Casey Handmer May 2026

What?

Elon has recently (late 2025, early 2026) been talking about building many terawatts of orbital AI compute and launching some components from Moon factories with a mass driver. This is an old idea, enabled by the Moon’s relatively low gravity and lack of an atmosphere. See, for example, The Moon is a Harsh Mistress and The High Frontier.

The fundamental problem with The High Frontier is that the set of products that can be made in space and sold on Earth while making money is very limited, due to the sheer cost and difficulty of accessing space. In general, they are observations and communication, which in both cases distributes the product using radio waves, which are much cheaper than physical return of artifacts from space.

In 2019, I wrote that Starlink was likely to be incredibly lucrative and I’m happy to see this is the case, with over $10b in revenue last year. Space AI takes this business model and ramps it up to 11. Why? Starlink has already established that converting a space solar photon into an electron and using it to relay bits of information around the world is extremely profitable. Space AI is a great way to vastly increase both the total demand for space data bits as well as the value per bit, as the tokens encoded by these bits have already proven to have stupendous economic value and apparently unlimited demand.

Why?

Starship promises a near future with launch costs to LEO of perhaps $100/kg. With electric propulsion, large quantities of cargo, including solar powered AI can be positioned anywhere in cis-Lunar space for an incremental cost beyond that. For AI hardware, most of the cost is in the GPU/TPU die, which contributes almost no mass, while most of the mass is in the solar panel and radiator, which cost (relatively speaking) almost nothing.

Here’s a spreadsheet I put together last year with some basic first-principles analysis. At even $500/kg, launch cost is only 5% of the total satellite deployment cost, so a lunar mass driver is unlikely to drastically improve the economics of space-based AI, by reducing launch costs.

It’s also unlikely to have low start up costs!

Instead, we must look to a future where Starship costs stop falling from experience and economies of scale and rise to unaffordable levels, perhaps comparable to the Shuttle’s $50,000/kg, because of a constraint on launch capacity.

In my spreadsheet, I estimate that one Starship can deliver about 15 MW of solar power to orbit. Last year, China produced over 1 TW of solar photovoltaic panels. Supposing we weren’t constrained by chip fabrication and Starship was fully operational, it could launch 1 TW to orbit per year with just 67,000 launches, or one every 8 minutes.

This might seem like a lot, but the world currently sustains about 100,000 commercial flights per day! In a world where SpaceX can turn around a launch site in an hour, only seven or eight pads would be necessary to keep up with this rate of launch, requiring a fleet of perhaps 10 boosters and a few hundred Starships.

Nor would this launch rate defeat our global oil production. One Starship launch consumes roughly 10,000 barrels of oil (equivalent), and the world currently consumes 100 million per day. So 67,000 launches per year is less than a week of the world’s current supply of oil. It may require a few gas pipelines in Texas to be upgraded, and of course by the time this happens solar synthetic fuel will be a recognized and mature technology.

There has been some speculation about damage to the upper atmosphere of the Earth caused by huge launch volumes.

In any case, we’re talking about a launch volume of hundreds of thousands of Starships per year, or more than 10 million tonnes of cargo per year, with a total launch revenue of about a trillion dollars – equivalent to about six weeks of the global oil and gas industry!

The lunar mass driver must transcend this scale.

What does the lunar mass driver drive?

The Moon is made of rocks. Primarily volcanic rock, similar to Earth basalts. As ores go, they are not preferred sources of metals on Earth. Though they contain nearly every metal – the net present value of the metal in 1 tonne of basalt is about $1300 vs the $20 price as crushed gravel – they’re mixed together and generally considered energetically infeasible to extract. We’re working on this at Terraform but the energy demand is a fact of life.

In one model, the lunar mass driver fires raw rocks into Lunar orbit, to be processed in space using copiously available space solar power. In another model, moon rocks are pre-processed to increase their metal content, or even converted into finished products, before launch. Blue Origin has demonstrated a process to convert Lunar regolith (dirt) into a functioning solar panel, but it’s not clear what the energy return on energy invested for this process would be.

The Moon’s surface itself can be a tough place to do anything energetic, because it is subjected to 14 Earth days of shade during the long lunar night, followed by 14 days of unrelenting sun during the day. Any serious infrastructure will require serious power, either from extremely large nuclear reactors operated near the poles to create functional shaded radiators, or from energy beamed up from the Earth or from Lunar orbit, or both.

