
Kevin A. Bryan
11.9K posts

Kevin A. Bryan
@Afinetheorem
Assoc. Prof. of Strategy, U Toronto Rotman | Chief Economist, CDL Toronto | Co-Founder, AllDayTA | Ars longa, vita brevis, occasio praeceps (especially now)




The latest IPCC offers hope that we can limit warming, but time is short. In today's @nytimes, we make a case for which decarbonization issues should be prioritized and which debates distract us from our shared, near-term goals: nyti.ms/3uq3hy0

Note: I looked into whether this was an effect of economists talking about “artificial intelligence” more (long words) and it didn’t seem to be that. Though there has been an explosion in AI related terms as economists have caught the AI bug -



A big story that most people are missing in the AI race for the consumer (ChatGPT vs Claude) is ads. Right now, most consumer AI revenue is coming from power users who are willing to pay high cost subscriptions. This currently skews positive for products like Claude - but this will not be the end state. Google makes ~$460/ user/year in the U.S., mostly on ads. Meta makes around ~$250. I would argue ChatGPT’s ad-based ARPUs will be even higher as they will ultimately have deeper / more frequent user engagement. Even at the $460 level - monetizing everyone in the U.S. via ads is $152 billion in annual revenue. By contrast, if you’re able to monetize even 5% of the population on a $200/month subscription (which is a stretch!), that’s only $40 billion 🤔 I suspect this will be even more drastic outside the U.S. where users are even less willing or able to pay directly for subscriptions. And, the earliest data from a very small rollout shows ChatGPT ads are already outperforming Meta in effectiveness - this just gets better over time. TL;DR - I would not count ChatGPT out on consumer AI revenue. Once ads start working, that can quickly become a massive machine.

Packy and I spent the past month unpacking world models from first principles. This piece is the result of that exploration. We go into why, what, how, and look out into the future on the implications of our work.


7/ After the paper was finalized, we ran agentic systems that mimic how humans would learn to solve problems in esoteric languages. We supplied our agents with a custom harness + tools on the same benchmark. They absolutely crushed the benchmark. Stay tuned 👀



Enough is enough. Just because you can generate an academic paper in minutes doesn't mean you should. When your name is on something, you should check every reference and claim before submitting. If you can't be bothered to do that, you should be banned from submitting.

4/ My boring take as a non-software-developer is these jobs will exist in the future and that they will be very different from the past. I don't know how many there will be. This incipient recovery *may* be the early innings of that transformation.



A mini-rant abut AI and longevity. They say "Artificial Superintelligence would take only a few years to cure cancer, solve longevity, and defeat death itself'. This is a common claim by pro-AI lobbyists, accelerationists, and naive tech-fetishists. But the claim makes no sense. The recent success of LLMs does NOT suggest that ASIs could easily cure diseases or solve longevity, for at least two reasons. 1) The data problem. Generative AI for art, music, and language succeeded mostly because AI companies could steal billions of examples of art, music, and language from the internet, to build their base models. They weren't just trained on academic papers _about_ art, music, and language. They were trained on real _examples_ of art, music, and language. There are no analogous biomedical data sets with billions of data points that would allow accurate modelling of every biochemical detail of human physiology, disease, and aging. ASIs can't just read academic papers about human biology to solve longevity. They'd need direct access to vast quantities of biomedical data that simply don't exist in any easy-to-access forms. And they'd need very detailed, reliable, validated data about a wide range of people across different ages, sexes, ethnicities, genotypes, and medical conditions. Moreover, medical privacy laws would make it extremely difficult and wildly unethical to collect such a vast data set from real humans about every molecular-level detail of their bodies. 2) The feedback problem. LLMs also work well because the AI companies could refine their output with additional feedback from human brains (through Reinforcement Learning from Human Feedback, RLHF). But there is nothing analogous to that for modeling human bodies, biochemistry, and disease processes. There are no known methods of Reinforcement Learning from Physiological Feedback. And the physiological feedback would have to be long-term, over spans of years to decades, taking into account thousands of possible side-effects for any given intervention. There's no way to rush animal and human clinical trials -- however clever ASI might become at 'drug discovery'. More generally, there would be no fast feedback loops from users about model performance. GenAI and LLMs succeeded partly because developers within companies, and customers outside companies, could give very fast feedback about how well the models were functioning. They could just look at the output (images, songs, text), and then tweak, refine, test, and interpret models very quickly, based on how good they were at generating art, music, and language. In biomedical research, there would be no fast feedback loops from human bodies about how well ASI-suggested interventions are actually affecting human bodies, over the long term, across different lifestyles, including all the tradeoffs and side-effects. It's interesting that most of the people arguing that 'ASI would cure all diseases and aging' are young tech bros who know a lot about computers, but almost nothing about organic chemistry, human genomics, biomedical research, drug discovery, clinical trials, the evolutionary biology of senescence, evolutionary medicine, medical ethics, or the decades of frustrations and failures in longevity research. They think that 'fixing the human body' would be as simple as debugging a few thousand lines of code. Look, I'm all for curing diseases and promoting longevity. If we took the hundreds of billions of dollars per year that are currently spent on trying to build ASI, and we devoted that money instead to longevity research, that would increase the amount of funding in the longevity space by at least 100-fold. And we'd probably solve longevity much faster by targeting it directly than by trying to summon ASI as a magical cure-all. ASIs has some potential benefits (and many grievous risks and downsides). But it's totally irresponsible of pro-AI lobbyists to argue that ASIs could magically & quickly cure all human diseases, or solve longevity, or end death. And it's totally irresponsible of them to claim that anyone opposed to ASI development is 'pro-death'.








Unbeatable city building and tourism strategy: Create beautiful architecture, develop a strong cafe culture, and plant tree lined streets everywhere

