[HN Gopher] AI: Startup vs Incumbent Value (2022)
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AI: Startup vs Incumbent Value (2022)
Author : tristanMatthias
Score : 73 points
Date : 2023-07-18 03:27 UTC (19 hours ago)
(HTM) web link (blog.eladgil.com)
(TXT) w3m dump (blog.eladgil.com)
| ankit219 wrote:
| Reflecting on this nine months later, it feels a lot of people
| misread the pace of innovation, and where inertia actually stays.
| A couple of aspects I thought of when I heard about Jasper
| layoffs.
|
| 1. A lot of value was supposed to come from selling to
| enterprises. The narrative was that they would move slowly and
| hence nimble startups could sell to them and generate quick
| revenue. The assumptions are really tested on this one. First,
| the virality and popularity meant any Engg leader working on AI
| related projects got social capital and prestige (and a
| promotion) inside the company, making it preferable for companies
| to build than buy. An API form factor helped immensely in getting
| to a POC within a day. Second, for those buying, many startups
| (in LLMOps) ended up selling the same thing, so they slowed down
| to evaluate. Third, the data privacy issues meant no enterprise
| was willing to go for cloud solutions.
|
| 2. A lot of startups never picked up the tougher problems. Eg:
| Training an open source model, or finetuning as a service, the
| core aspects to change the underlying behavior of a model was
| picked up in open source, but most startups never picked that
| part up. Partly to do with things that got hype. An LLM wrapper
| would show off a cool demo, gets shared widely, thus encouraging
| others to build something similar, rather than go deep. A very
| clear indication of this was how Open AI and then Anthropic
| stopped offering finetuning services on newer models electing to
| just enable zero/few shot learning and bigger context windows.
| Easy for them, but tough for consumers who really wanted a
| customized solution.
|
| There are still very cool moonshots out there, and probably
| unlock the value not captured by incumbents. At this point, my
| working assumption is that for an AI startup to capture value,
| they would have to go deeper into the stack, and offer a service
| their competitors would take effort to do (and by extension
| enterprises would take time to do). Eg: Ability/Training a open
| source model locally for search and summarization based on
| proprietary data. I know BCG[1] did it pretty well and got
| spectacular results.
|
| [1]https://bcg.com/press/10may2023-intel-bcg-announce-
| collabora...
| k8spm wrote:
| This is from October 2022... So a bit out dated given how quickly
| AI has moved. Incumbents have stepped up offerings in the
| meantime
| Animats wrote:
| No mention of profits.
|
| Now that the era of free money is over, and you have to pay
| nonzero interest, profits matter again. Is anybody in the AI
| space actually profitable? Is OpenAI losing money on every token
| to build volume?
| numbers_guy wrote:
| It seems possible for OpenAI to be profitable.
|
| There are an estimated 1 billion knowledge workers worldwide.
| The alleged operating costs of OpenAI are around $700,000/day.
| That's $0.25 billion / year. Add to that salaries and
| retraining. Salaries: 375 employees at an average $350,000 /
| year comes to $0.13 billion / year. And retraining cost seems
| to be on the order of tens of millions per training run.
|
| With the right subscription fee it does seem possible to
| balance the books and be profitable. Especially when they start
| selling bulk contracts to governments and schools and big
| corporations.
| fakedang wrote:
| > No mention of profits.
|
| This. Why does Silicon Valley always miss the effing obvious?
|
| The best way to assess a startup's value is to play a bank
| evaluating them for a traditional no-frills loan. How risky a
| debt the bank considers it is a fair measure of the value of
| the company (and in most non-public cases, it will be negative
| EV, future revenues be damned). Not the BS analyses made by IBD
| teams at banks, and not the "valuations" ascribed to the
| startup by its cash-rich, opportunity-deprived VCs.
| huijzer wrote:
| > The best way to assess a startup's value is to play a bank
| evaluating them for a traditional no-frills loan.
|
| In Shoe Dog by Nike co-founder Phil Knight, he describes how
| the banks kept refusing to borrow them money because they
| refused to value based on future cash flows. Eventually, Nike
| switched to another bank. The first bank could have made a
| lot of money there.
|
| In general, even Buffett after years of very conservative
| valuations (Sigar Butt Investing) switched to "buying great
| companies at fair prices". Why would you buy a company that
| barely keeps up with inflation if you could buy one that
| literally grows exponentially. If you hop from Sigar Butt to
| Sigar Butt, you can also grow your money exponentially, but
| it's harder because you pay more taxes, brokerage fees, and
| have to work more on finding the right enters and exits.
| Conversely, if you are as clever as the Nomad Investment
| Partnership and just only bought and hold Costco, Berkshire,
| and Amazon from 2005 to now, you would have gotten great
| returns on investement without having to do a thing.