For the following I’ll assume the driver is shifting dumb rock. It doesn’t change much but the g-tolerance of raw materials is a big plus!

How big is my mass driver?

Let’s run some numbers around mass flow rate. We’re not going to the trouble of building a mass driver for no reason, we’re doing it to alleviate the burden on Earth of launching 1000 Starships every day, to push total human compute into the 10s of TWs incremental increase per year.

So let’s assume one mass driver launches 10 million tonnes of rock per year. Taking into account rock refining losses and expansion this implies a Lunar fleet of a few dozen mass drivers, but you have to start somewhere. 10 million tonnes per year is 1 tonne every 3 seconds.

We can calculate the total energy expenditure too. Delta-V to LLO is 1.6 km/s, and we assume that Delta-V is cheaper to come by in orbit, thanks to solar powered tugs. We only need the lunar mass driver to get rocks into orbit, where they are collected and moved to wherever they need to go.

So total kinetic power is 0.5*mdot*v^2 = 450 MW, assuming 90% driver efficiency. The 10% waste covers ohmic losses, cargo-sled recycle weight, and active cooling.

Why not stack some additional assumptions on top of this result?

Let’s assume an equivalent price of $10/kg, reflecting that random rocks orbiting the moon are not quite as valuable to the customer as a finished satellite orbiting the Earth – where all the customers are. This implies that each lunar mass driver makes $100b/year. Assuming that 10% of this revenue pays for the power plant, this works out to $2.50/kWh, which is about 10x higher than a typical US rate payer in 2026.

Can a 450+ MW nuclear power plant be built and operated on the Moon for a 10x cost increment relative to Earth? I’m not sure but it’s not forbidden by the laws of physics.

For reference, a reactor of this scale would typically cost $2-4b on Earth and weighs perhaps 1000 T.

I previously estimated that a radiator-constrained space reactor mounted into a Starship could generate perhaps 3 MWe, implying several hundred launches for the power of one mass driver. A much larger, monolithic space reactor of 500 MW scale would need to be completely re-engineered, requiring delivery, an enormous radiator (many hundreds of acres, assuming permanent shadow), and other hassles. But it could be done.

Orbit, what orbit?

Low lunar orbits are, in general, unstable, due to the presence of gravitational anomalies called mascons (or mass concentrations) in various places corresponding to ancient impacts. There are, however, four classes of frozen orbits (~27°, 50°, 76°, 86°) on the Moon that are relatively stable, so provided the mass driver has a latitude and launch azimuth high enough to access these, the launched rocks won’t necessarily immediately return to obliterate the launch site two hours after launching. On a long enough timescale any passive payload launched from the surface of the Moon into Lunar orbit will run into the surface, so the trick is to launch payloads into converging bunches and scoop them up in orbit, performing circularization and/or relay transportation using some kind of orbital tug. It’s also possible to launch rocks from the Moon into more distant libration orbits, but I don’t cover that case in this post.

Technical implementation

The launcher in this image looks a bit like a rail gun. But rail guns pass current from rail to rail through the sabot (projectile enclosure) and suffer rail erosion at far higher rates than we can tolerate.

The lunar mass driver will be a maglev in disguise.

In this section I draw on my ancient experience as a levitation engineer at Hyperloop back in about 2016.

The job of the track is to accelerate a passive cargo-carrying sled to high speed over a short distance. The sled must be controlled over six degrees of freedom. Unlike a passenger maglev, where propulsion forces are a fraction of gravitational forces, a mass driver is optimized for highly efficient acceleration.

Assuming a launch speed of 1.6 km/s, v^2 = 2 a s gives track length and acceleration as inversely proportional.

If rocks can survive 1000 gs of acceleration (they can) then the launch track need only be 128 m long (or 256 m including the sled catching portion), greatly reducing its mechanical and construction complexity. Can an electromagnetic launch system deliver 1000s of gs?

A helpful intuition pump for this is to consider how much force a permanent magnet can deliver. A commercial neodymium magnet can easily support 20x its own weight, but 2000x is highly non-trivial. Bear in mind that no matter how cleverly built, the track will have to be toleranced with non-zero gap between the sled and the maglev track.

Add to this the fact that the acceleration is diluted by the mass ratio of the magnet to the rest of the system. I can imagine a launch sled which is 70% magnets, 10% structure, and 20% rocks, in which case even an optimal launch system would have to take a 30% haircut on acceleration.

