AI Revolution Mirrors Past Shifts--Focus on Distribution

The AI Revolution: Beyond the Hype to the Real Transformation

The conversation with Benedict Evans reveals a critical yet often overlooked aspect of the AI revolution: its profound similarity to previous technological paradigm shifts, despite the unprecedented speed of adoption. The core thesis is not that AI is merely as big as the internet or mobile, but that understanding how it mirrors those shifts--particularly in its early, uncertain stages--is key to navigating its impact. This discussion uncovers the hidden consequences of assuming AI is fundamentally different, highlighting how traditional business models and career paths will be reshaped. Anyone seeking to build a durable career or a resilient business in the coming decade will gain a crucial advantage by internalizing the lessons of historical technological adoption, focusing on distribution, and embracing the inherent uncertainty rather than succumbing to panic or overconfidence.

The 1997 Moment: Embracing Radical Uncertainty in AI

We are, according to Benedict Evans, living in the "1997" of AI. This isn't to diminish AI's potential; rather, it frames our current moment as one of immense excitement, profound uncertainty, and a significant gap between early adopters and the broader population. Just as the internet in 1997 was a marvel of potential but a functional mess for most, AI today offers glimpses of transformative power, but its practical application, widespread adoption, and ultimate impact remain unclear. The danger lies in extrapolating current capabilities or assuming a linear progression.

"We're in 1997 like it's very exciting most stuff kind of doesn't work yet most of the stuff that people are going to do hasn't been built yet and it's not really clear how any of it's going to work when it does work."

This analogy is crucial because it highlights a systemic pattern: technological revolutions create a wide distribution of adoption and maturity. While tech insiders might be deeply immersed, the majority of the population is still on the periphery, using AI tools sporadically or not at all. This disparity means that predictions of immediate, universal disruption are premature. The real impact will unfold gradually, sector by sector, as workflows are reimagined and new applications are discovered--a process that often takes years, not months. The accountant’s initial reaction to spreadsheets mirrors today’s lawyer or journalist looking at AI: a clever tool, perhaps useful for timesheets, but not yet integrated into core workflows. The true transformation requires not just the technology, but the development of supporting infrastructure and workflows, a process that historically takes significant time.

The Professional Services Boom: Where the Real Work Happens

One of the most counterintuitive observations from the AI landscape is the surge in demand for professional services, consulting, and "forward-deployed engineers" within AI labs themselves. This phenomenon directly challenges the narrative that AI will simply automate away existing jobs. Instead, it reveals a deeper truth about how complex organizational change is implemented. Reimagining internal workflows, integrating new AI capabilities, and training staff are not tasks that AI can easily accomplish independently. They require human expertise, strategic insight, and on-the-ground implementation--precisely the domain of consultants and professional services firms.

"You would think ai is going to like consultants were going to be gone no we don't need all these people anymore ai is going to do their work instead like the most cutting edge ai labs are the ones most investing in these folks it's i think it's pretty surprising."

This trend underscores a fundamental principle: AI is a powerful tool, but it excels at specific tasks, not holistic strategic transformation. The "hard part of the job" often isn't the discrete task that AI can automate, but the overarching strategic decisions, customer understanding, and internal political navigation. Amazon’s success, for example, wasn't just about efficiently delivering a known SKU; it was about understanding customer needs and building a comprehensive ecosystem. Similarly, AI can generate code or draft documents, but it cannot yet replicate the deep understanding of a client's business, the nuances of organizational change, or the strategic foresight that consultants provide. This is why companies, even AI labs, are investing heavily in human expertise to bridge the gap between AI capabilities and real-world business impact. The value accrues not just to the AI model itself, but to those who can effectively deploy and integrate it into complex human systems.

Distribution as the Ultimate Moat: When the Product Becomes a Commodity

As AI models become more capable and easier to access, the competitive landscape shifts dramatically. Evans argues that the foundational models themselves are increasingly becoming commoditized. The true differentiator, and thus the ultimate moat, will be distribution. This echoes historical patterns seen with the internet and mobile. While search engines and social media platforms were initially seen as distinct products, their value was ultimately amplified by their vast user bases and integrated distribution channels.

"If the product is a commodity then the distribution is what matters."

Consider the evolution of web browsers. While rendering engines might have technical differences, the browser as a product became a thin wrapper. Microsoft’s dominance with Internet Explorer wasn't solely due to technical superiority, but its integration into Windows--a powerful distribution channel. Today, companies like Google and Meta are leveraging their existing platforms (Android, search, social media) to distribute their AI models, even if the underlying technology is comparable to competitors. Apple’s strategy with "Apple Intelligence," powered by Gemini, further illustrates this: the value lies not just in the AI model, but in its seamless integration across a billion devices. The crucial insight here is that as the core AI technology becomes accessible and undifferentiated, the ability to reach and serve customers at scale--through existing platforms, strong brands, and user inertia--becomes the primary driver of value and competitive advantage. The focus shifts from building the best model to ensuring the best product reaches the most people.

Actionable Takeaways for Navigating the AI Transition

• Dive In, Don't Deny: Actively engage with AI tools and understand their capabilities and limitations. This is not about embracing AI uncritically, but about developing practical literacy. (Immediate Action)
• Focus on the "Job," Not Just the "Task": Identify the overarching strategic, creative, and interpersonal aspects of your role that AI cannot easily replicate. These are the areas where human value will persist and grow. (Long-Term Investment)
• Master Distribution and Integration: If you are building products or services, prioritize how you will reach and serve customers. For individuals, understand how to integrate AI into workflows to enhance your unique contributions and make yourself indispensable. (Immediate Action & Ongoing Investment)
• Embrace the "It Depends" Mentality: Recognize that AI's impact is not uniform. Develop a nuanced understanding of how AI affects different industries, roles, and tasks, avoiding sweeping generalizations. (Immediate Action)
• Develop Adaptability Over Specialization: In a rapidly changing landscape, the ability to learn new skills and adapt to new tools will be more valuable than deep specialization in a single, potentially automatable, area. (Long-Term Investment)
• Understand Historical Parallels: Study how previous technological shifts (internet, mobile) unfolded. This provides a framework for understanding AI's adoption curve, the role of distribution, and the eventual commoditization of foundational technologies. (Ongoing Learning)
• Seek "Discomfort Now, Advantage Later" Opportunities: Invest time in understanding AI's complexities and potential applications, even if it feels challenging or uncertain. This upfront effort will create significant advantage as AI becomes more integrated into professional life. (Immediate Action, pays off in 6-18 months)