Megafauna extinction and startups

I was recently in Alaska and saw a massive moose, probably the biggest animal I’d ever seen in person. It got me down a rabbit hole as to why moose are so big, which eventually led me into a fascinating story of evolution and extinction that seems particularly relevant to startups in the age of LLMs.

Why being big wins

There seem to be 4 main reasons why moose are so large:

1. Surface area to volume ratio for cold winters. Moose live in very cold regions that have particularly harsh winters. Because of that, the ability to conserve body heat is very important. Surface area to volume ratio works in favor of large bodies here: surface area is generally a square of your dimensions and volume is a cube, so as your dimensions grow, the amount of surface area for heat to escape shrinks compared to your overall mass. This means that larger bodied animals are more adept at retaining heat (the same reasoning works the other way for why you want smaller bodies for long distance runners to allow heat to escape more easily).
2. Kleiber’s Law $BMR \propto M^{0.75}$. Swiss biologist Max Kleiber found in the 1930s that the basal metabolic rate (the energy expenditure of an animal) generally tends to scale to the 3/4 power of an animal’s mass. This means that if an animal gets 100 times larger, then the amount of calories the animal needs to consume only increases by 32 times. So larger animals tend to be more efficient. If there’s enough food available, then a larger animal will tend to use calories more effectively than a smaller one.
3. Size makes predation much harder. Adult moose are so big that they have almost no natural predators. Even those predators like wolves or bears who do attack them very much prefer to attack babies or injured / old moose. Adult bull moose are very large and have very strong, long legs that can kill a wolf with a good strike, so bears/wolves tend to rely on surprise and prey on weakened moose.
4. Energy reserves. The larger you are, the more energy you can carry on your body in the form of fat. This is particularly useful for climates moose live in, which swing wildly from abundance in the spring and summer months to barrenness in the winter months. It’s also generally helpful for making you more robust to environmental changes.

Interestingly, reasons 2-4 apply to most animals and aren’t specific to moose or their arctic habitat. That leads to the next observation. Instead of asking why moose are so large, the opposite question might be more useful: why are there so few large animals?

Megafauna and mega-extinction

In fact, there used to be a lot more throughout history, and scientists even had a name for them: megafauna. There’s even a name for the pattern, Cope’s rule, which says that lineages tend to get larger over time.

• Irish elk: a deer the size of a moose with antlers spanning 12 feet.
• Woolly mammoth: 6 tons, roughly the size of an African elephant.
• Giant ground sloth (Megatherium): a 4-ton sloth that stood taller than an elephant when it reared up.

However, most of them suddenly went extinct in the last 50,000 years. Of the 57 species of herbivore over 1,000 kg that were alive then, only 11 survive today. In North America, the average land mammal shrank by more than 10x. We still have some megafauna (elephants, giraffes, moose), but most animals are now much smaller.

Though there were decades of debate about what caused these extinctions (particularly whether climate change had an impact), scientists now generally accept that humans were the cause. These animals had survived more than twenty glacial cycles over the previous two million years, and the last one was only different in that humans had arrived on the scene. A 2023 study reconstructed the population histories of 139 living megafauna species from their genomes and found that 91% of them started declining between 32,000 and 76,000 years ago, on every continent, and almost exactly tracking the spread of humans. Total megafauna biomass on Earth fell by 92%. The only place the giants held on was Africa, where they’d had a couple million years to get used to us.

Megafauna are perfect for humans: they’re huge, and you only need to hunt one to have enough calories to survive for a very long time. The problem though is that you don’t need a huge amount of hunting in order to kill off a megafauna species, especially if the species reproduces really slowly.

Going extinct, but gradually

So how did humans kill off so many species of megafauna, especially given the relatively small human populations at the time?

Well, when you control for body size, reproductive rate has a massive impact on potential extinction probability. Species producing one or fewer offspring per female per year had more than 50% odds of dying out, regardless of size. You just need:

If a species is held slightly below replacement for a long time, you get extinction over time.

For a fast breeder (think rabbits or mice), $r_{birth}$ is huge and it takes an enormous amount of hunting to flip the inequality. For a mammoth, gestation was about 22 months, females didn’t mature until 10 to 12, and after that they had one calf every 3 to 6 years. This means a few percent of extra mortality a year is all it takes for extinction. You actually don’t have to hunt a species to zero.