There are a few ways to skin this cat, but probably the easiest is with a synchronous linear motor along a pair of parallel tracks with the sled suspended in between. At any meaningful level of acceleration, gravitational forces from the Moon round to zero. It is possible to include, for example, some null flux electrodynamical suspension system for guidance, but if you’re using the sled magnets for anything other than acceleration then you’re throwing away performance and making the track longer.

On this note, achieving anything like the necessary levels of magnetic shear force requires very very large, very flat magnets. In my model below, they are 20 cm wide, 2.8 cm thick, and 9 m long, entirely enclosed by an electromagnetic stator, and connected via high strength steel shear panels to the payload bucket.

It’s not impossible to build long tracks but, having seen this close up at Hyperloop, it becomes extremely difficult to achieve sub millimeter alignment precision over long distances. To give a flavor of this, the Shanghai Pudong transrapid Maglev operated with a ride height of about 1 mm. It is built on an elevated steel reinforced concrete trackway whose foundations were dug to a depth of 80 m into the Pudong silt. Shanghai, like many cities, is built on a river delta. Literally thousands and thousands of tonnes of concrete and steel to form the track way. You would think that it would be stable enough that the track could be calibrated once during construction and then would be fine, but in fact the track had to be re-aligned, by a custom designed track maintenance vehicle, twice per day, to account for such perturbations as tidal deformation of the crust of the Earth.

The Moon does not have saturated silty deltas or wildly varying tidal forces (being tidally locked) but it does have a very extreme temperature cycle over its 28 Earth-day day, so probably the best way to achieve dimensional stability, as well as a measure of meteorite protection, is to bury the track under a few meters of dirt.

(I asked AI to make a better version of this diagram but it wasn’t right. Motion is into the page. 200 kg of moon rocks can fit in a container 40 cm on a side.)

(Here’s an OnShape model. Below, diagrams of how the whole thing goes together.)

The cart oscillates back and forth on the track, launching rocks for ¼ of its cycle, with the rocks separating from the sled bucket at the midpoint of the track. The slow down step can recover some momentum as power, provided there’s a place to store it! At 1000gs, launch takes just 0.16s and the complete cycle (accelerate, release rocks, slow down, fly back to rock loading site) is as short as 0.64 s, not including time to reload rocks. At the 10 million tonnes per year rate, each launch would carry about 200 kg, with a total sled weight of 1000 kg loaded and 800 kg empty. This launch and recycle process would load the structure with up to 1000 T, that is 10 MN, of force, at about 1.4 Hz. That’s a pretty wild vibrational environment for the ground anchoring system to endure.

Power = force*velocity, peaking at 16 GW at the middle point of the 256 m long mass driver, a lot more than the average demand. Power consumption averages 1.75 MW per meter, but much of this is recycled. Assuming 3% loss as ohmic heating, we’re still looking at 50 kW per meter, easily enough to justify an active coolant loop. For the purposes of this post, I assume any waste heat pumped and dumped in a permanently shadowed radiator used primarily by the power reactor. Beaming power up from the Earth would require a receiver rectenna several km across.

Structurally, the entire launch rail must endure tremendous forces, variable loads (due to unbalanced cargo), impacts from micrometeorites and rock spray during rocket landings, electrostatic and thermal nonsense caused by the Lunar day night cycle, and lots of other things. Fortunately, it has no moving parts and no wear surfaces!

So while the mass driver could be brought to the Moon over several launches in pieces, it needs a sophisticated anchoring system to react out loads and cope with extreme thermal swings during the Lunar day/night cycle.

What do you think, Claude?

In which I roast myself with the latest AI so you don’t have to. Also some more technical detail.

