[HN Gopher] Acquisitions, consolidation, and innovation in AI
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Acquisitions, consolidation, and innovation in AI
Author : pfarago
Score : 74 points
Date : 2025-04-24 18:31 UTC (4 hours ago)
(HTM) web link (frontierai.substack.com)
(TXT) w3m dump (frontierai.substack.com)
| no_wizard wrote:
| I read the article and while it doesn't say this nor imply it,
| this is my takeaway, though correct me if I'm wrong:
|
| Model innovation is effectively converging and slowing down
| considerably. The big companies in this space doing the research
| are not making leap over leap with each release, and the
| downstream open source projects are coming closer to the same
| quality or in fact can produce the same quality (e.g DeepSeek or
| LLAMA) hence why it's becoming a commodity.
|
| Around the edges model innovation - particularly speed ups in
| returning accurate results - will help companies differentiate
| but fundamentally, all this tech is shovels in search of miners,
| IE you aren't really going to make money hand over fist by simply
| being an LLM model provider.
|
| In another words, this latest innovation has hit commodity level
| within a few short years of going mainstream and the winners are
| going to be the companies that make products on top of this tech,
| and as the tech continues to become a commodity, the value
| proposition for pure research companies drops considerably
| relative to application builders.
|
| To me this leaves a central question: when does it hit a relative
| equilibrium where the technology and the applications on top of
| it have largely hit their maximal ability to add utility to
| applicable situations? That's the next question, and I think the
| far more important one
|
| One other thing, at the end of the article they wrote:
|
| >Ultimately, businesses won't rearrange themselves around AI --
| the AI systems will have to meet businesses where they are.
|
| This is demonstrably untrue. CEOs are chomping at the bit to
| reorganize their business around AI, as in, AI doing things
| humans used to do and getting the same effective results or
| better, thereby they can reduce staff across the board while
| supposedly maintaining the same output or better.
|
| Look at the leaked Shopify memo for an example or the trend of
| _"I can vibe code with an LLM making software engineers
| obsolete"_ that has taken off as of late, if LinkedIn is to be
| believed
| epistasis wrote:
| I would agree with this and also say that it's been clear this
| is true for at least a year. Innovations like Deepseek may not
| have been around a year ago, but it was very clear that "AI" is
| actually information retrieval and transformation, that the
| chat UI had limited applicability (nobody wants to "chat with
| their documents"), and that those who could shape the tech to
| match use cases would be the ones capturing the value. Just as
| SaaS uses databases, but creates and captures value by shaping
| the database to the particular use case.
| nemomarx wrote:
| so when do we get to the point where AI apps are just CRUD
| apps essentially? RAG kinda feels like a better version of
| those to me
| babelfish wrote:
| now!
| skeeter2020 wrote:
| I agree with this but I think it's still an open question if
| anyone can build a successful product on top of the tech. There
| will likely be some but it feels eerily similar to the dot com
| boom (and then bust) when the vast majority of new products
| built on top of this (internet) technology didn't produce and
| didn't survive. Most AI products so far are fun toys or
| interesting proofs, and mediocre when evaluated against other
| options. They'll need to be applied to a much smaller set of
| problems (that doesn't support the current level of investment)
| or find some new miracle set of problems where they change the
| rules.
|
| Businesses are definitely rearranging themselves structurally
| around AI - at least to try and get the AI valuation multiplier
| and Executives have levels of FOMO I've never seen before. I
| report to a CTO and the combination of 100,000 foot hype
| combined with down in the weeds focus on the "protocol de jour"
| (with nothing in between that looks like a strategy) is
| astounding. I just find it exhausting.
| adpirz wrote:
| The dot com boom is an apt analogy: the internet took off, we
| understood it had potential, but the innovation didn't all
| come in the first wave. It took time for the internet to
| bake, and then we saw another boom with the advent of mobile
| phones, higher bandwidth, and more compute per user.
|
| It is still simply too early to tell exactly what the new
| steady state is, but I can tell you that where we're at
| _today_ is already a massive paradigm shift from what my day-
| to-day looked like 3 years ago, at least as a SWE.
|
| There will be lots of things thrown at the wall and the
| things that stick will have a big impact.
| dingnuts wrote:
| other than constantly feeling gaslit about the quality of
| these tools, I can tell you where we are _today_ is
| basically the same in my day to day as it was three years
| ago.
|
| oh except, sometimes someone tells me I could use the bot
| to generate a thing, and it doesn't work, and I waste some
| time, and then do it manually.
| vonneumannstan wrote:
| >Model innovation is effectively converging and slowing down
| considerably. The big companies in this space doing the
| research are not making leap over leap with each release, and
| the downstream open source projects are coming closer to the
| same quality or in fact can produce the same quality (e.g
| DeepSeek or LLAMA) hence why it's becoming a commodity.