When Polynesians arrived in New Zealand, the moa (a 500-pound flightless bird with no natural predators) was gone within about a century. The human population at the time was under 2,000 people, roughly one person per 100 square kilometers. There wasn’t any industrial hunting or big-game culture, but there was just enough persistent drag on a species that couldn’t breed quickly.

The interesting thing is that some of these species lived for thousands of years while $r_{birth} < r_{death} + r_{hunting}$. For example, though mainland mammoths were killed off by about 10,000 years ago, there still existed a small group of mammoths that survived on Wrangel Island in the Arctic Ocean until about 4,000 years ago. There were only a few hundred of them living for millennia. We have parallel examples of this with Caribbean ground sloths, where island populations lasted another 4,000 to 6,000 years after the mainland animals were gone, and Steller’s sea cow, which vanished across most of the North Pacific and then hung on as a Commander Islands remnant until the last animals were killed in 1768.

There’s a distinction here between dead and extinct. A species is functionally dead when it’s held below replacement with no way back (like the mammoths on Wrangel Island), even if the last individual doesn’t die for thousands of years.

What thrived afterwards

As the megafauna died off, it opened up the world for other species to thrive. As Aristotle said, “nature abhors a vacuum” and there was plenty of opportunity as changes in the environment allowed for new winners, typically employing one of three strategies:

Strategy: Generalist (Rats, Pigs)

The first and most common strategy was to be a generalist. Species employing this strategy would eat whatever is around, live wherever there’s shelter, and breed faster than anything could kill them.

Rats are the clearest case. They were perfectly set up to take advantage of human proliferation, as humans made much more of the habitat that rats love. They’re more than happy living in ships, sewers, grain stores, or apartment walls. As humans built up cities, rats found an environment that was incredibly hospitable because of their ability to eat human scraps.

Another key element of the generalist strategy is fast reproduction. Rats have a gestation period of about three weeks and a female can produce dozens of young a year. This turnover rate means you can hunt rats continuously and still fail to push them below replacement (just look at what happened to New York Rat Czar).

Strategy: Adapt (Coyotes, Raccoons, Crows)

The second winning strategy was rarer, because relatively few species were able to pull it off. The strategy was adaptation: as the world changed, these animals changed with it by adjusting their diets, habitats, and behaviors.

Coyotes are the most well-known example of this. Before Europeans remade North America, they were mostly a western prairie animal. As wolves disappeared and forests were cleared, coyotes adapted to be able to live in close proximity to cities. Their social structure became flexible, as they can either hunt alone or in pairs when prey is small, or form loose family groups when they need to take on larger animals. In cities, they flip their schedule and go mostly nocturnal to avoid people and move quickly through developed blocks at night. They live in the leftover green areas like parks, golf courses, and railroad corridors.

Their diet also changed. They prefer rodents and rabbits (things you would find on the prairie), but switch to fruit, pet food, and garbage when rodents aren’t available. They changed their hunting and feeding patterns to take advantage of the human food in cities. This flexibility means they’re now everywhere and have migrated to every US state except Hawaii.

Strategy: Partner (Dogs, Cows, Chickens, Sheep)

The final winning strategy was to partner, basically “if you can’t beat them, join them.” Species that chose this strategy stopped competing with humans and survived by becoming useful to us.

Many animals hit upon this strategy, but dogs are famously good at it. They were originally wolves, but became hunting partners and companions instead of rivals. They’re now everywhere and there are literally a billion of them. The wolves that didn’t take the deal still exist, but they’re far less numerous (scientists estimate there are around 200-250k gray wolves in the world).

This strategy comes with tradeoffs though: dogs are no longer fierce predators, and while there are billions of cows and chickens, their existence isn’t particularly happy. Also, most of them can’t survive outside the human farming system. Partnership is a good way to thrive, but it’s also how you lose control of your future.

Megafauna and startups

I’m particularly fascinated by the megafauna extinction because I think you can compare the introduction of humans in evolutionary history to the arrival of LLMs in the tech world.