Missing or under-treated

Sled recycle losses. You bury the second 128 m of deceleration in “recover some momentum as power.” Empty sled at 1.6 km/s = 1.02 GJ. At 1.56 Hz and 90% recovery, that’s 160 MW of pure dissipation, not in your 450 MW budget. At 80% it’s 320 MW. You either need to argue >95% round-trip electrical recovery (hard given switching losses on multi-GW pulses), or your reactor is closer to 700–800 MW. [CH: Correct, there’s a very steep penalty for higher parasitic power losses.]
Pulsed-power infrastructure. 16 GW peaks at 1.5 Hz with bidirectional flow. The capacitor banks / flywheels for this dominate the system mass and probably the cost. ITER’s pulsed power is tens of GW for comparable durations and it’s a building. On the Moon, this is the actual hard engineering problem, more so than the rail itself. Worth a paragraph. [CH: There are several ways to skin this cat.]
Catcher architecture. 56,000 launches/day into LLO requires tug coordination at obscene tempo. Velocity dispersion at exit sets apogee error: 0.1% velocity error → ±5 km position error after a half-orbit. Either you hit launch precision <10⁻⁴ (very hard with thermal drift on a 256 m structure) or your catcher needs km-scale capture and active pursuit. The catcher fleet plausibly outweighs and outcosts the driver. [CH: I have also waved my hands about what to do with rocks in lunar orbit – they still need to be converted into solar computronium somehow!]
Δv beyond LLO. Customer is in Earth orbit, not lunar orbit. LLO→GEO ≈ 3.9 km/s, LLO→LEO higher with aerocapture. You save ~9 km/s vs Earth launch but you’re not at the customer. The relevant comparison is (launch + tug from LLO) vs (Earth launch), not (launch) vs (Earth launch). Worth one paragraph stating the tug budget. [CH: As stated, the assumption is that Moon launch delta V is the expensive part we’re trying to overcome.]
Chemical-lander baseline. A dumb LOX/LH2 (or LOX/CH4) lander from the Moon to LLO needs ~1.8 km/s, mass ratio ~1.7. If you have any propellant production at all, chemical wins until volume is huge. Mass driver’s case rests on (a) no propellant feedstock available at scale, or (b) volume past the crossover. State this; the reader will ask. [CH: There’s no good reason to synthesize prop on the Moon and the gear ratio makes flying chemically propelled shuttles to and from LLO pretty silly.]
Reactor radiator. 450–800 MW thermal, polar shadow, ~700 K reject → tens of thousands of m². Your 2025 Mars-reactor post implies a tonne/m² class radiator. This is hundreds of tonnes of radiator alone, which dwarfs a 1000-T terrestrial reactor mass. The 10× cost premium claim wants more support. [CH: True, as a good rule of thumb, a space reactor’s radiator will weigh about as much as everything else put together.]
Magnet survivability at 1000g. NdFeB is brittle. Sintered blocks 9 m long under 1000g shear and oscillating tension fatigue is not obviously feasible. Either bonded magnets (lower B), or segmented with structural steel, which eats into your 70% magnet mass fraction. [CH: Actually building magnets and a sled structure which can make this work is serious engineering.]

Alternatives

The Moon’s lack of an atmosphere means you can also operate a lunar sling. A tower with extendable tethers whirls around to obtain high speeds without as high g loads. I see no reason why entrepreneurs couldn’t build a range of lunar launching solutions!

Conclusion

Lunar mass drivers do not violate the laws of physics. Their tech can be developed and tested thoroughly in labs on Earth at prices that are reasonable in comparison to the complexity and cost of Starship development. They are unlikely to be able to compete with Starship flying at any level of volume from Earth (where the chip fabs are) to space (where the infinite sunlight is), unless Earth launch is supply limited in some way. Order of magnitude, this is beyond 100 TW per year deployment. For reference, 10 TW of compute is roughly equal to the total economic output of the entire world’s supply of natural intelligences. We live in interesting times!

In her book on the history of the laser, historian Joan Bromberg notes that the technological and scientific predecessors of the maser (which itself preceded the laser - two critical technologies whose developmental histories I sketched in this piece two months ago) were in place for decades before physicist Charles Townes had the insight to combine them:

Stimulated emission had been known to physicists for over 30 years, and “regenerative” oscillators, that is, oscillators with feedback, were well known to engineers. Why, then, was Towne’s insight so novel? The answer appears to be that in 1951, physicists and engineers in the United States were not yet sufficiently acquainted with each other’s territory to find it natural to put the two ideas together.

This sort of decades-long wait between when a technology first becomes possible, and when it actually appears, seems common, or at least seems like it might be common. I’ve previously written about why it took so long for wind power to be widely deployed after it became technologically possible, and people often idly speculate whether inventors in the Roman Empire could have built a steam engine, or why we waited so long to put wheels on luggage.

