Every major tech shift follows a pattern: the initial excitement centers on the foundational infrastructure, but the enduring giants are those who apply that technology to solve real problems.
Infrastructure vs. Applications: The Internet Example
When the internet emerged, early players like AOL dominated by providing the basics—dial-up access and portals. But that gateway quickly became commoditized. The real winners (Amazon, Google, Facebook) built on top of the internet, solving specific high-value problems like online shopping, organizing information, and social connectivity.
AI: The Internet of Our Time
Today’s AI landscape is analogous. Foundational model providers like OpenAI or Google DeepMind are grabbing headlines, but infrastructure alone rarely sustains a lead. The true economic power will likely belong to companies that harness AI to solve focused issues—just as Amazon leveraged the internet for e-commerce.
Where AI Can Drive Real Innovation
Innovation depends on two data streams:
- Quantitative: Hard metrics (sales figures, usage data) revealing what is happening.
- Qualitative: The human “why” behind those numbers (opinions, motivations, feedback).
Why Legacy Tools Hit a Wall
- Quantitative tools show what is happening but not why.
- Qualitative methods (focus groups, surveys) capture “why” but don’t scale well and often sit in silos away from the numbers.
AI breaks down these silos by continuously learning from both data types, capturing complexity no rule-based approach or separate analytics stack ever could.
AI as the Bridge Between Data and Action
Instead of stitching together siloed reports, AI-driven platforms can:
-
Model Human Behavior at Scale
They integrate real-world purchasing patterns, feedback, and social signals to predict how different segments respond. -
Enable Rapid Iteration
By combining simulations with real-world feedback loops, organizations can refine ideas or products in days rather than months. -
Continuously Evolve
Feedback doesn’t stop at launch—AI platforms keep learning from each new data point, turning product development into a living, breathing process.
A Continuous Innovation Loop
- Simulate ideas with AI-based “People Models.”
- Validate with real-world feedback.
- Iterate quickly and repeat.
- Launch with a high level of confidence.
- Keep Going: Real usage data flows back into the AI for ongoing improvements.
This mirrors how software development moved from rigid release cycles to continuous integration and deployment—but now applied to everything, including physical products and services.
Platforms, Not Infrastructure
Winners in the AI era won’t just train big models; they’ll offer platforms that unify data, insights, and iteration into a seamless workflow. AI becomes a means to an end—enabling faster, smarter innovation rather than an end in itself.
TL;DR
We’ve seen this movie before. AOL built the on-ramp to the internet and lost out to Amazon, Google, and Facebook, who turned that connectivity into consumer-centric platforms. With AI, the pattern is the same. The real opportunity lies not in being the biggest model creator, but in delivering products and platforms that solve actual problems—on top of the AI layer.