What if AI’s energy crisis could be solved not by building more power plants,
but by making computation thermodynamically reversible?
By Futurist Thomas Frey
We stand at a fascinating crossroads in human history. On one side, artificial intelligence promises to revolutionize everything from medicine to materials science. On the other, the energy demands of our AI ambitions threaten to overwhelm our power grids. Data centers already consume roughly 2% of global electricity, and that figure is projected to triple by 2030 as AI systems scale exponentially.
But what if I told you there’s a solution hiding in plain sight—one that could theoretically reduce computational energy consumption to nearly zero?
Enter reversible energy, a paradigm shift in computing that Ray Kurzweil recently highlighted in his conversation with Peter Diamandis on the Moonshots podcast. While most discussions about AI’s energy crisis focus on building more solar farms or resurrecting nuclear power plants, Kurzweil points us toward something far more elegant: making computation itself thermodynamically reversible.
The Energy Wall We’re About to Hit
To understand why this matters, consider where we’re headed. Kurzweil predicts we’ll achieve artificial general intelligence by 2029, with the full technological singularity arriving around 2045—a point where human intelligence effectively multiplies a thousandfold through our merger with AI systems. These aren’t idle predictions from a dreamer; Kurzweil has an 86% accuracy rate on his long-term forecasts.
The problem? Current AI training runs can consume as much energy as a small city. A single large language model might require megawatts during development. As we scale toward human-level and eventually superhuman AI, our conventional computing approaches will hit a hard wall—not because we lack the algorithms or the data, but because we simply cannot generate enough power or dissipate enough heat.
Traditional computers are thermodynamically wasteful. Every time they erase a bit of information or perform an irreversible logic operation, they must dissipate energy as heat. This is governed by the Landauer limit, which establishes a minimum energy cost for erasing information—approximately kT ln(2) at room temperature. Multiply this tiny amount by the trillions of operations happening every second in modern processors, and you get the massive power draws we see in today’s data centers.
Nature’s Efficiency Blueprint
Here’s where things get interesting. The human brain, despite its remarkable computational capabilities, runs on just 20 watts—about the same as a dim light bulb. How? Our neurons fire slowly, perhaps 1 to 200 times per second, compared to modern chips executing trillions of operations. But our brains compensate through massive parallelism, with billions of neurons working simultaneously.
Silicon chips have adopted the parallelism part—modern GPUs perform billions of operations concurrently—but they haven’t addressed the speed-energy relationship. They run at maximum velocity, burning energy at every step. As Kurzweil notes in the podcast, we’ve solved half the equation but ignored the other half.
The brain’s efficiency offers a crucial insight: you can achieve remarkable computational throughput without astronomical energy consumption if you’re willing to slow down individual operations while expanding parallelism. But even this biological efficiency pales compared to what reversible computing promises.
How Reversible Energy Actually Works
Reversible energy isn’t about generating power differently—it’s about fundamentally rethinking how we perform computation. In Kurzweil’s words from the podcast: “We can use reversible energy which most of the computation would be using reversible energy which in theory uses no energy at all because it reverses itself and gives back the energy that it’s taken.”
Imagine a pendulum swinging back and forth. In an ideal system with no friction, it could swing forever without additional energy input because the potential energy at the top of each swing converts to kinetic energy at the bottom, then back to potential energy, in an endless cycle. Reversible computing applies this same principle to information processing.
Traditional logic gates destroy information. An AND gate with two inputs produces one output—you can’t work backward from the output to determine what the inputs were. This information destruction requires energy dissipation. Reversible logic gates, by contrast, preserve all information. Gates like the Fredkin gate or Toffoli gate maintain every input in their outputs, allowing the computation to run backward and recover the invested energy.
In practical terms, this might involve adiabatic circuits that gradually transfer energy to minimize losses, or resonant circuits that oscillate energy back and forth like an electrical pendulum. The key insight is that if you preserve information throughout your computation, you can theoretically “uncompute” and reclaim your energy investment.
Kurzweil extends this vision further, suggesting we’ll ultimately “go to reversible energy using atomic levels of computation which don’t require any energy at least in theory.” This points toward nanotechnology-enabled systems where individual atoms serve as computational elements in reversible operations—approaching the theoretical limit of zero net energy consumption.
Reversible computing could let AI systems reclaim their energy by preserving information
through each calculation—like a frictionless pendulum that swings forever.
From Theory to Reality
The exciting news is that reversible computing is moving from theoretical physics to practical engineering. While Kurzweil acknowledges “we haven’t actually experimented with that” on a large scale, several organizations are making significant progress.
Vaire Computing in the UK is developing the first commercial reversible chips. Their “Ice River” prototype, demonstrated in 2025, recovers 40-70% of computational energy using adiabatic resonators. The company targets AI data centers and projects efficiency gains of 4,000 times by the late 2020s—a staggering improvement that could single-handedly solve the AI energy crisis.
Sandia National Laboratories, led by Michael Frank, is working to bypass Landauer’s limit entirely through reversible hardware designs. Their research suggests we could achieve unlimited efficiency scaling—not just incremental improvements but a fundamental escape from thermodynamic constraints that have governed computing since its inception.
At the University of Texas at Dallas, Joseph Friedman’s team explores skyrmion-based nanoscale reversible logic for heat-free operations. European Union Horizon projects like E-CoRe are building reversible architectures specifically for machine learning and blockchain applications.
Why This Changes Everything
The implications extend far beyond just saving electricity, though that alone would be transformative. Reversible energy enables the entire suite of technologies Kurzweil envisions for reaching the singularity.
Consider medical AI. Kurzweil describes testing millions of drug possibilities in a single weekend using advanced simulations. This requires enormous computational resources—but becomes feasible with near-zero energy costs. Nanobots swimming through our bloodstreams, monitoring and repairing cellular damage, need onboard computation that can’t rely on plugging into a wall socket. Brain-cloud interfaces connecting our neurons to vast AI systems demand energy efficiency that conventional computing cannot provide.
Without reversible energy or something equivalent, we face a stark choice: abandon our AI ambitions or accept massive environmental consequences. With it, we can pursue exponential intelligence growth sustainably.
Final Thoughts
The transition to reversible computing won’t happen overnight. We need to redesign processor architectures from the ground up, develop new programming paradigms that take advantage of reversibility, and solve practical engineering challenges around heat dissipation and error correction in these novel systems.
But the trajectory is clear. Just as we’ve seen exponential improvements in processing power, memory density, and network bandwidth, we’re now poised for exponential improvements in energy efficiency—not through better batteries or cleaner power generation, but through computation that barely consumes energy at all.
Kurzweil’s 2029 timeline for AGI suddenly seems less fantastical when we consider that energy constraints—one of the biggest potential obstacles—may soon dissolve. His vision of human-AI merger by 2045, with intelligence multiplying a thousandfold, becomes not just possible but perhaps inevitable if reversible computing delivers on its theoretical promise.
We’re witnessing the early stages of a transformation as profound as the shift from vacuum tubes to transistors. Reversible energy represents more than an engineering improvement—it’s a fundamental reimagining of what computation means and what becomes possible when we align our technology with the deep principles of physics rather than fighting against them.
The singularity may indeed be near. And reversible energy might just be the key that unlocks it.