From Arena Magazine, June 3:
When assessing present conflicts, there’s a tendency to focus on the last conflict to produce a mental model. The age of social media was one of natural monopolies. Now it is taken for granted that the age of AI will be the same.
“Tech giants Microsoft and Alphabet/Google have seized a large lead in shaping our potentially A.I.-dominated future,” Daron Acemoglu and Simon Johnson write in the New York Times. “This is not good news. History has shown us that when the distribution of information is left in the hands of a few, the result is political and economic oppression. Without intervention, this history will repeat itself.” This lens — which lacks an understanding of the underlying technology — is uncritically taken to a literal Marxist conclusion: “We believe the A.I. revolution could even usher in the dark prophecies envisioned by Karl Marx over a century ago.”
The curse of every contrarian is for his views to be imitated by the crowd, twisted to the point of cliché. Peter Thiel believed he was stoking controversy when he wrote “Creative monopoly means new products that benefit everybody and sustainable profits for the creator. Competition means no profits for anybody, no meaningful differentiation, and a struggle for survival.” Now, regulators and columnists are assuming that all emerging technologies naturally become monopolistic. This simplistic view fails to account for the distinct qualities and dynamics of each sector. As a result, regulators confident that monopolies are inevitable are setting policies with those assumptions — and with complete disregard for the consequences for startups.
The idea that AI is the next great monopoly is a common belief driving regulators to take pre-emptive action. In July 2023, the FTC released a blog post titled “Generative AI Raises Competition Concerns.” Lina Khan, chair of the Federal Trade Commission (FTC), has made statements implying she believes a monopoly is inevitable. She stated the FTC wanted to act against AI monopolies “before it becomes fully fledged.”
Advisors to Senator Elizabeth Warren, Ganesh Sitaraman, and Tejas N. Narechania, articulated the same sentiment in Politico. “While AI might be new, the problems that arise from concentration in core technologies are not. To keep Big Tech from becoming an unregulated AI oligopoly, we should turn to the playbook regulators have used to address other industries that offer fundamental services, like electricity, telecommunications and banking services.” In an Iowa Legal Review article, Narechania uses the term “‘natural monopoly” to express similar apprehensions: “I find that some machine-learning-based applications may be natural monopolies, particularly where the fixed costs of developing these applications and the computational costs of optimizing these systems are especially high, and where network effects are especially strong.”
But unlike these academics, the market still believes in competition; it’s positively planning on it.
Vertical Disassembly
Venture capital money is going to various layers of competition throughout the AI pipeline. This pipeline can be broken down even further, but let’s proceed with the following six layers:
- Hardware (Nvidia, Groq)
- Infrastructure (AWS, Langchain)
- Data supply / generation / curation (Reddit data repositories, Large Model Systems/lmsys.org)
- Pre-training (OpenAI, Anthropic, Meta)
- Fine-tuning (Some done by base companies, but more widely available)
- Prompting / Software Chain / Audience Specialization
An important takeaway from the current AI ecosystem is that there is rapid diversification throughout the AI pipeline. What was once a single, vertically integrated process done by one company is separated into specialized improvements. As funding becomes more available and demand grows for niche applications of AI, the machine learning process is being sliced into ever narrower, more precise subprocesses. This is vertical disassembly.
The market intelligence firm CB Insights found that while most of the VC money is going into AI infrastructure, the number of deals is much higher in applications.
Even the most dominant player in building foundation models, OpenAI, is moving its strategy in this direction toward applications.
....MUCH MORE
