Wave 1: GenAI native companies. ChatGPT, Midjourney, Character.AI, Stable Diffusion, Github copilot, and other early launches that have now gained significant revenue and user traction.
Wave 2 (current wave): Early startup adopters and fast mid-market incumbents. This is the first wave of startups to launch on top of GPT-3.5/4 like Perplexity, Langchain, Harvey or others. In parallel, a small number of founder led multi-billion companies like Navan, Notion, Quora, Replit, and Zapier launched AI-powered products quickly and are the early adopters of the wave.
Google’s MedPaLM2 model outperforms human physicians to such a strong degree that having medical experts RLHF the model makes it worse (!).
Rather then view LLMs, Transformers, and diffusion models as part of a continuum with past "AI", it is worth thinking of this as an entirely new era and discontinuity from the past
Value in prior AI waves went largely to incumbents over startups - as the capabilities were not advanced enough to create new market openings.
When many business people talk about “AI” today, they treat it as a continuum with past capabilities of the CNN/RNN/GAN world. In reality it is a step function in new capabilities and products enabled, and marks the dawn of a new era of tech.
ChatGPT’s launch was the starting gun for mainstreaming that AI is a big deal in terms of new capabilities.
Given that large enterprise planning cycles often take 3-6 months, and then prototyping and building will take a year for a large company, we are still very far away from peak AI usage or peak AI hype.
there are likely at least 4 waves of AI to consider in these early days.
Wave 3 (coming soon): Next wave of startups currently being founded. It will be exciting to see what is in this mix and may include new formats like voice and video in addition to using natural language in more verticals and more ways, as well as new types of infrastructure. Companies like Eleven Labs/LMNT/LFG Labs, Braintrust, and many more will provide incremental experiences.
Wave 4 (coming 2024/2025?): First big enterprise adopters. Since enterprise planning and build cycles are so long, anticipate the first really products (versus demos or prototypes) from larger companies other than MSFT, Adobe, Google, Meta to start to show up in a year or two.
It is almost like cars existed, and someone invented an airplane and said “an airplane is just another kind of car - but with wings” - instead of mentioning all the new use cases and impact to travel, logistics, defense, and other areas. The era of aviation would have kicked off, not the “era of even faster cars”.
The biggest inklings that something interesting was afoot came kicked with GPT-3 launching in June 2020. GPT-3 was a massive step up from GPT-2 and prior models. It was not quite good enough to do all the things we now view as hallmarks of “AI”, but it was highly suggestive of what was to come (I went on the A16Z podcast a few months later to talk about GPT-3, as it was so striking). For those in the know, the launch of GPT-3.5 in March 2022 solidified the perception of transformer-based models as the future. Internally at companies like Google, OpenAI, Microsoft, and Anthropic, early access to models gave a subset of people a glimpse of the future that was coming. This led to a Google engineer eventually proclaiming an internal AI chatbot named LaMDA as being “sentient” - this chatbot was a sort of predecessor to chatGPT and products like Character.AI.
Companies like Eleven Labs/LMNT/LFG Labs, Braintrust, and many more will provide incremental experiences. There is a big wave of new startups coming. The current YC batch alone appears to have a 100 or more AI startups….
Glasp is a social web highlighter that people can highlight and organize quotes and thoughts from the web, and access other like-minded people’s learning.