AI is moving so fast, which is why your strategy should be long-term.

9 February 2025

AI is moving so fast, which is why your strategy should be long-term.

Danny Hillis, a pioneer in AI He understood something decades ago that many executives are only now coming to terms with. Technology does not evolve in stable, predictable increments.

Danny Hillis Legacy: AI is moving so fast, which is why your strategy should be long-term.

Danny Hillis, a pioneer in AI, parallel computing and creator of The Connection Machine, understood something decades ago that many executives are only now coming to terms with. Technology does not evolve in stable, predictable increments—it moves in waves of rapid obsolescence and reinvention.

The past teaches us that every major technological revolution—the printing press, the steam engine, the internet—was first underestimated and then misunderstood by those who clung to outdated ways of thinking. AI is no different. AI is evolving faster than most companies can keep up with. Any AI investment made today will be outdated in a year. So what should executives do? My take on this is that treating it as a one-time transformation is not the way to go.

Brace yourself for constant change. That is at least how i’m preparing myself. Adopt an iterative, experimental mindset, embracing “strategic decay”—the idea that any AI capability has a limited shelf life, and businesses must plan for continuous renewal. I think that the worst decision is to wait for stability in a field that will never be stable.

The companies that thrive in the AI era will be those that experiment relentlessly, test rapidly, and build learning systems that adapt in real time. You don’t need a perfect AI strategy today—you need a framework that allows you to stay ahead despite inevitable obsolescence. You don’t need to have a perfect AI strategy today. You need a learning system that keeps you ahead despite inevitable obsolescence and a framework for testing output. If it fails, fail quickly.

If we look at what a few of the pioneers in the field of AI and parallel computing gave us. I came across the work of Danny Hillis, an pioneer in AI and parallel computing, but what makes him particularly interesting ni this context is his work on long-term thinking. He helped build the 10,000-Year Clock, designed to keep time for ten millennia, forcing people to think beyond short-term cycles.[3] His ideas are the perfect counterbalance to today’s AI hype cycle, where businesses assume that whatever is state-of-the-art now will be relevant in five years. Hillis would tell us: Plan for a future where everything changes—because it will.

Most AI strategies are fragile because they assume today’s AI is the final form. But just as no one runs a business on 1990s software, no one will rely on today’s AI in five years. Instead of chasing trends, leaders must design for continuous renewal:

No permanent AI solutions—foster a culture of experimentation.
AI isn’t the end goal—treat it as an ongoing adaptation.
Beyond tools, invest in AI integration for long-term agility.

A key reminder: AI is an evolving tool, not a fixed investment. Your strategy must account for continuous obsolescence—standing still isn’t an option.

The Connection Machine [1] developed by Danny Hillis in the 1980s, was one of the first attempts at massively parallel computing, mimicking the brain’s structure. Instead of a single processor, it used millions of interconnected units in a 20-dimensional hypercube.[2]

Parallel with Today’s AI

One of the most striking parallels between the Connection Machine and today’s AI revolution is the shift from linear, rule-based computing to massively parallel processing. Modern deep learning models, like transformer architectures (which power GPT models and other LLMs), rely on parallelism—processing massive datasets across thousands of GPUs to identify patterns and relationships.

Hillis believed that the future of AI wouldn’t be about one central brain, but about interconnected intelligence—many small systems working together. This idea is more relevant now than ever. LLMs like GPT-4 and Gemini operate by integrating vast networks of data and learning in parallel. The kind of intelligence Hillis foresaw.

The Connection Machine was revolutionary, but it was quickly overtaken by newer technologies. This is a powerful lesson for businesses today. AI is not a static investment—it is constantly evolving. The AI models we use today will be outdated in 12–24 months, just as the Connection Machine was quickly surpassed by more efficient computing paradigms. Hillis understood that technology has a built-in expiration date, which is why he later focused on long-term thinking. His work with the Long Now Foundation emphasizes that businesses shouldn’t think in 1–2 year cycles, but in decades.

Lessons

What I learned from the work of Hillis and his colleagues is that you should not treat AI as a fixed investment. Instead, build a culture of ongoing AI adoption. Don’t assume today’s models will last. The companies that thrive will be those that can adapt to new AI generations quickly. What you are doing now with AI will be silly in a years time, but the question is where do you stand in a year? Ever tried, ever failed, no matter, try again, fail again, fail better[4]

[1] Hillis, W. Daniel (1989a). “Richard Feynman and the Connection Machine”. Physics Today. 42 (2): 78. Bibcode:1989PhT….42b..78H. doi:10.1063/1.881196. Retrieved 30 June 2021. Danny Hillis and Sheryl Handler founded Thinking Machines Corporation (TMC) in Waltham, Massachusetts, in 1983, moving in 1984 to Cambridge, MA. At TMC, Hillis assembled a team to develop what would become the CM-1 Connection Machine, a design for a massively parallel hypercube-based arrangement of thousands of microprocessors, springing from his PhD thesis work at MIT in Electrical Engineering and Computer Science (1985).