| fakedang wrote:
| One could also use that story to illustrate my point too.
| Phil Knight was being transparent with his banks on the
| books. His American bank thought he was cooking the books,
| his Japanese bank saw the growth rate of Nike's cash flows
| and loaned him the money based on that. It was not idle
| speculation in an ivory tower (or a Sand Hill Road office)
| like most VCs today. How many VCs even use a DD audit in
| the final stages of their Series D+ investment?
|
| The second thing is that Nike had the cashflow to show in
| its books, unlike most of today's startups. Stuff was
| moving off the shelves super fast, and they were making a
| neat profit on every sale. It wasn't like a tech startup
| purposely underpricing itself initially then worrying when
| users don't retain after future price hikes. To put it
| another way, Nike would have been attractive for a PE firm
| today, unlike most startups today.
| Animats wrote:
| That's a different kind of business problem. Each
| transaction is profitable but profits are not sufficient
| to grow fast. This is different from each transaction
| being a loss.
| huijzer wrote:
| I completely agree with you that some/many VCs spend
| money on ridiculous business models. On the one hand, it
| seems like a waste of resources. On the other hand, you
| could also say that it's a great way for innovation to
| happen. Maybe some ideas made no sense at all, but worked
| and lead to a technological breakthrough? If VCs wouldn't
| fund moonshot ideas with many millions then who? Apart
| from a few universities, most universities I've been are
| absolutely terrible at getting people and resources
| aligned towards a common goal.
| satvikpendem wrote:
| (2022) article. Interestingly, a lot has changed in just under 9
| months in the AI world. GPT 4 has come and it's actually an AI
| crunch, not a gain. I wrote in another post I submitted but the
| gist is that bootstrapped startups and incumbents will be the
| true winners while VC backed startups won't, because there is no
| moat in AI to defend their high valuations.
|
| https://news.ycombinator.com/item?id=36761643
| nopinsight wrote:
| GPT-3.5, GPT-4, and similar are fairly new. There are many uses
| of the technology that remain unexplored by product people, not
| to mention the technology is getting more advanced and has more
| capabilities by the week (or day or month, depending on your
| perspective).
|
| The new AI platform may over time enable more products than
| even the shift to mobile.
|
| ADDED:
|
| What's implied is that the moat could be built and some kinds
| of moats are not yet well-known or prevalent. Proprietary data
| is often mentioned. But also the application on top of LLMs (or
| LFMs) needs not be just a thin layer with little technical
| barrier.
| [deleted]
| kozikow wrote:
| Data is moat in AI.
|
| I work in a domain of applying AI to specific enterprise
| domain. It's not like you can crawl our data in the open web.
| Getting any data from clients is years of lawyer struggles and
| chicken and egg problems to solve. Fine-tuning models to client
| expectations - they are not going to go through the process
| again with someone else.
|
| And moat in B2C AI is owning tons of your personal data and
| habits that Google and Facebook do. It's just not trully
| utilized with GPT models yet.
| lowkey_ wrote:
| I heard from a large AI founder recently on this topic. Data
| is an okay moat, but in this craze we'll see the power of
| data shrink. Companies are getting enough VC funding
| ($10m-$100m+) to buy any data they need. A better model could
| also make up for a lack of better data.
|
| Instead, the best moat is to know that your product isn't a
| thin replicable wrapper for ChatGPT but instead has a large
| surface area, with lots of well-built features. Continue
| building those features at a fast pace, and you can win.
| 2sk21 wrote:
| Completely agree - much reduced opportunities for
| differentiation with the new LLMs.
| futurisold wrote:
| Very insightful piece. Excellent foresight.
| Animats wrote:
| It _is_ different this time, though. Take a look at this open
| source project.[1]
|
| This is a system which lets you talk to NPCs in video games. It's
| a collection of off the shelf components held together by some
| Python code. The components do this:
|
| - Listen to the user talking and convert speech to text.
|
| - Watch the user's facial expressions via webcam.
|
| - Watch the game, and use face recognition on the game images to
| determine what character is being addressed.
|
| - Run the user's text through a LLM preloaded with about 30 lines
| of info about the NPC to generate a reply.
|
| - Generate voice output in a voice generated to match the
| character's persona.
|
| - Modify the image of the character on screen to animate their
| facial expressions to match the voice output. This is done on the
| output image, _not_ by animating the 3D character.
|
| Five years ago, that was science fiction. A year ago, half that
| stuff wouldn't work right. Now it's someone's hobby project.
|
| [1] https://github.com/AkshitIreddy/Interactive-LLM-Powered-NPCs
| FishInTheWater wrote:
| But it isn't different. People have been using things like
| Markov chains to experiment with NPC dialogue for well over a
| decade.
