Several Exponential Curves Are Crossing at Once

In the spring of 2025, a baby boy named KJ Muldoon was given a medicine that had never existed before and will never be needed again. He was born with a defect in a single gene, a metabolic disorder so rare that the standard treatment was simply to keep him alive and hope. Instead, a team of physicians and scientists read his particular mutation, designed a CRISPR base editor to correct that one misspelled letter of DNA, manufactured it, cleared the regulators, and infused it into him—all in roughly six months. A bespoke cure, for a population of one. Think about what had to be true for that to happen. The gene had to be sequenced cheaply. The editing tool had to be precise enough to change a single base without shredding the rest. And the whole apparatus of design and approval had to move at a speed that would have been science fiction a decade ago.

KJ’s cure was not a triumph of one technology. It was a triumph of several arriving at once—cheap sequencing, precise editing, fast computation, a regulatory system willing to bend—each climbing its own steep curve, quietly, for years, until they happened to intersect over one infant in Philadelphia. We tend to narrate progress as a relay race, one breakthrough handing off to the next. What is actually happening now is stranger and harder to feel. Multiple exponential curves are crossing the same patch of sky at the same time, and the interesting events are happening where they overlap. I want to walk you through the brightest of those curves as they stand in 2026, and where each is honestly headed in the five years to 2031—trying to do two things that do not come naturally together: keep the wonder, because it is earned, and keep the skepticism, because the hype around all of this is a pollutant. Where I am guessing about the future, I will say so.

The Machines That Learned to Reason

Start with the curve everyone is watching. In late 2022 a chatbot could write you a passable limerick. By 2026 the frontier systems from OpenAI, Anthropic, and Google DeepMind are agentic—they do not just answer, they act, taking a goal and breaking it into steps, calling tools, writing and running code, browsing, and stitching the results back together over long horizons. The shift from oracle to agent is the whole story of the last two years. A model that answers a question is a reference book. A model that can be handed a task and left to work is something closer to a colleague, and a far more consequential, and dangerous, kind of thing.

The economic and labour question follows immediately, and it is genuinely unresolved. The optimistic case is that agents become a universal productivity layer, the way spreadsheets did—amplifying skilled workers rather than replacing them. The grimmer case is that for a wide band of cognitive work—routine coding, drafting, analysis, customer support—the agent is not an amplifier but a substitute, and the displacement lands fast and unevenly. Both can be true at once, in different sectors, on different timelines. What I will not do is pretend anyone knows the net number. Anyone who tells you confidently how many jobs this creates or destroys by 2031 is selling something. The honest forecast is wide uncertainty with a heavy tail of disruption, and a policy vacuum we are nowhere near filling.

A model that can be handed a task and left to work is not a reference book. It is closer to a colleague—and a far more dangerous kind of thing.

There is a deeper caution worth naming. These systems are now good enough to be wrong persuasively. They produce fluent, confident output that pattern-matches to expertise without always possessing it, and the failure mode of the next five years may be less spectacular than rogue superintelligence and more corrosive: a slow flooding of the information commons with plausible, unaccountable, machine-made text. The capability is real. So is the rot it can spread. Both deserve our attention, and the second gets far too little of it.

Science at Machine Speed

If you want the clearest proof that this is not hype, look at where AI has already stopped being a demo and started being an instrument. In 2024 the Nobel Prize in Chemistry went, in part, to Demis Hassabis and John Jumper of Google DeepMind for AlphaFold, the system that solved a fifty-year-old problem—predicting how a protein folds from its sequence alone. AlphaFold has now released predicted structures for over 200 million proteins, very nearly every one science has sequenced, into a free public database. A problem that used to cost a doctoral student years of crystallography now resolves in seconds. That is not a chatbot writing poems. That is a machine doing real science, and a Nobel committee agreeing it counts.

The same pattern is spreading into materials. DeepMind’s GNoME system used graph neural networks to sift roughly 2.2 million candidate crystals, flagging on the order of 380,000 as stable enough to be worth making—after which collaborators in the lab managed to synthesize several hundred of them. A predicted structure is only a hypothesis, mind you; the hard, slow work of synthesizing and verifying these compounds in a lab remains, and critics have fairly argued the practical yield is thinner than the headline number. But the shape of the change is undeniable: AI is becoming a telescope for the space of possible molecules, letting us see candidates worth chasing. Over the next five years, expect the biggest gains here—in drug discovery, battery chemistry, catalysts—to be quiet, cumulative, and lab-bound rather than viral. That is what real scientific progress usually looks like.

The Quiet Collapse of the Cost of Power

Here is the curve that gets the least attention and may matter the most, because everything else runs on it. The cost of solar electricity and, crucially, of storing it has fallen off a cliff. According to BloombergNEF, the benchmark cost of a four-hour battery storage project dropped about 27 percent in a single year to roughly 78 dollars per megawatt-hour in 2025—a record low. Solar panels were already the cheapest source of new electricity in much of the world; the missing piece was always what to do when the sun went down. Cheap batteries are the answer arriving in real time. Pair plummeting panels with collapsing storage and you get something close to dispatchable solar—power available after dark—at prices that undercut new fossil plants in a growing number of markets.

