AI’s Infancy and Future: Key Takeaways from Benedict Evans via Lenny Rachitsky

In a recent LinkedIn post, Lenny Rachitsky shared his key takeaways from insights by Benedict Evans regarding the current state and future implications of Artificial Intelligence. Rachitsky frames the present moment for AI as analogous to 1997 for the internet, emphasizing that the technology is still nascent and its most significant impacts are yet to be fully realized or even imagined.

As Rachitsky highlights, the current AI landscape mirrors the early days of transformative technologies:

“We’re in 1997 for AI—it’s as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. We’re at the stage where most stuff kind of doesn’t work yet, most of what people will build hasn’t been built, and it’s not clear how any of it will work when it does.”

He further elaborates on this nascent stage, noting the limited current adoption among younger demographics and the uncertainty surrounding future dominant companies and use cases.

Navigating the Risks and Rewards of Technological Waves

Rachitsky, channeling Evans’s perspective, underscores that every major technological advancement carries the potential to disrupt and negatively impact lives, whether intentionally or unintentionally. He argues for a balanced approach, acknowledging these risks without succumbing to panic.

According to Rachitsky:

“Every wave of technology—databases in the 1970s, social media in the 2010s, AI today—creates new ways to harm people. We need to be conscious of these risks, build safeguards, and hold people accountable. But we also can’t let fear of potential harms stop us from capturing the benefits.”

This perspective suggests a need for proactive governance and ethical considerations as AI development progresses, ensuring that potential harms are mitigated while the benefits are pursued.

Historical Parallels in Automation and Employment

Rachitsky delves into historical patterns of automation, citing the accounting profession as a prime example of how technological advancements can paradoxically lead to increased employment rather than widespread job losses.

The Jevons Paradox in Practice

He explains this phenomenon using the Jevons paradox, where increased efficiency and reduced cost of a resource (in this case, tasks through automation) lead to its expanded use and, consequently, often to an increase in overall employment in related fields.

“Despite adding machines, punch cards, mainframes, databases, ERP systems, cloud software, spreadsheets, and PCs, the number of accountants keeps going up. This is the Jevons paradox: when you make something cheaper or easier, you don’t do the same amount of work for less money. You often do vastly more because the ROI changes.”

This historical lens suggests that while AI will undoubtedly automate certain tasks, it is likely to create new roles and expand economic activity in unforeseen ways, even if the transition involves temporary individual hardship.

Evolving Moats and Value Accrual in the AI Era

Rachitsky also touches upon the shifting competitive landscape driven by AI. As building software becomes more accessible, Rachitsky points out that ‘distribution’ is emerging as a more critical competitive advantage.

He elaborates:

“As AI makes building software cheaper and faster, the market gets noisier. More products launch, more companies compete for attention, and breaking through becomes harder. This means distribution—the ability to reach customers and get them to use your product—matters more than ever.”

Furthermore, Rachitsky questions the long-term pricing power of foundational AI model companies. He suggests that without strong network effects and with increasing competition, value is more likely to accrue to companies that build on top of these models and leverage unique distribution channels, rather than the model providers themselves.

Finally, Rachitsky notes the strategic partnerships between leading AI firms like OpenAI and Anthropic with consultancies and private equity firms, interpreting this as a sign of the ongoing effort to integrate AI into existing business workflows and unlock new use cases, even for entities that might seem self-sufficient.