Knowing how long this gap between when an invention becomes possible, and when it actually appears, is useful, because it tells us something about the nature of technology and technological progress. What factors govern whether some new technology appears? How much does mere technical possibility matter, and how much do things like cross-pollination of knowledge, economic feasibility, and political factors contribute? Knowing more about how long it takes for an invention to appear once it becomes technically possible can help us answer these sorts of questions.

I wanted a better sense of how long it takes for some technology to appear once its necessary predecessors are in place. So I used AI to try and find out.

Method

To do this, I used a list of 190 major inventions that I’ve used for previous analyses of technology. For each invention, I asked Claude Opus 4.7 how much earlier it could have been invented.

This required pinning down what exactly I mean by “could have been invented.” For one, it’s often possible to build a working example of some technology long before it’s capable of solving a real problem. Working incandescent light bulbs were built decades before Edison, but they weren’t useful for providing indoor illumination until Edison developed one that lasted for many hours without blackening or burning out. For another, it’s often possible to build something before the problem that it solves has been articulated. Surgical masks — a cloth covering over the face — could have been invented thousands of years ago, but inventing them only makes sense once the germ theory of disease has been articulated.

Nonetheless, I decided to use “could a working example of this technology be built” as my meaning, ignoring whether that technology would be practical or economically useful. In part I chose this criterion to follow the contours of the inventions list, which for the most part is when things were first invented, not when they first improved to the point of becoming practical for regular use. For instance, the list includes the ballpoint pen as being invented in 1888 by John Loud, even though practical ballpoint pens didn’t appear until the 1930s. I also chose this criterion because pinning down technical possibility seemed difficult enough without also having to consider questions like “would someone in this time period be willing to pay for a technology that worked roughly this well?”, which seems like a much harder question.

I specified “could a working example of this technology be built” as follows: I asked Claude to assume an inventor working in a well-equipped, era-appropriate workshop with a team of highly skilled engineers and craftsmen. Could they, using knowledge and technology available at the time, build a working example of the technology in five years?

I allowed the hypothetical team to build one required precursor technology, if that technology was simple enough that the team could plausibly build it along with the invention in question. For instance, Edison’s light bulb was predicated on the demonstration of Sprengel’s 1865 mercury vacuum pump, using those insights to achieve vacuum high enough that the filament wouldn’t burn out quickly. However, a Sprengel mercury pump is not amazingly complicated — it works by dropping mercury through a glass tube, forcing air out along with it — and it’s plausible that a team working to build a better incandescent lamp could have invented it as a collateral innovation as part of their improvement efforts. (Edison, after all, had to invent lots of other technology — generators, distribution systems, etc. — along with his bulb to make it practical.)

I also allowed the fictional team to generate new knowledge through iteration and engineering-style experimentation, but I did not allow them to discover new scientific frameworks or make novel key empirical observations guided purely by scientific curiosity. So a team trying to build an electric motor in the early 19th century in this simulation could not just summon up Oersted’s critical observation that electric current creates a magnetic field, and a team trying to invent the transistor in the early 20th century would not have access to the band theory of quantum mechanics.

You can read the entire prompt I used here.

For the output, I had Claude list a range encompassing two dates. The first is the earliest plausible date for when the inventor’s team could have succeeded with some charitable assumptions and a bit of luck. The second is the straightforward date, when multiple independent teams would be likely to converge on a working model of the invention. Along with the date range, I had Claude list the necessary prerequisite technologies and scientific knowledge, and give a short explanation of its reasoning. An example of the output from Claude for an invention is below:

Claude gave ranges for 166 of the 190 inventions. The other 24 it flagged, mostly either because it was a scientific discovery rather than an invention (like X-rays) or because its real-life invention was a serendipitous accident that couldn’t be expected to be recreated earlier (like Perkin’s invention of mauve dye). You can read the full document with all Claude’s answers here.

One issue I noticed reading through the answers is that I didn’t do quite enough to pin down cases like the surgical mask, which are gated almost entirely by conceptions of the problem. There’re a few other items like this, notably Morse Code and Braille, and while Claude did a reasonable job of handling them (it gave answers for Morse Code and the surgical mask which assumed that they needed to wait until the problem was articulated, and flagged Braille) but if I was re-running this simulation I would give more specific instructions for handling cases like these.

Are the AI’s answers any good?

Based on previous work I’ve done using AI to answer questions about the nature of technology and science, I expected the answers I got to be reasonably good, though not perfect. But while AI hallucinations have become much less of a problem, the ability of an AI model to provide quality output on tasks like this is definitely still a moving target. So I took several steps to try and verify Claude’s answers.

