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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 co-invest in Cybersecurity startups with Menlo Ventures in 2022 instead of building their own dedicated cybersecurity vertical, because a partnership driven growth model nets the most profit with the least resources expended when trying to expand your TAM into a new and very competitive market like Cybersecurity.

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 because at Hyperscalers size and revenue, Foundational Models are just a feature (an important feature, but a feature nontheless).

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.

>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.

Well-put because the business is focused and to-the-point from the beginning.

For those applications where this gets you in the door to the domain, or gets you in sooner, this can be a competitive advantage. I think Lukas is pointing out the longer-term limitations of the approach though. I thought this would extend from 1980s electronics myself.

You could edit this however:

>Startups can [prosper] by concentrating on creating a very domain specific model, and providing support and services that comes out of having expertise in that specific domain.

And it may hold true anyway and you may have a lifetime of work ahead of you whether or not the more-generalized capabilities catch up or not. You don't always have to actually be competitive with capitalized corporations in the market if you are adding real value to begin with, and the sky can still be the limit.

>the most accurate domain knowledge always tends to involve internal data.

>Incumbent hyperscalers . . . tend to have more leverage when it becomes this type of data

That can help as a benchmark to gauge when a person or small team actually can occasionally outperform a billion-dollar corporation in some way or another.

I'm no Mr. Burns, but to this I have slowly said to myself "ex-cel-lent" similarly for decades.

It's good to watch AI approaches come and go and even better to be adaptable over time.

All of what you wrote is mostly true, except that "not 100% perfect in a number of fields and usecases" is quite an understatement. You mention the cybersecurity vertical. As a datapoint, I have put the simplest code security analysis question to ChatGPT (4o mini, for those who might say wait until the next one comes out). I made a novel vulnerable function, so that it would have never been seen before. I chose a very simple and easy vulnerability. Scores of security researchers in my vicinity spotted the vulnerability trivially and instantly. ChatGPT was more than useless, failing miserably to perform any meaningful analysis. The above is anecdotal data. Could be that a different tool would perform better. However, even if such models were harnessed by a startup to solve a specific problem, there is absolutely no way for present capabilities to yield a 30-50% HC reduction in this subdomain.

I agree. Foundational models suck at the high value security work that is needed.

That said, the easiest proof-of-value for foundation models in security today is automating the SOC function by auto-generating playbooks, stitching context from various signal sources, and being able to auto-summary an attack.

This reduces the need for hiring a junior SOC Analyst, and is a workflow that has already been adopted (or is in the process of being adopted) by plenty of F500s.

At the end of the day, foundational models cannot reason yet, and that kind of capability is still far away.

4o mini is a weak, old model. I’m pretty sure even regular o1 (non-pro) would be able to crack a simple vuln

I knew this comment would come.

Interestingly, this is the exact opposite of the point the article makes — which is that over time, more general models and more compute are more capable, and by building a domain-specific model you just build a ceiling past which you can’t reach.

This is not the same as having unique access to domain-specific data, which becomes more valuable as you run it through more powerful domain-agnostic models. It sounds like this latter point is the one you say has value for startups to tackle

> This is not the same as having unique access to domain-specific data, which becomes more valuable as you run it through more powerful domain-agnostic models. It sounds like this latter point is the one you say has value for startups to tackle

Exactly!

    > AI will not play out the way the Web did (upstarts unseat giants)

Yes, I agree.

I recently spoke to a doctor that wanted to do a startup one part of which is an AI agent that can provide consumers second opinions for medical questions. For this to be safe, it will require access to not only patient data, but possibly front line information from content origins like UpToDate because that content is a necessity to provide grounded answers for information that's not in the training set and not publicly available via search.

The obvious winner is UpToDate who owns that data and the pipeline for originating more content. If you want to build the best AI agent for medical analysis, you need to work with UpToDate.

    > ...not brand new startups who have to try to convince companies to give them all their data for the first time.

Yes. I think of Microsoft and SharePoint, for example. Enterprises that are using SharePoint for document and content storage have already organized a subset of their information in a way that benefits Microsoft as concerns AI agents that are contextually aware of your internal data.

> will instead play out like smartphones (giants entrench and balloon).

Someone correct me if I'm wrong, but didn't smartphones go the "upstarts unseat giants" way? Apple wasn't a phone-maker, and became huge in the phone-market after their launch. Google also wasn't a phone-maker, yet took over the market slowly but surely with their Android purchase.

I barely see any Motorola, Blackberry, Nokia or Sony Ericsson phones anymore, yet those were the giants at one time. Now it's all iOS/Android, two "upstarts" initially.

> Now it's all iOS/Android, two "upstarts" initially.

They weren't upstarts, they were giants who moved into a new (but tightly related) space and pushed out other companies that were in spaces that at first seemed closely related but actually were more different than first appeared.

Android and iOS won because smartphones were actually mobile computers with a cellular chip, not phones with fancy software. Seen that way Apple was obviously not an upstart, they were a giant that grew even further.

Google is perhaps somewhat more surprising since they didn't do hardware at all before, but they did have Chrome, giving them a major in on the web platform side, and were also able to leverage their enormous search revenue. Neither resource is available to an upstart/startup.

Neither Apple nor Google were giants in 2007.

Both were giants in 2007. Google was at peak post-IPO fame and Apple was full steam ahead with Jobs at the helm. They were huge players, far from startups or upstarts.

Seconded. For context, Google was about to launch Chrome and Apple was about to launch the Windows version of Safari. Both companies were clearly on the offense at the time.

(and the parent poster was competing with both :) )

"Not as giant as they are today" is not the same thing as "not giants". Let's compare revenue and net income for Google, Apple, and the other manufacturers that OP mentioned:

* Google: $16.5B revenue, $4.2B net income [0]

* Apple: $24B revenue, $3.5B net income [1]

* Motorola: $36B, -$49M net income [2]

* BlackBerry (RIM): $2B, $382M net income [3]

* Nokia: €51B, €7.9B net income [4]

* Sony: $48B, $799M net income (converted from yen at 158 yen/$) [5]

These numbers are absolutely in the same ballpark as the supposedly-larger players which they beat out, and in terms of profit substantially higher than all but Nokia.

[0] https://abc.xyz/assets/investor/static/pdf/2007_google_annua...

[1] https://d18rn0p25nwr6d.cloudfront.net/CIK-0000320193/4913a18...

[2] https://www.motorolasolutions.com/content/dam/msi/docs/en-xw...

[3] https://www.annualreports.com/HostedData/AnnualReportArchive...

[4] https://www.nokia.com/system/files/files/request-nokia-in-20...

[5] https://www.sony.com/en/SonyInfo/IR/library/ar/SonyAR07-E.pd...

> Someone correct me if I'm wrong, but didn't smartphones go the "upstarts unseat giants" way?

I think "upstarts" is being used uphthread to mean "startups" and "giants" is being used in a general, not market-specific, sense; that is, it isn't referring to entities that are mere new entrants in a particular market but still potentially quite large and established firms displacing incumbents in the particular market, but new, small-starting firms taking over a newly-opened market segment, beating out the large, established firms (from other markets) that are also trying to compete in it.

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.

> 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.

Except they won't package it themselves because they are inept and inert. They still won't sell it to startups though.