far and still need an LLM.
These things are products.
> you would just eventually train and deploy your own model
Just train an LLM? It's really not that easy! Even if it was, it'd be like how people can "just" run their own email service. Hosted email API is not rocket science but in practice companies all choose to pay Microsoft or Google to do it. Doing these things isn't a core competitive advantage so it gets outsourced.
> The fact that Meta rather inexplicably chooses to give away assets that cost millions or billions to create doesn't mean LLMs aren't a product
EDIT
Your last paragraph makes an excellent point. In the near future, I could see big corps paying OpenAI (or a competitor) to train a private LLM on their squillion internal documents and build a very good helpdesk agent. (Legal and compliance would love it.)
Giving expensive things away for free is a great marketing technique that has been used since time immemorial, so why startups like Stability do it is somewhat understandable. And OpenAI uses free API access as a loss leader for their API product so that's understandable too.
Why Meta/Google/others do open weight releases is a bit less clear. Recall though that the first Llama wasn't really an open source release. You had to sign a document saying you were a researcher to get the weights, and that document was an agreement to keep the weights secret. Two people signed the documents, anonymously compared their weights, discovered they weren't watermarked (i.e. Meta didn't take this seriously, it was a sop to their AI politics/safety people) and promptly leaked them.
Presumably this was useful for the more libertarian wing of Meta as they could then prove the sky wouldn't fall, and so the influence shifted towards those arguing for more openness in research in general. With that Rubicon crossed other companies didn't see competitive advantage in withholding their similar sized models anymore and followed the leader, so to speak.
Sometimes it also feels like Meta may have over-purchased GPUs and - lacking a public cloud - have just decided to let their researchers do what they wanted. Which is great for the public! But we mustn't be too overconfident. This is really only possible because of Zuckerberg's unique corporate structure that makes him unfirable, combined with Meta being a big data company. It's really benefiting all of humanity here because he's invulnerable to board action so doesn't have to worry about heat from shareholders over 'wasting' money like this.
There's a lot of R&D being done right now on shrinking models whilst preserving quality, so hopefully the Zuck's generosity is enough to ride the open AI research community through the hard times when you needed billions to train LLMs.
Then they don't suck as much.
With OpenAI and Claude, you throw some text instructions and you get back the answers which are surprisingly correct (minus a few exceptions). In order to replicate that with Llama you'd probably need N-hundreds finetunes and a model to decide which finetunes to use.
Well, you are humbly wrong then.
> You would just eventually train and deploy your own model because an LLM is not a product.
Hallucinations aren't exclusive to LLMs it seems.
The only play for OpenAI et al in my opinion is to try to pull up the draw bridge behind them by getting legislation passed which makes compliance prohibitively difficult if that's not your core business.
Would you consider Database to be a product ?
SQLLite , PostGres etc. are free and yet we have Oracle , Mongodb and MS SQL doing billions in revenues.
I imagine you meant to say that LLMs are comoditized.
Getting the correct words here is important, as you can see by all the people disagreeing on the literal interpretation of your post.
And yeah. LLMs have got the fastest transition from highly innovative singular product to plain commodity I've ever seen or read about. BSD licensed software libraries do not move that quickly. They were mostly not even adopted yet, and have a huge barrier to entry, what makes it much more of a feat.
Yep, much better way of putting it!
Where is the differentiation?
VC business model is throwing money at the wall and seeing what sticks. They love congratulating themselves on how smart they are but at the end of the day their overall returns trail S&P 500. They are salespeople and their job is to sell themselves to private capital on how smart and connected they are.
I am waiting what will be next one after AI, because quantum computing feels like too hard to become a hype, the same with space ventures, there is some upward trend going on there but still space is too hard.
Biotech and space will both be trends but slower and less bubbly because the cost to play is high, though the returns are possibly huge in both.
This is legitimately just the same damn hype train the tech sector is constantly attempting to create. Now AI is the next internet. Before that it was the metaverse. Before that it was NFTs. Before that it was cryptocurrency. Before that it was quantum. Before that it was VR. Before that it was AR.