|
| It just never got widespread adoption because it's just _not
| interesting_ , and LLMs are no different here. The dialogue is
| still _empty_ , despite being deeper and more grammatically
| complex than previous attempts.
|
| If every farmer in an RPG hands out the same "collect 20 bear
| asses" quest it doesn't matter if they all have "detailed"
| randomly generated backstories and can opine about the game
| world, real world philosophy, or the 2024 US elections.
| lowkey_ wrote:
| I actually think it makes a world of difference to opine
| about the game world. It's so much more immersive.
|
| Have you ever gone to a living history museum? (Old
| Sturbridge Village is one example, my favorite I've been to).
| All these people in character, able to talk about the period,
| it makes for an amazing experience.
|
| In traditional video games, if we try to or even accidentally
| push any deeper, we see the cracks in the universe. "Oh, I
| spoke to this person again, and they said the same thing to
| me." AI can help fix those cracks, and fill them in wherever
| the player ventures.
|
| This certainly doesn't change Fortnite, but I think it could
| change immersive RPGs and MMOs.
| FishInTheWater wrote:
| "Living History" is a well crafted written experience, not
| procedurally generated slop.
|
| The issue here is that LLMs can only act in-character _if
| the world has already been built and written_ , if the
| prompts are so pre-chewed that you may as well just write
| the dialogue directly and get even better results.
|
| Take Solaire of Astora. He's an interesting NPC not because
| of any depth of the dialogue, but because of how well in-
| tune he is to the world and game itself. A true believer in
| the old god, a beacon of optimism in a depressed dying
| world, and someone who sets the tone of the co-op
| multiplayer to be silly and fun.
|
| You can't get that out of an LLM.
| germinalphrase wrote:
| "'Living History' is a well crafted written experience,
| not procedurally generated slop"
|
| Having known people who lived/worked at a living history
| museum, their experience was much closer to
| improvisational comedy than a scripted interaction. Sure,
| they were riffing on their historical knowledge instead
| of cracking jokes, but it was not scripted.
| Animats wrote:
| > It just never got widespread adoption because it's just not
| interesting.
|
| True. There have been NPC systems where the NPCs had
| motivations and a life of their own, even when no one was
| around. Those haven't helped gameplay much.
|
| The current problem is that LLMs don't know enough about the
| game world. Recent progress on that.[1]
|
| [1] https://arxiv.org/abs/2304.03442
| notahacker wrote:
| I think this is a pretty good illustration of why incumbents
| are likely to capture much of the value though. Some simple
| scripts using OSS libraries can do some pretty amazing stuff
| you'd have previously needed advanced research teams to even
| attempt.
|
| So the majority of the value gets captured not by companies
| focused on writing new components that nobody else can match,
| but by the incumbents with the wealth of proprietary data to
| feed into the components, or the infrastructure to run the more
| infrastructure-dependent models at scale, or the customer base
| to milk for AI-enabled versions of their existing product or
| selling AI consulting services.
|
| Don't get me wrong, it _is_ cool for indie game developers to
| be able to procedurally generate NPC conversations. But indie
| game developers are not likely to capture more value from being
| able to generate stuff very easily than Microsoft.
| quickthrower2 wrote:
| Really tough one to guess. If the small laptop-run models win
| (become useful enough), the value may be captured by the commons,
| with various applications (glue code essentially) capturing the
| value. A bit like the early internet scenario - good for
| startups.
|
| Likely NVidia, AWS, Azure, Google Cloud will capture a lot of the
| value. OpenAI might, but they are playing a game of tennis where
| they are "Advantage" but could still lose.
| vonnik wrote:
| The reasons why startups did not capture a lot of value in the
| last wave of AI was because incumbents held the data and ML was
| primarily a feature added to someone else's product and
| distribution channel.
|
| The reason why ChatGPT changed that is because they developed an
| algorithm/model good enough to offer a consumer-grade
| conversational interface and they scraped the web to train it.
|
| That is, they offered a whole product and nailed distribution so
| they could own the relationship with the user.
| numbers_guy wrote:
| Why would I use a Google LLM or a Facebook LLM over OpenAI's LLM?
|
| Google and Facebook are today's knowledge dealers. They do not
| profit from providing an LLM that sidesteps all their products
| and gives you the answer you are searching for directly. They
| want to influence your eyeballs. They will try to do this by
| injecting their own thought manipulation crap in their LLMs. I
| instinctively would not trust them. I would want an LLM that is
| pure in some sense. Unfortunately, even OpenAI is already
| debased, but for another reason.
|
| But here you can see the value that a startup can provide over
| the current incumbents. A startup can provide an unadulterated
| knowledge base of the internet and be profitable. Whether that is
| OpenAI or one of its competitors I do not know, but Google and
| Facebook cannot do that. There is no gain for them.
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