Questions about AI

The more I learn about AI, the more questions I have. These are some I keep coming back to.

I was reading Elad Gil’s excellent post about AI questions and it compelled me to write down some of the questions I’ve been thinking about:

1. Scientific progress

   1. How much will continued human-led scientific progress in non-AI fields matter in the long run?

   2. How should ambitious people decide between pursuing deep domain expertise vs trying to push the AI frontier forward? For e.g., when all cancers are cured, will we look back and be more indebted to individuals who pursued AI research or oncology research post 2022?

   3. Do we need new technical breakthroughs to get to AGI?

   4. Will the step change from GPT4 to GPT5 will be as large as the one from GPT3 to GPT4? When does progress start to asymptote?

2. Training data

   1. The first LLMs required large amounts of training data to work. Will we reach a point soon where more human-generated training data won’t be needed?

   2. What role does synthetic data play in the long run?

   3. Lack of training data is the main constraint holding back faster progress in robotics. How does this get solved?

3. Regulation & piracy

   1. There are many things LLMs don’t let you do like generate videos with copyrighted characters or porn. Are we about to enter an LLM era of piracy that will be like the torrenting era of the mid 2000s - early 2010s?

   2. If we don’t enter a piracy era, how will these use cases be supported given the obvious demand?

   3. If we do enter a piracy era, how long will it last and will it be followed by a legitimization period much like what happened with Netflix/iTunes? Will incumbents adapt or get disrupted?

4. Business

   1. Which company will have the assistant with the most users in the long run? ChatGPT is the early leader but it doesn’t have access to most of my data — email, calendar, Slack, Notion, etc. And while ChatGPT has a plugin ecosystem, will big tech companies let another company own the relationship to end users?

   2. Will each tech giant have their own assistant and plugin ecosystem, and then leverage their large user-bases to force smaller B2B companies to build plugins? How much room is there for “all-in-one assistants & plugin ecosystems”? Does Glean have a shot at being very successful for enterprise use cases vs the tech giants?

   3. Many smaller B2B startups like Notion have or are building their own assistants. Will this trend continue or is it short-lived because narrow assistants all suffer from insufficient context (for e.g. Notion’s assistant not knowing about Slack conversations)?

   4. Google is paying Reddit $60M/yr for training data. Will UGC platform like Reddit make an increasing amount of revenue from data sales? If so, what changes? Will Reddit focus more on creating product experiences that result in higher quality data vs. more traffic for advertisers?

5. Startups

   1. We got the iPhone in 2007 and the most successful mobile-enabled startups in 2009 (Uber, WhatsApp), 2010 (Instagram), 2011 (Snap), 2013 (Robinhood, Doordash), and 2016 (TikTok). So the average very successful mobile-enabled startup started ~4.5 yrs after the iPhone release. ChatGPT launched late 2022, so we’re only ~1.25yrs into the new AI paradigm. Are we so early into the AI era that perhaps none of the long-term AI winners have been founded yet? Or is there something structurally different about this wave that makes being early more important?

   2. Will value from AI mostly be captured by large incumbents or by startups?

   3. Many AI pioneers are Canadian (Geoffrey Hinton, Ilya Sutskever, etc.). Will we see any massively successful Canadian AI startups?

   4. Will human and synthetic data live side-by-side in UGC platforms? Or will we see products like “YouTube for generative content” as generative data makes up an increasing fraction data on the internet?

   5. What happens to SEO? What tricks will marketers come up with to get included in assistant responses? Who’s building the Ahrefs / Semrush for the generative data world?

   6. Will any organization build a “Common Crawl for robotics” or will these dataset be guarded by private companies?

6. Competition

   1. How will the relationship between Microsoft and OpenAI evolve? Microsoft owns a huge fraction of OpenAI, but the stipulation that OpenAI does not share post AGI technology with Microsoft must motivate Microsoft to build independent expertise.

   2. How much room is there for new foundation model companies? How screwed are Pika and Runway in a world where OpenAI keeps investing billions in Sora? Should new startups be building foundation models?

   3. To what extent will profit be competed away for foundational model companies? What gives these companies pricing power in the long run? Will it be more important that models are smart or that they have access to unique and time-sensitive data sets like X, Reddit, and the NYT?

   4. What’s the future of open-source models? So far it looks like the most capable models won’t be open-source (too much compute $ needed) and the most knowledgeable models won’t be open-source (no access to expensive proprietary datasets). But maybe those limitations are fine for the majority of B2B and B2C use cases?

   5. What happens with Perplexity? They have a much better product than Google’s and Microsoft’s assistants, but is that enough to be massively successful?

7. Google

   1. What happens to google.com in the long run? Does an assistant fully replace search, will there be toggles to switch between both paradigms, will the interface depend on the search input, or something else?

   2. How much of an advantage is having access to YouTube’s video corpus?

   3. How does Google keep increasing revenue overall and ad revenue specifically in a world where assistants are monetizing directly without ads?

   4. Will the bet in TPU architectures pay off or will it be a crutch in the long run?

   5. How will Google handle the backlash of choosing particular LLM responses in their models (vs being able to deflect blame to an algorithm that merely ranks external links)?

   6. Does Sundar get fired because he failed to get Google to an early lead despite having every conceivable advantage?

8. Arts & creativity

   1. We’re not far away from a hyper-personalizable future where anyone for e.g. can generate their own ending to Game of Thrones. But in a world where this capability exists, will people be excited by it? So much of what makes media & entertainment great is the shared global context — i.e knowing that after a season finale drops you can talk about it with anyone IRL, in subreddits, etc.

   2. AI both democratizes creation (anyone with a phone will be have the power to make Hollywood-quality movies) and destroys the economics of it for some fraction of the population (some copywriters, illustrators, etc.). On the whole, will the impact on creative jobs be more positive or negative?

9. Robotics

   1. Will robotics have an inflection point “ChatGPT moment” or will progress in robotics be more gradual given training data limitations?

   2. How large is Tesla’s advantage given their experience with self-driving cars?

   3. How large is Figure’s advantages given their relationship to OpenAI?

10. Consumer hardware

    1. Is the intersection of LLMs and AR/VR actually interesting or will future generations of Apple Vision Pro / Quest be successful primarily due to other reasons?

    2. Does continued LLM progress create conditions for a consumer device to emerge that’s more popular than the smartphone?

11. Philosophy

    1. How will our understanding of consciousness evolve as LLMs continue to get better?

    2. Will consciousness of some sort emerge in more advanced LLMs?

    3. Are we in the early stages of a crisis wherein we realize that we’re not as uniquely smart and self-aware as we thought?

    4. LLMs have already passed the Turing Test without much fanfare. Will the same be happen when we pass AGI thresholds?