None of those were the revolutions postured by techno-fetishistic CEOs. Most stick around in some capacity, like VR and AR, and the argument can be made that those have a future. Blockchains certainly have a future as it's a highly useful technology, even if the financial vehicle made by it is utterly useless. The metaverse is Dead on Arrival because nobody ever wanted it in the first damn place, apart from the speculators betting money on it.
Neither of those things you listed were mainstream trends. If you can not distinguish between fads and major trends that is your problem.
Internet, Mobile, Cloud and now AI are technology trends with mainstream buy in from the biggest companies in the world.
You're distinguishing based on hindsight. It's mainstream if it succeeds and if it doesn't, it was a fad. How much did Zuckerburg put into Facebook's metaverse projects? I believe it was $46 billion. Then there was Decentraland too, those were as mainstream as it got. You had tons of internet-famous people cashing in for hundreds of thousands of dollars just for selling their original copies of reddit memes. How is that not at least somewhat mainstream?
I have zero doubt whatsoever that after the AI bubble lets go, I'll be having this same damn conversation with someone else and they'll be saying AI was a fad. That's my entire point.
AI has buy in from the entire tech sector. Just like the cloud, mobile and internet did.
They certainly were sold as trends about to go mainstream.
> Blockchains certainly have a future as it's a highly useful technology
The reason for the big pension funds and endowments to invest in VC is that it’s a bit counter cyclical. Also it’s a tiny pimple on the side of the PE sector overall.
You can see how marginal it is in the financial sector by going to an LP meeting — the big institutionals send kids — first year analysts — to attend because it isn’t that important.
Most startups don't get big. How many startups founded in the past decade have become hugely profitable? It's not that many. A handful out of the 500k or so funded startups. Meanwhile the S&P keeps chugging along at 8% annually.
The price of an investment is based on the expected profitability of a company, an investment in a barely profitable company, if priced correctly, should yield returns at least equal to good companies like Apple, Google, and Microsoft, as the investment would be discounted to compensate for the poor expected future earnings of the company you are investing in.
AI is still too opaque to reliably know beforehand if an idea will pan out, so you just gotta try it.
Plus it's easy, once you start imagining how the magic of AI is gonna make you rich, to ignore the problems with your idea and assume that the AI will handle them too.
So you've got all these hyped up fools trying to make stuff that lacks merit. How do you capitalize on that? You don't invest in the stuff that's doomed to fail, you create a slot:
> Insert coin, insert half baked idea, receive AI app
That way you get to keep the coins even when apps don't turn out. Plus, you're collecting the institutional knowledge necessary to pounce on something that comes along which is actually worth investing in.
Or at least that's the vibe I get when our meetings feature an AI-excited VC (which isn't common, but it happens).
None of those three make my life better or easier:
Dropbox -> We drop your data directly on S3 but give you worst security...
Slack -> Being able to navigate our messy interface is an IQ test in itself...
Instagram -> Only makes your life better if you have bikini posts to share....
> Dropbox -> We drop your data directly on S3 but we give you worst security...
You're top 1% tech-savvy then. 99% people don't know what S3 is and won't deem Dropbox as a worse S3.
(insert rsync joke)
And despite all that, I get mysteriously being pushed by partner companies I work with, this product named Box. With tactics similar to the push to use Teams. It's like being volunteered for karaoke night by your Japanese boss...
For those interested, the first comment on the launch thread for Dropbox here on HN had very similar sentiments to belter...
...What?
Like, what? I feel like stepping into another timeline reading this.
This becomes very obvious when you use Claude Projects with Artifacts! ChatGPT depends heavily on one’s ability to copy and paste… and even though it is an improvement, Claude Projects still make managing a set of documents tedious compared to your standard code editor.
Third-party tools like Cursor are an improvement but will be prohibitively expensive compared to companies that create and manage their own LLMs.
I expect to see a native document editing/code editing software system directly from one of the LLMs-as-a-service companies at some point.
I agree. I'm of the hypothesis that, when it comes to AI, a lot of product teams are pursuing overly ambitious and sophisticated features instead of targeting easy wins that are in plain sight [1].
[1] https://thomasvilhena.com/2024/06/easy-wins-for-generative-a...
