This is a summary of the most important insights from the article “Is AI the new crypto?” by John Luttig”.
95% of this artcile was summarized by AI, with the 5% being small edits and structural organization of the article.
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The most important insights from the article are that AI may be able to avoid the downfall of inflated expectations that crypto experienced, but only if it is navigated carefully. AI has a different capital context than crypto, and its mission is not as clear. AI also has the potential to be mission-neutral, allowing for a wide range of application-level missions. Finally, AI has the potential to be corrupted by grifters and financialists, and is at risk of regulatory scrutiny.
The two most important Insights from the Article:
AI may be the only sector wearing a parka in the nuclear winter techpocalypse.
Crypto provides a cautionary tale for excessive hype, with four critical flaws: capital, mission, people, and value creation.
Flaws from crypto, as compared to AI:
Capital: Two capital problems in crypto that may also be present in AI are: a capital vs. progress mismatch, and VC short-termism, where crypto-specific funds provided a steady flow of capital into the ecosystem, but progress in the industry was not constant. AI avoids the capital problems of crypto, with generalist funds keeping the bubble in check.
Mission: AI’s mission is not as crystallized as crypto’s, but it should be mission-neutral at the infrastructure level. Crypto's original pioneers were libertarians and anarchists, but disorganized grifters flowed in as the ecosystem matured, attracting regulatory scrutiny.
People: AI has a purer talent evolution arc, but the same risk of regulatory scrutiny.
Value creation: AI has the potential to create real value for users, unlike crypto which generated more value for token holders.
Challenges for the AI industry
The article suggests that AI has a purer talent evolution arc than crypto but faces the same risk of regulatory scrutiny.
Cultural scaling challenges in AI include absorbing an influx of "tourists" into the industry, while keeping genuine technologists in charge and filtering out negative human capital.
The best cultural control mechanism is to keep technologists in charge, as there is no trading element in AI, only building and leveraging the core technology.
Regulatory risks for AI include political burdens and strict geopolitical boundaries, as well as concerns about AGI replacing human labor.
The article argues that, unlike crypto, AI has already created value for users through its applications in big tech and other industries.
It also suggests that while crypto is still in the "infrastructure phase", encumbered by self-referentiality, AI is already being used in a variety of valuable ways.
Conclusion
The article concludes that for AI to avoid the fate of crypto, it needs to focus on four key areas: capital, mission, people, and value creation.
Capital: AI needs to productionize its applications to justify continued capital influx and avoid a bubble.
Mission: AI needs a positive or at least neutral and apolitical mission to disprove dystopian predictions.
People: The industry will have its fair share of grifters but AI leaders need to prevent them from having control over the ecosystem.
Value creation: AI needs to transition from interesting toys to trusty tools to ensure adoption and success.
The article suggests that in the long term, value creation should dominate and AI is already winning handily in this area, with businesses and consumers already benefiting from the technology.
Gartner hype cycle comparisons
The author include a graphical reference to the Gartner hype cycle which might play out for AI, he also proposes a modified Gartner hype cycle.
The Gartner hype cycle suggests that what goes up must come down, but it's also noted that the plateau of productivity may vary dramatically by industry, AI may plateau far above the historical peak.