Sure. I just think one should interrogate and really understand the data points being used to support this claim. Let's see how they look when presented as bullet points:
- Nvidia's Revenue and AI Spending: Sequoia says "the industry spent $50 billion on chips from Nvidia to train AI in 2023, but brought in only $3 billion in revenue." - This comes from some Sequoia presentation which it appears was originally cited in an earlier WSJ article and then has been repeated everywhere. It would be nice to see that presentation and the context of this data in that presentation. And yes, this nascent industry in essentially its first year of commercialization brought in less than was invested in anticipation of future growth
- Synthetic Data for Training: "To train next generation AIs, engineers are turning to 'synthetic data,' which is data generated by other AIs. That approach didn’t work to create better self-driving technology for vehicles, and there is plenty of evidence it will be no better for large language models," says Gary Marcus, a cognitive scientist. aka Gary Marcus a noted AI skeptic
- Incremental Gains in AI Models: "AIs like ChatGPT rapidly got better in their early days, but what we’ve seen in the past 14-and-a-half months are only incremental gains," says Marcus. "The truth is, the core capabilities of these systems have either reached a plateau, or at least have slowed down in their improvement." aka Gary Marcus a noted AI skeptic
- Convergence in AI Model Performance: "Further evidence of the slowdown in improvement of AIs can be found in research showing that the gaps between the performance of various AI models are closing. All of the best proprietary AI models are converging on about the same scores on tests of their abilities, and even free, open-source models, like those from Meta and Mistral, are catching up." No citation provided for this "research".
- Commoditization: "A mature technology is one where everyone knows how to build it. Absent profound breakthroughs—which become exceedingly rare—no one has an edge in performance." A broad generalization.
- AI Startups Facing Turmoil: "Some AI startups have already run into turmoil, including Inflection AI—its co-founder and other employees decamped for Microsoft in March. The CEO of Stability AI, which built the popular image-generation AI tool Stable Diffusion, left abruptly in March. Many other AI startups, even well-funded ones, are apparently in talks to sell themselves." People at a couple of start-ups are moving around. Unsourced general claim that unnamed AI startups are looking to sell themselves (is this actually bad news?)
- High Operational Costs: "The bottom line is that for a popular service that relies on generative AI, the costs of running it far exceed the already eye-watering cost of training it... analysts believe delivering AI answers on those searches will eat into the company’s margins." Unsourced "analysts". Would be interesting to see the context of this discussion but also it is not unusual for investment in a new wave of growth to eat into margins initially
- Survey Data on AI Use: "A recent survey conducted by Microsoft and LinkedIn found that three in four white-collar workers now use AI at work. Another survey, from corporate expense-management and tracking company Ramp, shows about a third of companies pay for at least one AI tool, up from 21% a year ago.
This suggests there is a massive gulf between the number of workers who are just playing with AI, and the subset who rely on it and pay for it." Two cherry-picked surveys conducted for marketing purposes jammed together to make an unrelated claim.
- Limited Revenue Growth: "OpenAI doesn’t disclose its annual revenue, but the Financial Times reported in December that it was at least $2 billion, and that the company thought it could double that amount by 2025.
That is still a far cry from the revenue needed to justify OpenAI’s now nearly $90 billion valuation." It is completely normal for the leading edge company showing massive growth in a nascent field to have a huge valuation. It doesn't always work out well for that company but this is expected whether the company is ultimately a success or not and the ability to tap that valuation improves the likelihood of success
- Productivity and Job Replacement: "Evidence suggests AI isn’t nearly the productivity booster it has been touted as, says Peter Cappelli, a professor of management at the University of Pennsylvania’s Wharton School. While these systems can help some people do their jobs, they can’t actually replace them." Non-specific "evidence" is cited here.
- Challenges in AI Usage: "AIs still make up fake information, which means they require someone knowledgeable to use them. Also, getting the most out of open-ended chatbots isn’t intuitive, and workers will need significant training and time to adjust." Author assertion
- Historical Patterns in Technology Adoption: "Changing people’s mindsets and habits will be among the biggest barriers to swift adoption of AI. That is a remarkably consistent pattern across the rollout of all new technologies." Author assertion