AI: Startup Vs Incumbent Value

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There may be other reasons. Let me know what you think on HN or Twitter :)
Will this AI wave be different?
I have worked on AI-driven products for a long time. I worked on ads targeting at Google 15 years ago (in addition to kick starting many of the mobile efforts there) and then for a period worked on search product at Twitter (before taking on more operational intensive business areas). I co-founded Color which started off focusing on big-data, ML, and genomics (and has since morphed into a virtualized healthcare delivery company) and have also invested in AI related companies for 10+ years. 
While many of the prior innovations in AI were striking and exciting (AlexNet, CNNs, RNNs, GANs etc) this time does feel different for a few reasons. There is reason to believe while incumbents should capture a good amount of the value in this wave, startups will take a bigger share of AI generated value this time around.
Differences include:
1. Better tech is coming across many areas.
One of the remarkable things about this current technology wave is the speed of innovation across many areas. Future GPT-like language models (GPT-4? GPT-N?) should increase the power, fidelity, and reach of natural language across consumer and B2B in deep ways and potential change everything from human interactions (dialogue based interactions?) to white collar work (co-pilot for anything that touches text, by vertical). In parallel, advances in image generation, speech to text [LINK TO WHISPER], text to speech, music, video, and other areas are happening. One can imagine 4-5 clear business use cases from image-gen, from better versions of various design tools to storyboarding for movie making. Which of these uses cases are won by startups versus incumbents remains to be seen but one can guess for a subset based on the strength or nimbleness of existing incumbents.
This time, the technology seems dramatically stronger, which means it is easier to create 10X better products to overcome incumbent advantages. The " why now " may simply be a technology sea change.
2. New tech means there are startups providing valuable infrastructure to the rest of the industry. 
Unlike the prior wave of AI startups, there are a clear set of infrastructure-centric companies with broad adoption and rapidly growing usage - this includes OpenAI , Stability.AI , Hugging Face , Weights and Biases , and others. While revenue is lagging usage for a subset of companies in this segment, it is ramping quickly in a manner not atypical for open source or API centric business models. 
OpenAI is now the clear leader in LLM APIs - a position that 4 years ago Google was arguably in the default position to win. The failure of Google to capitalize on its many advantages specifically in AI has been striking. It feels like a Xerox Parc moment of inventing transformers , having all the talent, data, and distribution to build the seminal infrastructure for the industry, and then having a startup show up, Apple-like, to drive the industry forward[0].
Similarly, HuggingFace, Weights and Biases, and others are providing tools for the AI industry in ways that incumbent dev tools companies have failed to do to date.
3. There are clear app use cases without strong incumbents.
A number of the earliest use cases and startups - for example marketing copy (Copy.AI or Jasper), Image Gen (Midjourney, Stable Diffusion, etc.) and code gen (Github Copilot, Replit) are seeing nice adoption and growth in a way that did no exist in the prior AI wave. 
In general, this wave of AI applications seems to do best in markets where:
There are highly repetitive, highly paid tasks (code, marketing copy, images for websites etc)
Imperfect fidelity is fine, as you have a human in the loop who wants to review the items (which creates a nice feedback loop or future training set). Human in the loop is not necessary, but seems to be a common feature to date.
Workflow tools do not exist or are weak for the use case, so the AI features become a core and useful part of a broader workflow tool
Summarization or generation of text or images is useful for the product application - this is enabled in a high fidelity way by new AI tech in a way that did not exist before. 
So far, companies with these characteristics seem to be the sweet spot for this wave of ML. Other things like voice transcription, robots, video etc. all on their way as well which will broaden next-gen AI use cases.
Focus on end-used and markets
The key with all this exciting tech will be to avoid the hammer-looking-for-a-nail problem. It will be important to identify actual end user needs and unserved product/markets that will benefit from this wave of exciting technology.
As the builders in the market shift from research scientists to product-centric builds (including, of course,  some product-minded research scientists) we should see a blossoming of new machine learning driven applications. This will likely be a 10-20 transformation similar to cloud which is itself still ongoing.
Scale matters
When thinking about startup versus incumbent value it is important to remember the scale of incumbents. For example, a 10% increase in Google's market cap is currently $130 Billion, or the equivalent of almost 7 Figmas, 4 Snowflakes, 17 Githubs, or 130 Stability.AIs! The market caps of incumbents have gotten so large that even small changes can add up to entire ecosystems or market segments.
Given the likely coming impact of AI, one could imagine one or more truly massive startups being created. Even if incumbents capture most of the value this time due to raw scale, startups should participate in a significant way in new market cap and impact to the world. Certain market segments (e.g. search) might become vulnerable again for the first time. After having personally worked for 15 years on AI-related products directly, or investing in them, it feels like startups will finally start to get real value from AI. Exciting times lie ahead![1]
NOTES
[0] Xerox Parc famously invented the GUI, the mouse, etc and then demo’d it to Steve Jobs who launched it all with the Apple Mac. Google invented transformers and informed the world about it. OpenAI capitalized on this technology the best so far.
[1] I could of course be wrong on all this. If so expect Stability.AI and Hugging Face to be taken public via SPAC in the bubble of 2030 as the Fed drops rates and the government does massive inflationary money drops for the Great Panic of 2030 [2] and creates the mother of all bubbles.
[2] This panic will, of course, be due to either a global over reaction to something that isn’t really truly that bad (™), or an avoidable policy error that leads to either a giant energy crunch[3] or some mass escalation or geopolitical problem[4].
[3] Whoops. Maybe we shouldn’t have shut down so much global power generation (and the knock off effects on fertilizer, food prices, and prices for everything with energy as a cost input, which is roughly everything) due to the Stockholm-Paris-Seattle protests[5] of 2027?
[4] China-Taiwan? Other?
[5] “Largely peaceful” protests of course[7]. The good news is Seattle is now entirely a giant autonomous zone named SNAZY (Seattle North Autonomous Zone - Yes!), which is quite the snazzy acronym[6].
[6] This goes to show that alongside biologists and the DoD, anarchist activists also like acronyms.
[7] This is a lie. They were not peaceful, but were covered as such by the media for some reason.
MY BOOK