AI is all the “rage” lately and the noise ratio is extremely high out there. A cool head is needed. Here I would like to ZOOM OUT and lay out some high-level thoughts/questions, hoping they may serve as “axioms” to further think through the practical actions and directions for start-ups and investing.
- AI hype is over-rated in the near term and under-rated in the long term. At least we can all agree that this time is more tangible and value-adding than the hype around Crypto, Web3, and Clubhouse. Get-shit-done-ethos and continuous creative tinkering will see the AI experience improve from good enough/a toy to irreplaceable workflow tools for knowledge workers.
- However, Web3 (in a narrow sense an immutable identity infrastructure) cannot be written down yet as we may increasingly need a neutral infrastructure that distinguishes human vs. machine-generated content. Though this could be a long shot and irrelevant as convenience always trumps privacy for the majority of people. As machine may soon run out of human knowledge scraped from the web and begins using second derivatives of existing content to self-evolve. A side effect could be a proliferation of “fake” & “junk” content. Some humans will play a part in this because there is a profit to be made in harvesting attention.
- If AI is like a virus, to get max diffusion, it needs to become various form factors that are great at aiding the average human at their existing jobs to avoid social push-back and reach a critical mass of infection/adoption. Once enough population is infected, it may get a life/inertia of its own just like how wheat became the most successful grass on the planet because it is useful to humans, so humans planted it in mass.
- Schumpeter’s creative destruction is a process but there is a spark that ignites the process, ChatGPT is that. True technological innovation is deflationary as productivity per knowledge worker will be enhanced by AI-enabled tools. The search experience will be enhanced, but the search business model will get worse before it gets better until costs to serve per LLM-based query come down and new search modalities get created and adopted, along with new ways to monetize. The pie for old biz models will get smaller with some product categories seeing a shrinking revenue/profit pool while others get created and become larger.
- Sometimes it is not the best technology product that wins, but the one good enough with the largest distribution and reaches a critical mass of adoption. Yann Lecun describes “LLM is an off-ramp on the high-way to Human Level AI”, but AI does not need human-level intelligence to be dangerous. Here lies the difference between an entrepreneur and a well-regarded scientist. The next-gen product (LLM-based or alternative architecture) will need to be significantly better before investments/ecosystems coalesce around the current best solution, namely LLMs. Do you go all-in on LLMs or do you hedge against another paradigm shift?
- Google is facing the classical innovator’s dilemma in the AI search war. Microsoft used minimal capital and risk (with an arms-length entity Open-AI) in gaining PR momentum and causing maximum threat (perceived or real) to Google’s core search business. Even taking away a 5% search market share is enough to cause pain to Google as the moat around search has not seen any real challenges in the past 20 years. On paper, Google should cannibalize its own business before someone else cannibalizes it for them. That could take two paths 1) pay up for TAC (Traffic Acquisition Cost) and compress Gross Margin to gain proprietary access to certain data and hold on to publishers 2) Open-source its models (vs. OpenAI currently chose to closed-source) like how it did with Android. However, history rhymes but does not repeat in the exact same way. What will Google do this time? Google open-sourced Android because when it saw the 1st gen iPhone (Google actually was building a phone before iPhone came out), the iPhone was so good that Google realized it could not compete with Apple in designing smartphones, so it scorched the earth by open-sourcing its Android system to secure distribution for its search product. Smart move! The circumstance is different this time. On the plus side, Google
- In the application layer, U.S. and China will again diverge as parallel universes due to cultural and institutional differences, technology diffusion and application are not only influenced by basic scientific advances but also cultural nuances, if not much more. China lags behind the U.S. in LLMs development (by two years?) and now has restricted access to the most advanced AI chips, but it is less constrained by conservative vs. liberal debates (for good or bad) and more driven by government directives and pure animal spirit.
The trend is here but there are many moving forces at play. Next, I would like to ZOOM IN to think through practical problems/actions from the ground up.
Note：The following is translated from English to Chinese using ChatGPT, with minor edits (less than 5%):
- 有时，获胜的不是最好的技术产品，而是分发最广、达到关键采用量的产品。Yann Lecun认为，“LLM是通往人类级别AI的高速公路的一个出口”，但AI并不需要人类级别的智能就可能具有危险性。这就是企业家和备受尊敬的科学家之间的区别。下一代产品（基于LLM或替代架构）需要在投资/生态系统围绕当前最佳解决方案（即LLM）形成共识之前显着提高性能。你是要全力支持LLM，还是对抗另一种范式转变？
- 谷歌正在面临AI搜索战中的经典创新者困境。微软通过使用OpenAI这样一个独立实体来获得公关动力并对谷歌的核心搜索业务构成最大威胁（无论是实际上还是虚构的）。即使夺走5%的搜索市场份额也足以给谷歌带来痛苦，因为在过去20年中，搜索的护城河没有受到任何真正的挑战。纸面上幼稚的看，谷歌应该在其他人将其替代之前蚕食自己的业务。这可以有两种路径：1）支付更高的TAC（流量获取费用）并压缩毛利率以获得对某些数据的专有访问并保持publisher的粘性；2）像Android一样开源其模型（与OpenAI当前选择的闭源不同）。然而，历史会重演但不会以完全相同的方式重复。这一次谷歌会怎么做？不过，微软和ChatGPT也许给了谷歌借口和美国Federal Trade Commission辩论，你看我不是垄断。