Enterprises I have spoken to says they are getting pitched by 20 startups offering similar things on a weekly basis. They are confused on what to go with.
From my vantage point (and may be wrong), the problem is many startups ended up doing the easy things - things which could be done by an internal team too, and while it's a good starting point for many businesses, but hard to justify costs in the long term. At this point, two clear demarcations appear:
1/ You make an API call to OpenAI, Anthropic, Google, Together etc. where your contribution is the prompt/RAG support etc.
2/ You deploy a model on prem/private VPC where you make the same calls w RAG etc. (focused on data security and privacy)
First one is very cheap, and you end up competing with Open AI and hundred different startups offering it. Plus internal teams w confidence that they can do it themselves. Second one is interesting, but overhead costs are about $10,000 (for hosting) and any customer would expect more value than what a typical RAG provides. Difficult to provide that kind of value when you do not have a deep understanding and under pressure to generate revenue.
I don't fully believe infra startups are a tarpit idea. Just that, we havent explored the layers where we can truly find a valuable thing that is hard to build for internal teams.
An acquisition here amounts to teams luck surface.
Of course they went with both, and as far as I can tell both are a major disaster post layoffs :)
To placate the AI fans, that's not because AI isn't interesting, it's because that's how these hype cycles always go. I remember when everything had to be XML'd. XML has its uses, but a lot of money was wasted jamming it everywhere because XML Was Cool. AI has its uses, but it is still an engineering tool; it has a grain, it has things it is good at, it has things it can't just wave a magic wand and improve, the demarcation between those two things is very, very complicated, and people are being actively discouraged from thinking about those lines right now.
But there really isn't any skipping the Trough of Disillusionment on your way to the Plateau of Productivity.
That was probably before my time. Was it really "cool"? Like big data, cloud and agile cool? Or more like ... dunno ... some design pattern? So hard to think about XML as having been cool.
I pushed for local first models but the cost tradeoff just did not make sense for anything but the biggest clients, and they were constantly swapping back and forth whether openai would be acceptable or not for "insert sensitive use case here"
Mostly just a big cluster
(Edit) Apple is using them in their Apple Intelligence, hence the association. But the technique was around before.
https://machinelearning.apple.com/research/introducing-apple...
> Our foundation models are fine-tuned for users’ everyday activities, and can dynamically specialize themselves on-the-fly for the task at hand. We utilize adapters, small neural network modules that can be plugged into various layers of the pre-trained model, to fine-tune our models for specific tasks. For our models we adapt the attention matrices, the attention projection matrix, and the fully connected layers in the point-wise feedforward networks for a suitable set of the decoding layers of the transformer architecture.
Want to solve a real problem, help me create custom benchmarks, clean my data, get my small parameter model to reason better etc.
Starting by investing yourself fully into a given problem, and fixing it with the most appropriate tool (might be GenAI, might not) is much more likely to end in something people actually want or need.
Doing the reverse, and trying to find an existing problem that matches a solution you've already picked is how you end up with hundreds of companies selling thin API wrappers for ChatGPT.
Our problem was we had a real world problem and real data. All the startups were solving for imaginary problems and had no data.
And maybe that's at the core of the issue here, namely that this service in its current form doesn't scale like b2c internet tech
I think we will go back to tools combined with humans to solve at least some of them. So it's services and software.
Also, what kind of evaluations for quality of reasoning do you use?
That is why you have to pay for your own dev team as SaaS vendor is not going to be your custom development team - as your custom problem is not available for easy resell to other customers.
I recently started a company with a friend if mine to do exactly this.
Ive worked at a few AI startups over the last 8 years, and the problem everyone tackles independently (and poorly) is the long tail of dealing with input data that isn't great. You build a demo with sample data that works well, then you move on to real world uses and the data is suddenly... blehg.
Our customers are really the type of AI infra companies being talked about in this article. And yea, the new ones I work with everyday are often a dime a dozen. A revolving door of small startups trying to make the same general purpose AI infra targeting other traditional "boring enterprise infra" companies.
The ones that I'm seeing get the most traction, have the best products, and best chances of success have zeroed in on specific niches and sub-industries. (Think AI infra that helps B2B2B companies where that last "B" is like Roofing companies and the value provided is helping Roofing companies easily and drastically scale their outbound and inbound marketing and sales.)