First, I did spot checks for various specific, verifiable claims that Claude made regarding various required predecessors. In the arc lamp example above, for instance, I checked whether Galvani published research on electric current in 1791 (he did). And for 20 of the inventions, I fed the entire output back to Claude, and asked it to pair each verifiable claim with a reliable source, and flag any cases where the claims were incorrect or a reliable source couldn’t be found. (I then checked these sources for accuracy.) This yielded an accuracy rating of around 97% — only a tiny number of verifiable claims were inaccurate, and the inaccuracies were relatively minor (such as a claim that something was common by the 1850s when it should have been the 1860s.)

Checking people and dates is one thing, but this doesn’t tell us the most important part: whether Claude correctly identifies the actual binding constraints for creating a given invention. To try and verify this, I read through Claude’s analysis of several inventions that I know well enough myself.

Here’s the first sample answer, which Claude produced for the Fleming valve:

The Fleming valve is essentially an Edison-effect lamp with the cold electrode brought out as a usable anode and the device used as a rectifier. All physical prerequisites — evacuated bulb, carbon filament, sealed lead-throughs, battery, galvanometer — were in place by 1880. The binding item is the Edison effect itself (1883). If we treat thermionic emission as off-limits before 1883 (it required Edison to notice the unilateral current in his lamps, not something a rectifier-builder would set up to look for), the earliest plausible date is essentially 1883–1890: a motivated team aware of Edison’s observation could have made the small inferential leap to “this rectifies AC” almost immediately, especially once Hertz (1887) created a clear use case for a sensitive RF detector. Earliest straightforward sits in the late 1890s, after Hertzian-wave work motivated multiple labs (Marconi, Lodge, Braun, Fleming) to seek better detectors than the coherer; Fleming himself was investigating the effect from the early 1890s and could have arrived at the diode concept several years before his 1904 patent. The historical 1904 date reflects how long it took to connect Edison’s curiosity finding to the practical problem of wireless detection.

Here’s the second, the Wright brothers’ airplane:

The Wright Flyer is a clean case where the binding constraint is the lightweight internal combustion engine. Cayley had laid out the basic principles of fixed-wing flight by 1810, gliders were practical from mid-19th century, and Wenham’s wind tunnel (1871) plus Lilienthal’s glider work (1890s) gave aerodynamic data. The three-axis control concept is team-discoverable through glider iteration — it’s an engineering insight, not a scientific discovery. The hard prerequisite is a roughly 10-hp engine weighing under ~200 lb, which only becomes feasible after the Otto cycle (1876) and Daimler/Benz refinements of the mid-1880s; steam plants of earlier eras were too heavy. With a Daimler-class engine available by ~1885 and Lilienthal-style glider knowledge accumulating, a motivated team could plausibly assemble a Flyer-equivalent in the late 1880s (earliest plausible). By the late 1890s prerequisites were widespread enough that multiple teams (Langley, Maxim, Ader, the Wrights) were actively converging — making 1895–1903 the straightforward window. The historical 1903 date reflects how genuinely difficult the integrated control problem was, not missing prerequisites.

And here’s the third, for the jet engine:

The turbojet is a system-integration invention rather than a new-phenomenon invention; nothing in it requires post-classical physics. The binding constraint is the maturity of turbomachinery — specifically a compressor and turbine pair efficient enough that the cycle yields net positive thrust, plus turbine blades that survive the combustion-gas temperatures. Parsons’s steam turbine (1884) and the first gas-turbine attempts (Stolze, Armengaud-Lemale, ca. 1900–1906) demonstrate that the basic building blocks existed by the early 20th century, though early gas turbines barely broke even because compressor efficiency was marginal. A motivated team in 1905–1915 with access to Parsons-class turbomachinery, a centrifugal compressor, and contemporary nickel-chromium steels could plausibly run a short-duration jet — Whittle’s 1937 W.U. ran on similar materials. By the mid-1920s, compressor efficiency, high-temperature alloys, and aerodynamic theory had matured enough that multiple teams could converge straightforwardly, which is essentially what happened: Whittle (UK) and von Ohain (Germany) independently produced running turbojets within a few years of each other. The historical lag from “straightforward” to flight reflects funding skepticism and the engineering grind of making it light, reliable, and aircraft-ready, not a missing fundamental.