This is the genuinely good news in the whole picture, and it is largely a story of unglamorous manufacturing scale rather than a single eureka. Lithium-iron-phosphate chemistry, factory overcapacity, relentless competition—boring forces, world-changing result. The sober five-year view is that the bottleneck shifts from generation to the grid itself: transmission lines that take four to eight years to permit and build, transformers on multi-year backorder, the messy politics of where to put all of it. We are about to have cheap clean electrons and nowhere near enough wire to move them. That, not the panels, is the fight of the late 2020s—made fiercer by a new and voracious customer. The International Energy Agency projects that electricity demand from data centres will roughly double by 2030, driven hard by AI, with data centres absorbing something like half of all U.S. demand growth. So the curves are not merely crossing; they are feeding on each other. AI needs power; power is getting cheaper and cleaner; but AI’s appetite is growing faster than the clean supply can be wired up, which means, in the near term, more gas, more strain, more delay. The synthesis we want—abundant intelligence running on abundant clean energy—is real but not automatic. It has to be built, against a clock.

Bodies, Brains, and the Edge of the Possible

Three more curves are worth watching, each thrilling and each over-promised. The first is embodied AI—robots with the new models for brains. In 2025, Figure’s humanoid robot wrapped a roughly ten-month pilot on BMW’s Spartanburg line, and Tesla put Optimus units to work inside its own factories—mostly, by the company’s own account, to gather data and learn rather than to carry real production. The leap is that the same large models powering chatbots can now, increasingly, give a robot a body’s worth of common sense. The caution is that the physical world is merciless to demos; reliability, safety, and cost still stand between a pilot and a workforce. Expect real but narrow industrial deployment by 2031—and a great deal of staged video in the meantime that you should treat with suspicion. The second is the brain-computer interface. Neuralink’s first human patient, Noland Arbaugh, paralyzed below the shoulders, has used an implant to move a cursor, play chess, and game with his mind; the company has since implanted a growing cohort, and Synchron, taking a less invasive route through the blood vessels, has run its own human study. This is medicine first—restoring agency to people who have lost it—and on that ground it is already a quiet miracle. The science-fiction dream of healthy people uploading thoughts remains exactly that: decades off if ever, and one we should approach with more unease than excitement. The near-term reality, restoring function to the paralyzed, is wonder enough.

The third pair are the ones that have burned the public before: fusion and quantum computing. On fusion, the milestones are real—the National Ignition Facility has achieved ignition repeatedly since its 2022 breakthrough, with one 2025 shot reaching a target gain above four, meaning the fuel released several times the laser energy delivered to it, and private firms like Commonwealth Fusion Systems are building demonstration machines, their SPARC tokamak well advanced and targeting first plasma around 2027. But a physics gain at the fuel pellet is not a power plant; grid electricity from fusion is realistically a 2030s-and-beyond proposition, and prudence says treat any nearer promise as a forecast. Quantum earns the same measured hope. In December 2024, Google’s Willow chip showed, in a paper published in Nature, that adding more qubits could actually reduce the error rate—crossing the long-sought ‘below threshold’ line that error correction had chased since the 1990s. That is a foundational result, the difference between a noisy curiosity and a path to a real machine. It is also the beginning of a long road. A fault-tolerant quantum computer breaking useful problems is still years away. The milestone is genuine; the timeline is not short.

Standing Inside the Wave

There is one more curve I have only touched: medicine, where the GLP-1 drugs—semaglutide, tirzepatide, the ones sold as Ozempic, Wegovy, Mounjaro—have quietly become one of the most consequential interventions of the decade. They began as diabetes and weight drugs and turned out to guard the heart and kidneys besides, with the liver looking likely to be next; regulators have already approved them to cut the risk of heart attack and stroke, and to slow kidney disease. A single class of molecule is reshaping the treatment of the chronic diseases that kill most of us—a reminder that not all of the future arrives as code. Some of it arrives as a weekly injection that changes how a body ages. Pull back and look at all of it together and the honest feeling is vertigo. Any one of these—machines that reason, AI that does science, dirt-cheap clean power, rewritable genes, robots with sense, interfaces to the brain, the first real footing under fusion and quantum—would define a decade on its own. We are getting them braided together, and the braiding is the point. Cheap compute accelerates the science that designs the drugs and the materials that store the energy that powers the compute. The loops are closing. That is what an exponential age actually feels like from the inside: not one rocket, but a sky full of them lighting at once, and no clear sense of which will reach orbit and which will fall back on the launchpad.

This is what an exponential age feels like from inside it—not one rocket, but a sky full of them lighting at once.

I will end where I am most uncertain, which is the human part. None of these curves bends toward justice on its own. Cheap solar can power a village or a data centre that displaces the village’s workers; a cure costing millions to design for one child does nothing for the millions who cannot afford the standard pill. The same edit that fixes a fatal mutation can, in other hands, become something we should fear. The technologies are arriving faster than the institutions meant to govern them, faster than our laws, our ethics, our capacity to even agree on what we want. That gap—between what we can do and what we have decided we should—is the real frontier of the next five years. The machines are not the hard part anymore. We are. And the most important technology of the coming decade may turn out to be the oldest one we have: the slow, contested, deeply human work of deciding, together, what all this power is for.