|
| You're just showing how disconnected from the progress of the
| field you are. o3/o4 aren't even in the same universe as
| anything from open source. Deepseek R1, LLama 4? Are you
| joking?
| no_wizard wrote:
| Depends on how they're applied. I've had success using LLama
| and while we check to see if OpenAI or Google's Gemini would
| give us any noticeable improvement, it really doesn't for our
| use case.
|
| While certainly newer models are more capable on the whole,
| it doesn't mean I need all that capability to accomplish the
| business goal.
| vonneumannstan wrote:
| This is kind of a useless statement. If your use case is so
| easy that "old" models work for it then obviously you won't
| care about or be following the latest developments but its
| just not accurate to say that Deepseek R1 is equivalent to
| o3 or Gemini 2.5.
| no_wizard wrote:
| Producing quality results is not the same thing as saying
| Deepseek R1 is the equivalent of o3 or Gemini 2.5
|
| Again, its not about capabilities alone, (on this, many
| models lag behind, I already said as much). I follow
| these developments quite closely, and I purposely said
| _results_ as to not say they 're equivalent in
| capability. They aren't.
|
| However, if a business is getting acceptable results from
| older models or cheaper models than capability doesn't
| matter, the results do. Gemini 2.5 can be best of breed
| but why switch if it shows no meaningful improvement in
| results for the business?
|
| If I need more capability or results are substandard, I
| can always upgrade, but its like saying there's no room
| for cheaper processors and you'd be out of your mind not
| to be using only the latest at all times no matter the
| results.
| luckylion wrote:
| That's not what GP was saying though. To stay with that
| analogy, the assertion was that "all processors are kinda
| the same, there's no real qualitative difference", which
| sounds pretty strange. It's somewhat accurate if your
| use-case is covered by the average processor and the
| faster one doesn't benefit you. They're not equal, but
| all of them surpass your needs.
|
| > If I need more capability or results are substandard, I
| can always upgrade
|
| You wouldn't be able to upgrade (and see improved
| results) if the model you use today was close to equal to
| the top of the line.
| no_wizard wrote:
| That wasn't the assertion. The results - not the models
| themselves, not strictly speaking their over all
| capabilities - if they have no meaningful improvement by
| moving to a newer model, why then would I want to switch
| if I'm not getting any tangible improvement in results?
| Der_Einzige wrote:
| Your belief that O3 and O4 are that superior to open source
| models comes from the fact that models are often using shit,
| garbage, trash samplers like top_p and top_k.
|
| Switch them to good samplers and write the tool calling code
| to allow tool calls in the reasoning chain and you'll see
| close to parity in performance.
|
| The remaining advantages left to closed source come from
| better long context, and later data cutoff points.
|
| If you don't believe me let's see the receipts of your ICLR
| or NeurIPS publications - otherwise sit down and listen to
| your elders.
| bongodongobob wrote:
| > This is demonstrably untrue. CEOs are chomping at the bit to
| reorganize their business around AI, as in, AI doing things
| humans used to do and getting the same effective results or
| better, thereby they can reduce staff across the board while
| supposedly maintaining the same output or better.
|
| Nah. Maybe tech CEOs. Companies are blocking AI carte blanche
| at the direction of their security teams and/or only allowing
| an instanced version of MS Copilot, if anything. Other than
| write emails, it doesn't do much for the average office worker
| and we all know it.
|
| The value is going to be the apps that build on AI, as you
| said.
| borski wrote:
| > Companies are blocking AI carte blanche at the direction of
| their security teams
|
| What companies?
| epistasis wrote:
| I know many IP-heavy and health-centric companies are
| blocking AI use severely. For example, pharma depends on
| huge amounts of secrecy and does not want _any_ data leaked
| to OpenAI, and often has barely-competent IT and security
| staff that don 't know what "threat model" means. Those who
| deal with controlled health data also block with a heavy
| hand.
| no_wizard wrote:
| I imagine it'll take time for any of this tech to
| permeate and the lower barrier of entry will see adoption
| faster - as is usually the case with new tech - but it'll
| make its way eventually. On premise AI will be a thing
| borski wrote:
| Once upon a time, they blocked docker too. Things change.
| no_wizard wrote:
| It certainly isn't maybe, look at the recent Shopify memo
| leak, and the way that lots of companies are talking about
| AI.
|
| Any company with any sort of large customer service presence
| are looking at AI to start replacing alot of customer service
| roles, for example. There is huge demand for this across many
| industries, not only tech. Whether it actually delivers is
| the question, but the demand is there.
