Why AI Should Keep Getting Better

Predictions about artificial intelligence (AI) are everywhere—what it'll do, how big the market could get, how much is being spent to build it. Those are outcome questions.

Ours is more fundamental: Why will AI keep getting better?

The answer starts with compute. For 60+ years, computing power improved at a pace with almost no precedent. The engine behind that progress was Moore's Law, the decades-old pattern of computing power roughly doubling every two years.¹ As that engine slows, a new one is taking its place.

The five questions that follow trace how compute got us here, why it may keep improving and what that could mean for investors.

1 | Why is AI taking off now?

After nearly 39 doublings under Moore’s Law, leading-edge chips now contain more than 200 billion transistors.²

Exponents in their early stages feel and are insignificant, but as the exponents continue to compound, they start to become massive and change our lives and the economy around us. We cannot think of another innovation that has continued to improve so consistently. Even though Moore’s law is slowing, it is still creating massive improvements at huge scale.  

A widely used Silicon Valley parable compares exponential growth to doubling grains of rice across a chessboard: the gains compound through the first half, but their scale becomes dramatically larger in the second. Semiconductors have followed a similar path, helping to create the compute power that made today’s AI possible.

2 | If Moore’s Law is slowing, how can compute keep improving?

We are moving from improving the chip to improving the machine around it. Compute is not slowing along the path of Moore’s law, but accelerating.

For most of the semiconductor era, the formula was straightforward: Make transistors smaller, fit more transistors onto a chip and achieve more computing power. Physics and economics are making that approach increasingly difficult.

Transistors within a semiconductor chip are now just a few atoms wide, and shrinking them further has become dramatically more expensive, effectively slowing Moore’s Law.³ But instead of relying on a single processor, compute can increasingly scale across systems that combine processors, memory, packaging, networking, power, cooling and software. 

At the largest scale, these systems can coordinate thousands of processors inside what are effectively AI factories.

3 | Why does compute matter so much?

The answer is captured in what AI researcher Rich Sutton called the “Bitter Lesson”—over long periods, the most successful approaches have tended to be the ones that can absorb more compute and more data.⁴

Chess offers an early example. Computers surpassed human experts not by being taught the special structure of the game, but by using processing power to evaluate millions of positions per second.

Modern AI reflects the same principle. The core mathematics has existed for decades. What changed was the scale at which those ideas could be applied. As compute increased, researchers could train larger models on larger datasets and tackle relationships that previously exceeded the practical limits of computing. More compute has unlocked new capabilities.

4 | Why do we think compute can keep accelerating?

AI compute no longer depends on a single source of improvement.

Most technologies follow an S-curve: a period of rapid improvement that eventually gives way to slower, incremental gains. Semiconductors have been a remarkable exception, and the shift to system-level scaling may mark another inflection point. Instead of relying on one S-curve, the AI Scaling Era may be built on several S-curves advancing at the same time across memory, packaging, networking, power and tensor compute.

We see these five frontiers as potential drivers of the next wave of compute improvement, with each supported by a visible engineering roadmap, as the table below shows.

5 | What could it mean for investors?

Sustained improvement at this scale could mean more disruption.

Industrial revolutions are built on general-purpose technologies—foundational technologies that improve over time, spread across industries and become platforms for further innovation. The semiconductor helped take computing from mainframes to personal computers to smartphones and became one of the major technological foundations of the Third Industrial Revolution.

We believe AI is emerging as a general-purpose technology that could help drive the Fourth Industrial Revolution.

For investors, that could mean more change across the global economy and in market leadership, with innovation extending into robotics, genomics, clean energy and blockchain alongside continued advances in AI.