There are quite a lot of similarities:

• Before LLMs, it made sense to be as big as possible. Hiring engineers was how you shipped more, and also how you kept those engineers from becoming a competitor. Between 2019 and 2022, Amazon’s headcount rose 93%, Meta’s 92%, Alphabet’s 60%, and Microsoft’s 53%. Bigger teams were less efficient per person, but they were still the optimal choice for a climate where ecommerce and internet spend was increasing dramatically. The focus on hiring was a similar optimization to what the megafauna made as bigger generally means you can accomplish more and dominate in the environment you’re placed.
• The arrival of LLMs is similar to the arrival of humans: there was a massive shock to the system that caused the landscape to change. Coding and many auxiliary functions which previously required armies of people got much cheaper and more efficient. Leverage from LLMs has made size-based defenses much less useful. In fact, the size of your company, which was once an asset, has now become a hindrance.
• Incumbents didn’t immediately recognize that AI native companies were predators. Similar to dodos and the moa, they had never seen this kind of competitor before and they didn’t register as a threat. They were slowly losing the next dollar, but mainly focused on keeping their existing customer base. In 2025, the head of LexisNexis North America said he didn’t see the new legal AI startups as a serious threat. By then Harvey had reached $100 million a year and was in 42% of the AmLaw 100.

Zombie megafauna

Some of the megafauna are already visibly dying, but relatively few of them are extinct. Yes, there are examples like Chegg (its stock fell 40% after their CEO said ChatGPT was hurting new signups) and Stack Overflow (new questions fell to about 1,100 in August 2026, from a peak of more than 200,000 in a month). But in general, like in megafauna extinction, much of the technology ecosystem extinction is playing out more gradually and most incumbents are attempting to adapt.

Nearly every company now has an AI strategy, an AI product, and an executive whose job is to make the company AI-native. So the interesting question is actually whether incumbents will adapt fast enough. A company’s population is its customers, and it shrinks for the same reason a species does:

An incumbent can ship a perfectly good AI product and still lose if competitors are taking customers more quickly. A few things from the megafauna explain how that happens:

• Predators adapt quickly. Megafauna could adapt, but their slow reproduction rate hindered them in comparison to faster reproducers. Humans adapted quickly by using culture and by spreading knowledge through spoken word between generations. Other winners reproduced on much faster cycles (e.g. rats every few weeks). Similarly, an incumbent’s AI strategy moving through annual planning and multiple layers of red tape can be outmaneuvered by a twenty-person startup that ships every week.
• Predators take the calves. Bears don’t go after healthy adult moose, they go after the young, the old, and the sick. AI-native startups similarly won’t be ripping out existing contracts to start. They generally win the next customer, like Harvey did against LexisNexis. The existing customer base might look healthy, but a herd without calves is dying. And once the new species gets established, it’s very hard to dislodge.
• Decline looks normal from the inside. A population shrinking 1% a year takes about 1,400 years to go from a million to zero. That’s instant from a geological standpoint, but basically invisible to anyone who’s living through it. Losing a few percentage points of growth each quarter doesn’t feel alarming for any particular quarter, but taken in aggregate can be catastrophic.
• Most megafauna shrink. Plenty of megafauna species survived in much smaller populations and ranges (lions went from southern Europe, the Middle East, and most of Africa to scattered African populations), and some physically shrank (modern bison are noticeably smaller than their Ice Age ancestors). The likely outcome for most incumbents is to become a smaller company in a smaller niche, while its growth goes to someone new.

Winning strategies in the AI era

The most interesting part to me though is who’s thriving. Each winning strategy since the LLM boom has parallels with evolutionary winning strategies. Generalists eat everything, adapters change what they eat, partners attach to the frontier labs, and new companies are born growing fast. Though, similar to animals, each strategy comes with its own tradeoffs.

Strategy: Generalist (TSMC, Cloudflare, Stripe, OpenRouter)

Rats didn’t win by being clever, they won by eating whatever humans left behind and living in whatever humans built. The generalists of the AI era do the same: they don’t need to pick which lab wins, because they thrive whenever more tokens flow through the system.

TSMC fabs Nvidia’s GPUs, but also Google’s TPUs and Amazon’s Trainium chips, so they eat whenever the system increases total token spend. Cloudflare and Stripe are the backbone that handles the traffic and the payments. Every new AI app needs to sit behind a network and charge its customers, and every AI crawler and agent hitting the web runs through Cloudflare. OpenRouter routes requests to hundreds of models from nearly every lab. It doesn’t care which model wins, only that people keep sending tokens.