| warkdarrior wrote:
| Claiming these AIs "don't do much" overlooks the very real
| productivity gains already happening - automating tedious
| tasks and accelerating content creation. This isn't trivial
| and will lead to the deeper integrations and streamlined
| (read: downsized) workforces. The reorganization isn't a
| distant fantasy; it's already here.
| o1inventor wrote:
| One of the possible alternative routes is this:
|
| Model providers and model labs stop opensourcing/listing their
| innovations/papers and start patenting instead.
| nc wrote:
| One thing this article gets wrong is how OpenAI isn't an
| application layer company, they built the original ChatGPT "app"
| with model innovation to power it. They're good at UX and
| actually have the strongest shot at owning the most common apps
| (like codegen).
| mattmanser wrote:
| I personally find their UX frustrating, basically a junior
| developer's attempt at doing a front end. What do you think is
| so good about it?
|
| It's also janky as hell and crashes regularly.
| monoid73 wrote:
| I think the UX of chatgpt works because it's familiar, not
| because it's good. Lowers friction for new users but doesn't
| scale well for more complex workflows. if you're building
| anything beyond Q&A or simple tasks, you run into limitations
| fast. There's still plenty of space for apps that treat the
| model as a backend and build real interaction layers on top
| -- especially for use cases that aren't served by a chat
| metaphor
| bilbo0s wrote:
| I don't disagree. But that's a pretty good reason to make sure
| you're making something _other_ than the obvious common apps if
| you want a big chunk of acquisition money.
| xnx wrote:
| There's a lot of opportunity to apply leading edge AI models to
| specific business applications, but success here is determined
| more by experience with those business domains than with AI
| generally.
|
| An AI startup could still be a useful "resume" to get acqui/hired
| by one of the big players.
| lenerdenator wrote:
| I think too many people are focused on the idea of AGI instead
| of doing what you're suggesting, which is where the _real_
| value-add is for customers.
|
| I don't need God in a datacenter. I need help diagnosing an
| Elastic Search problem.
| riku_iki wrote:
| Its just it is not easy to come into specific pre-occupied
| space for outsiders.
| dismalaf wrote:
| The LLM space was never going to be kind to those without deep
| pockets. And right now there's no point getting in it because
| it's hit a wall. So yeah, startups should steer clear of trying
| to make frontier LLM models.
|
| On the other hand, there's a ton of hype and money looking for
| the next AI related thing. If someone creates the next
| transformer, or a different AI paradigm that pushes things
| forward, they'll get billions.
| lemax wrote:
| This take doesn't really highlight the fact that the most
| competitive foundational model companies _are_ innovative
| application builders. Anthropic and OpenAI are vying for
| consumers to use their models by building these sort of super
| applications (ChatGPT, Claude) that can run code, plot graphs,
| spin up text editors, create geographic maps, etc. These are well
| staffed and strategically important areas of their businesses.
| There 's competition to attract consumers to these apps and they
| will grow more capable and commoditize more compliments along the
| way. Who needs Jasper when you can edit copy in ChatGPT, or an AI
| python notebook app, or, now, Cursor?
| imoreno wrote:
| This focuses on case where the acquirer seeks to capture the
| value of the startup's business. But this is not always the case,
| sometimes the startup is dubious, but a cash-rich enterprise can
| purchase startups simply to eliminate potential avenues of
| competition. They may not be interested in adding a better
| product to their portfolio, only in quashing any nascent attempts
| at building the better product so they can keep selling their own
| mediocre one.
|
| Also, "model innovation" strikes me as missing the point these
| days. The models are really good already. The majority of
| applications is capturing only a tiny bit of their value.
| Improving the models is not that important because model
| capability is not the bottleneck anymore, what matters is how the
| model is used. We just don't have enough tools to use them fully,
| and what we have is not even close to penetrating the market,
| while all the dominant tools are garbage. Of course application
| innovation is the place to be!
| blitzar wrote:
| Every startup should generate a shitty Ai wrapper product, write
| one or two lines of code, generate hype and have 2025's version
| of Softbank give you a billion $'s.
|
| Frankly it's bordering on irresponsible to not be targeting
| acquisition in this climate.
| Bloating wrote:
| 1) Collect Underpants
|
| 2) ?
|
| 3) Profit!
| stuart_real wrote:
| The fact that a VSCode-based GPT-wrapper is being offered $3B
| tells you how desperate the LLM companies are.
|
| Anthropic and xAI will also make similar acquisitions to increase
| their token usage.
| paulsutter wrote:
| Work just to be a part of it. This is the most consequential time
| in history.
|
| It's the best time ever to build. Don't work on anything that
| could have been done two years ago.
|
| Learn the current tools - so that you can adapt to the new tools
| that much faster as they come out.
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