The startups I work with that make me scratch my head are the ones trying to build "disruptive" AI infra that does nothing different, provides nothing special, other than potentially nice UI/UX, and is liable to have their lunch eaten by either natural iterations and improvements of our own services they essentially just white label, or some other incumbent.
To me, it's like trying to create a new company to compete against Walmart and Target on groceries because they're too massive scale to win against "a well tailored customer experience" but then forgetting Costco, Aldi's, Trader Joes, and Whole Foods exist. And why would any of those aforementioned companies feel the need to acquire you rather than casually crush you as they go about their business either ignoring you as you wither or taking your good ideas and incorporating them into their own offering?
It's not impossible, just has to make sense and even then a certain degree of "the stars aligning" is required. Which is why there inevitably can only be a small group of winners out of this massive sea of hopefuls.
And I of course can only shrug my shoulders if asked if the AI infra startup I work at is differentiated, necessary, and lucky enough to be at the finish line with the survivors at the end. (We're finding our PMF and potential road to incumbency mainly with two-ish markets: old and new school enterprise infra and non-tech Fortune 500 type of companies.)
Almost all enterprises have pre-committed budgets for cloud which means unless your product is FOSS it's going to be hard to convince someone to bet their business on it. Especially given that in this fundraising environment there is a 95% chance they won't be around in a year or two anyway.
It's going to be a brutal few years especially if we are heading into a period of diminishing returns in terms of LLM accuracy.
Even before the recent LLM rush took off, AI was a thing. In the city of Copenhagen there was a project to digitalise a few million case files (which is 10-100 documents per case file), and how it was done was basically with an intermediary company who knew the training and a cooperation with Microsoft. Yes, I’m dumping down the complexity of it all, but once the training period of half a year was over, Azure made a lot (and I mean a lot) of infrastructure available for not a lot of money and the process completed in a week or so. Since it had to happen and because it was a PoC the same project was also done by real humans. This was the “actual” project and every time deadline and whatnot the AI project had came from how long it would take X humans to do it. I can’t recall how many X was, but it was enough to meet the legal deadline for when these case files had to be digitised and sorted correctly.
The human project was the result, and then the AI PoC was later used as a lesson on whether it could be done this way or not. It can, it was more accurate and not more expensive.
Anyway… I’m not sure who would’ve been capable of competing with Azure. (Outside the usual suspects). Maybe a company of Hetzner could? But you would need someone who can offer you a massive amount of computing on demand, and the only companies which are going to have that are big vendors.
Maybe it’s different with LLMs because the requirement is a continuous thing rather than something you need for a short period of time?
This isn't a death knell.
1. If you get into the marketplace, enterprises can spend their commit against you.
2. A few million in ARR is ~nothing to a hyperscale cloud, but meaningful to most startups. If you find the right positioning, you can get their sales team selling your solution on many deals.
If you don't think any ISVs are making millions through the marketplace you're simply mistaken. I worked at Google Cloud and personally know at least one startup that made the majority of their revenue through cloud partnerships.
We are not buying anything and we won't. We already have contracts in place with cloud vendors--the usual suspects: Google, Microsoft, Amazon--and the rest of the infra is developed in-house. Why should we buy subscription-based and high-maintenance products from an a16z-funded AI infrastructure company that might shut down operations in two years and say "bye-bye, it's been great"?
The former Uber/Michelangelo team that built Tecton, Netflix's Metaflow that later became Outerbounds, I have no idea who they are selling their AI infra products to.
Obviously I'm not nearly as pessimistic about it. Zoom out for a sec and generalize to SaaS in general, not just AI infra (a subset of Saas) - all the arguments listed apply there too, except the data moat (which honestly doesn't matter to tons and tons of AI infra companies. That's more of an AI application problem). Now of course most startups are doing AI at least a bit, but in the past decade we've seen plenty of SaaS vendors compete with incumbents either head on or by carving out their own niche. In fact, two of the companies the author considers "incumbents" are arguably still challengers, but definitely were in this exact situation just a few years ago: Vercel and Databricks.