The tradeoff is that generalists tend to grow with the tide, and shrink with it too when it goes the other direction.

Strategy: Adapt (Cognition, Intercom, Baseten, Handshake)

Coyotes kept what made them good (hunting skill, flexible packs) and changed their diet and schedule. The companies that have adapted well follow the same pattern of keeping a key asset and changing what they eat.

• Intercom kept its customers and support distribution, but rebuilt the company around their Fin AI agent. They started charging per resolution instead of per seat, cannibalizing their own pricing before someone else did it for them. They recently sold to Salesforce for $3.6 billion.
• Handshake had one of the biggest adaptations, going from a college recruiting network to a frontier lab data provider. They took their network of students and PhDs, but realized they could use that same network to train frontier models (and that this was much more lucrative). They became one of the first data companies to reach $1B in revenue, against a huge amount of internal resistance.
• Baseten started by doing ML infrastructure, but changed its customer from data scientists building apps to LLM inference. They’re now one of the fastest growing inference providers.

Strategy: Partner (Scale AI, Surge AI, Mercor)

These companies stopped competing with the frontier labs and made themselves useful to them instead by supplying the huge amounts of expert human data that labs need. By sheer mass, partnering is the most successful strategy there is: livestock now make up about 60% of mammal biomass on Earth, while wild mammals are about 4%.

Data companies like Mercor, Surge, and Scale are making billions in revenue, but their growth rate isn’t independent. Chickens breed exactly as fast as humans want them to, and data vendors grow exactly as fast as the labs’ data budgets.

Another interesting parallel is seeing different sub-strategies. For example, dogs are valued for judgment and companionship, while chickens are interchangeable. This is similar to why Surge, reportedly selling expert judgment and highly curated data, was able to pull ahead of Scale, which started as a labeling body shop.

Strategy: Start fresh (Cursor, Cognition, Harvey, ElevenLabs, Higgsfield)

The last strategy is one that didn’t have time to develop after the megafauna extinction (we’re too early in evolutionary time), but has shown up after other extinctions. Survivors of mass extinction events go through adaptive radiation and rapidly diversify into all the niches that just opened up. After the dinosaurs died, mammals went from small nocturnal animals to whales, bats, and horses.

Similarly, we’re seeing an explosion of new startups in niches that didn’t even exist a few years ago. AI code editors (Cursor), software factories (Cognition), legal AI (Harvey), voice AI (ElevenLabs), video AI (Higgsfield), and many more fields have suddenly opened up. Starting fresh has advantages in that you can shape yourself specifically to the new environment and there’s no mass to shed or old business to defend.

That said, adaptive radiation is also incredibly wasteful. It produces a burst of new forms and most of them die out. Jasper was one of the first AI copywriting companies, and had raised $125 million at a $1.5 billion valuation, when ChatGPT ate its niche. Tome did the same thing with AI presentations. They had 20 million users, then completely pivoted and abandoned their product once Anthropic and OpenAI moved in.

Which animal are you?

A huge environmental change just opened up a lot of niches, so whether you’re an existing company or starting something new, there’s a ton of opportunity. But remember that adaptive radiation is wasteful and most new species won’t make it.

So, some interesting questions to ponder:

1. What niche just opened? The best opportunities are things that weren’t possible (or weren’t economical) a couple of years ago. If the business could have been built in 2019, someone probably already occupies the niche.
2. How fast do you reproduce? How long does it take to go from idea to a high quality shipped product, and then to customers? If it’s measured in weeks, you’re built for this environment. If it’s still measured in quarters, you should take a look in the mirror.
3. Whose calves are you taking, and who’s taking yours? New companies don’t have to rip out an incumbent’s existing contracts. They can go after the next customer and the fringes of what incumbents make money from. If you’re the incumbent, is there a company winning the customers you would have won two years ago?
4. Are you a dog or a chicken? Almost every AI company depends on the frontier labs to some degree. That’s fine, as long as you add something meaningful that the labs can’t easily produce themselves.

Which brings me back to the moose I saw in Alaska. Moose aren’t the biggest animals that ever lived, or the fastest, or the strongest. But they made it through because they just happened to be built for the world that came next.

The moose got lucky, but you get to choose.