All three of these answers are pretty close to what I would have given. The Fleming valve is basically gated by the incandescent light bulb. Once the bulb existed, the phenomenon of thermionic emission (then named the Edison Effect) was quickly observed, and could have been exploited soon after by a motivated person. The connection to radio is also correct — Fleming was in fact a consultant for the Marconi Company, and invented the Fleming valve specifically for use in early radios.

For the Wright brothers’ airplane, a lightweight engine was indeed an important gating technology, and needing to wait until Otto’s internal combustion engine got light enough in the 1880s is a reasonable judgment. Thomas Edison worked on the problem of mechanical flight in the 1880s, but judged that what was needed was an engine with a very high power-to-weight ratio, and only made some cursory attempts to build one before giving up. Samuel Langley, one of the Wright brothers’ contemporaries, invested an enormous amount of his aircraft development efforts into building an extremely efficient gasoline engine; the resulting Manly-Balzer engine held the record for power-to-weight ratio for many years. The Wrights’ efforts were notable not because the brothers thought the engine was unimportant, but because they (correctly) thought that by the early 1900s engine technology was advanced enough that obtaining a good-enough engine wouldn’t be overly difficult.

You could argue that the “earliest plausible” date for the airplane could be pushed back somewhat earlier, into the 1870s or so, by someone building a barely-working airplane with a janky, dangerous steam engine. Langley, in fact, built several model aircraft that successfully flew using these sorts of engines. But Claude’s answer here seems defensible. And like Claude, I similarly would have noted that the control problem was extremely difficult, and the solution to it non-obvious, even if there weren’t technical barriers preventing it from being solved with the available contemporary tech.

For the turbojet, Claude is right on both gating technologies: sufficiently efficient compressors, and turbine blade materials that are capable of withstanding high temperatures. And it’s right as to when the technologies started to get good enough: the first gas turbine that could (barely) do actually useful work appeared in the early 1900s, and stainless steels (which Whittle did in fact use for his first turbine blades) started to appear in the 1910s. I probably would have pushed the “earliest plausible” date somewhat later — maybe 1915-1925 — but Claude’s conception of a sort of barely-working jet engine that’s not really good enough to use in an actual airplane a few years early doesn’t seem impossible.

Based on these checks, I think Claude’s answers are probably reasonably defensible most of the time. And what’s more, they show how genuinely difficult it is to answer these sorts of “how early could a technology have appeared?” questions. Even limiting ourselves to questions of technical possibility and not actual commercial or societal usefulness, good answers to these questions require knowing both a lot of history of some particular technology AND having deep technical knowledge of various technologies as they existed at various points in time. Answering “how early could an airplane have been built?” requires knowing not just who the various flight pioneers were and when they did their work, but the state of steam and internal combustion engine technology at various points in the 19th century and how far they could have plausibly been pushed. There are just vanishingly few people with this sort of deep knowledge about even a few technologies, much less 190 of them spread across two centuries. So while I don’t expect the AI’s answers to be perfect, I think it’s probably collectively a much better analysis than you’d get from almost any single human.

The results

So, how long do we have to wait for new inventions? To start, let’s look at a scatterplot of the results from the Claude simulation. The graph below shows how much earlier on average each invention could have appeared, for both the “earliest plausible” and “earliest straightforward” date ranges.

We can clearly see a few trends on this graph. One is that for most inventions, the gap between when it could have been invented and when it was actually invented is not particularly large. Of the 166 inventions Claude estimated a date for, 107 of them (64%) had an “earliest plausible” date 50 years or less from the actual date, and 150 of them (90%) had an “earliest straightforward” date 50 years or less from the actual date. For more than half the inventions, the average earliest straightforward date of invention was 10 years or less from the actual date.

Conversely, there were a relatively small number of inventions where the gap between “could have been invented” and “was invented” was very large. 30 inventions (18%) had an average gap of more than 100 years between “earliest plausible” and actually invented, and eight inventions had a gap of more than 1000 years. You can see this clearly on a histogram, which shows a large bump of small time gaps, and a long tail of fewer, larger gaps.

The inventions with the longest period between “could have been invented” and “was invented” are below.