Also, competition from incumbents is hardly a deathknell. There's room for multiple products in some market segments - how many RDBMS companies are there? Competition from a huge incumbent in many ways comes with benefits, because it helps grow the overall market and awareness of the product space, including your own product.
I suppose according to this author I'm in the "application layer" even though really I'm in the AI-application-layer-now-but-not-later-layer, software-infrastructure-layer. And that's great because I actually do have experience in that specific application area. But honestly, saying "you ought to have expertise in your domain" is 1) duh 2) in the examples (llamaindex parsing/ocr, langchain llmops + agnetic stuff), there is clearly a big enough twist on doing it "but with AI" that the application/vertical is close to novel. Successful challengers create valuable businesses without prior deep expertise in their domain all the time and I don't really see how this is any different.
Basically, you could repeat this for any SaaS business. Starting a company is hard, but I don't know if AI infra is uniquely hard in the ways laid out.
Snowflake is not profitable. I doubt Databricks is. Their market and business is crap.
They have 1.6B revenue, a 50% YoY growth. And still not profitable. Hm, okay, recent acquisition on ML stuff, and of course probably burning hundreds of millions on cloud-GPU-AI shit.
Well, I guess as long as they have so big growth it makes sense to invest and raise ... and yeah that probably completely obscures the actual profitability of their core business. (Not to mention that they are probably spending all that money to try to expand their core business. To upgrade their value prop from cloud version of less-dumb-data-pipelines to 1-800-data-4-AI.)
Databricks is different, it's fast, it's robust, I trust the results. They just built a good quality product.
At risk of getting philosophical, I’d ask yourself what your goals actually are if you feel only AI can have the impact you desire.
1. Building a sustainable business and making decent money
2. Building a market leader and making ludicrous amounts of money
3. Advancing the state of the art in technology
4. Helping people with their little daily struggles
5. Solving pressing problems humanity is facing
Or many other things I suppose. Now if you believe that AI is eventually going to make anything humans can build now redundant, that'd be a reason to believe nothing else matters in the end I suppose. But even if we get there, there's a lot of road leading to that destination. Any step provides value. Software built today can provide value even if nobody is going to need it ten years from now. And it's not like you could even predict that.
- writing and refactoring code. probably 50 times a day now - improving documentation across the company - summarizing meetings automatically with follow ups - drafting most legal work before a lawyer edits (saved 70% on legal bills) - entity extraction and data cleanup for my users
Probably an unpopular opinion, but I think the most efficient companies of the future will tackle the ironies of automation effectively: Carefully designing semi automation that keeps humans in the loop in a way that maximises their value - as opposed to just being bored rubber stamping the automation without really paying attention.
It's a clutch that will help you cope with the problem. But the real value is on fixing the actual issue.
If your meeting is aimed at producing "general understanding", it's already a dangerous one, and the understanding should go to the correct documentation (what is best done during the meeting). Otherwise, it should produce "focused understanding" between a few people and with immediate application.
If all you take from it is notes, well, I'm really sure that your team won't go digging through meetings notes every time they need to learn about some new context. Meeting notes are useful for CYA only, and if people feel safe they'll be filled directly at /dev/null.
None of that is marketed as "AI." It's just a thing the computer does. The single most valuable application of deep learning so far (content recommenders) is a cultural phenomenon, but it’s not referred to as “AI” but rather “the algorithm.”
My takeaway from the article is instead of being a Gen AI startup be a Gen AI startup for a specific use case.
If your product is something that can be ripped off in 3 months, then it probably wasn’t going to have a long term impact anyway.
My companies fastest growing competitor is "internally sourced departments" of the services we provide.
Quite a bit, if you don’t follow the standard tech hype. Find an industry that isn’t tech-first and you’ll notice that there’s a lot of room for improvement.
I am genuinely curious.
The thing is that the tools were well understood and battle tested.
I too think there are too many shovel chasers but I think it’s also a consequence of what’s easier to ship.
The people making shovels make the money by having strong profit margins, becoming a default vendor, and having a moat. Good luck doing that in AI!
And my favorite counter example of selling tools is precisely docker: They built tech used everywhere... yet how much value they captured? It's tge same story all over dev tool space.
For instance, foundational AI startups are also ridiculously hard to build. You need an insane amount of funding, spend it pretraining models to stay competitive only to find that gains in hardware and model architecture make them obsolete within months plus there's no real guarantee that scaling will keep working.
Application layer startups are hard in a very different way, there's an insane amount of competition and new capabilities are emerging every few weeks. I have worked with a few AI girlfriend startups and they are really struggling with keeping apace and warding off ridiculous amount of competition.
I think it's really just YMMV. Of course, the deeper you get into the stack, the more monopolizing pressure there is. Is it hard to build AI infra startups? Yes 100%. Will there be very few winners? Yes. Is it harder than foundational or application layer startups? Depends on the founders' strengths. Is it Is it a lost cause? I really don't think so.
This part:
> For AI infra startups to be “venture scale”, they will eventually need to win over enterprise customers. No question. That requires the startups to have some sustainable edge that separates their products from the incumbents’ (GCP, AWS, as well as the likes of Vercel, Databricks, Datadog, etc).
Given VC's penchant for throwing cash at grifters in the latest hype space is it any suprise that some of the beneficiaries are looking for a quick exit before they have to do any actual work?
I'm just surprised the Long Island Iced Tea Corp / Long Blockchain Corp hasn't become the Long LLM Corp yet.
1) insane levels of competition towards any goal make relavant minor, secondary, traits that are not obvious before hand. Pure luck becomes more important.
2) excess market concentration (of which the tech sector is maybe the most egregious example) makes any new initiative harder. The more dominant and controlling the incumbents the harder to find a decent sized niche to grow.
3) selling to risk averse enterprizes / organizations is always an uphill battle that requires climbing a mountain of bureaucracy and regulation, only to eventually face random internal politics.
In the end the current craze will certainly produce a modified tech landscape. These recurring hypes always overpromise and underdeliver, but a cumulative effect is slowly happening.
In such stormy seas its hard to identify an optimal course and strategy. Riding every hype wave may sound silly but might work. On the other extreme, one may seek beacons indicating eventual stable land and try to navigate there.
Good luck
I'm legitimately wondering how your hosted Whisper API for $0.17/hr is supposed to compete with groq's exact same API that costs $0.03/hr.
You may be about to find out how crowded all of the AI infra spaces are.
I strongly recommend narrowing your scope far beyond modality. If you've been working with this tech and getting familiar with it then you already have valuable expertise. Pivot now or panic later. If you want to stay in the speech space find what markets are being underserved with speech AI related solutions. Are there pain points there that can be solved by a STT API? If so, build those solutions. You can't compete at the infra layer and I'm not sure why you would want to try if you don't already have something unique about your offering beyond hosting open source models. It's never good if your competition is potentially just a single developer in a company standing up your entire service internally in a week.
If you are determined to stay in the AI infra space then you'll need to be tackling a hard problem that companies want solved. Maybe take a look at fine-tuning models. Hard problem and maybe there's a hunger for it. (It's a risky one to tackle too though since it's very possible general/foundational models will maintain a grip on "good enough".)
A very exciting and expensive solution in search of an actual problem, that will ultimately find its way, commoditised, in a small niche, while adjacent technologies take the lead for productive use-cases.
Does this logic also apply to industry-specific "AI Infra?," where the APIs are wrapping a service that solves a domain-specific problem using AI, rather than general purpose infra technology? And provides those APIs to other businesses within that industry?
If there's a goldrush, you get rich by selling shovels.
... unless there are already 200 shovel shops next to each other...
They can sell the stuff 1/5 of the price and you're not getting rich anytime soon.
> he owned the only store between San Francisco and the gold fields — a fact he capitalized on by buying up all the picks, shovels and pans he could find, and then running up and down the streets of San Francisco, shouting 'Gold! Gold on the American River!' He paid 20 cents each for the pans, then sold them for $15 a piece. In nine weeks, he made $36,000."
I don’t say that thinking that LLMs (really: Transformers and the corresponding scaling of compute around it) don’t represent a step change.
I say that because I am very sure that we are going to see a slope of enlightenment that results in products that improve the quality of human life.