There’re a few interesting trends observable here. Many of the longest-delayed inventions — the hypodermic needle, general anaesthetic, stethoscope — are medical inventions. (You could argue the surgical mask could be in this category as well). For the hypodermic needle, this probably needed to wait until the existence of some substance that needed to be injected (such as morphine, first synthesized in 1804), but for other medical inventions this possibly also reflects folks’ reluctance to do inventive-tinkering in a medical context. For general anaesthetic, for instance, the trial and error of getting the dose right was incredibly dangerous, and the inventor Hanaoka Seishu “crippled his mother and blinded his wife perfecting the dose.”

Several of the longest-awaited inventions are ones where the version in the list is an early, impractical version of the one that actually solved a problem. So the “dandy horse” — a two-wheeled, wooden vehicle that was a predecessor of the bicycle — could have been built in antiquity, but the dandy horse wasn’t particularly practical as a means of transportation, and actually useful bicycles had to wait for the improved manufacturing technology of the later 19th century. Likewise, the version of the ballpoint pen that Claude thinks could have been invented much earlier is John Loud’s 1888 version, but Loud’s pen worked poorly and wasn’t successful. Actually useful ballpoint pens are surprisingly difficult to manufacture (China famously couldn’t manufacture them until very recently), and credit for the “useful ballpoint pen” is usually given to Lazlo Biro in 1938. (Claude correctly notes that “useful” versions of both these inventions would need to wait until much later.) Judson’s early zipper and de Martinsville’s early sound-recording device are also examples of early, not-particularly-useful inventions.

Other inventions on this list seem like they might be a case of the surrounding social or technological conditions needing to be right for the invention to appear. So Otis’ elevator safety brake needed to wait until elevators were in higher demand, which probably didn’t occur until steam engines or some other similar power source came along (though maybe you could have water-driven elevators much earlier). Barbed wire perhaps needed to wait until enclosing very large areas of land for grazing became something people needed to do.

And some inventions seem like they might have been genuinely useful had someone thought of them earlier, and simply nobody did. Blanchard’s pattern-tracing lathe, Neilson’s hot blast, and the safety pin all seem like they fall into this category, though perhaps there were good reasons these didn’t appear earlier.

Going back to the scatterplot, the other obvious trend on this chart is that the gap between when an invention becomes possible and when it appears has narrowed over time. If we graph the average and median gaps for inventions by 20-year time periods, we can see that they have fallen over time.

For the 60 post-1900 inventions, every one has a “straightforward” invention date of 50 years or less than the actual date, and 75% of them have a straightforward date of 10 years or less before the actual date. Of the 30 inventions with a gap of more than 100 years between when they could have been invented and when they actually appeared, 29 of them were invented before 1900. So the process for creating new inventions seems to be getting more and more efficient — opportunities are getting noticed and exploited sooner and sooner, up through 1970 at least (which is when the list of major inventions extends to).

We can also look at how wait times vary by type of technology. The chart below shows average wait times by different categories, for both inventions overall and for just post-1900 inventions. We can see that medical inventions have the longest wait, while electronic inventions have the shortest wait.

We can also look at what types of factors tend to be bottlenecks. For some inventions, the bottleneck is primarily scientific: the limiting factor for the transistor is the band theory of quantum mechanics, and the limiting factor for the radio was Hertz’s demonstration of electromagnetic waves. But for other inventions, it’s primarily technological: the turbojet had to wait not for some new physical theory, but until compressor technology and high-temperature steels appeared; likewise the airplane had to wait not for some novel theory of aerodynamics but until a light enough engine appeared. The chart below shows how often “science” or “technology” was the limiting factor for a given invention, for both inventions overall and post-1900 inventions.

In both cases, technology is the bottleneck far more often than science (though of course if you removed enough technological bottlenecks eventually you’d hit a scientific one, and vice versa).

Conclusion

There is of course only so much you can learn from this sort of exercise: at the end of the day, this is based on an AI’s best guess, not a thorough analysis of the various controlling factors by experts. But while I wouldn’t swear to its accuracy, I think the answers are probably mostly pretty good, and enough for us to draw some general (if tentative) conclusions about the nature of technological progress.

My main takeaway is that we mostly don’t wait all that long for new inventions. Since 1800 most inventions have appeared within a few decades of when it was possible to build them, and since 1900 these gaps been even narrower. It also seems likely that medical inventions are more likely to have long wait times than other types of inventions, and that the limiting factor for how early some new technology could appear is most likely to be technological, rather than scientific.