"AI जॉब सर्वनाश" पूरी तरह से एक कल्पना है

The AI Alarmist, “Permanent Underclass” panic isn’t a convincing story. It isn’t even a new story. It’s the “lump-of-labor” fallacy, with updated branding.

The “lump-of-labor” fallacy claims there is a fixed amount of work to be done. It assumes a zero-sum competition between existing workers, and anyone or anything that may do the same job—whether that’s other workers, machines, or in this case, AI. If there is a fixed amount of useful work that needs doing, then if AI does more, humans must do less.

The problem with that premise is that it defies everything we know about people, markets and economics. Human wants and needs are anything but fixed. Keynes famously predicted almost a century ago that automation would lead to a 15-hour work-week, but of course Keynes was wrong. He was right that automation created a “labor surplus,” but rather than just sit back and enjoy the ride, we found new and different productive endeavors to fill our time.

Of course AI will absolutely eliminate some tasks and compress some roles (and there’s some evidence that that may already be happening). The shape of the labor market will change, as it always does when a transformational technology is unlocked. But the claim that AI will produce economy-wide, permanent unemployment is unhelpful marketing, bad economics and worse history. To the contrary, productivity gains should increase demand for labor, because labor becomes more valuable.

Here is our argument why.

“Checkmate, humans?” Come on.

We agree with the doomers—and, frankly, anyone with their eyes open—that the price of cognition is collapsing. AI is getting better and better at what, until recently, was considered the exclusive domain of the human brain.

The doomer argument goes, “If AI can do our thinking for us, then humanity’s ‘moat’ evaporates and our terminal value goes to zero.” Checkmate, humans. Apparently, we’ve done all the thinking we’re ever going to need or want, and now that AI will carry an increasingly large share of the cognitive load, humans slide into obsolescence.

Here’s the thing, though: precedent (and intuition) shows that when the cost of a powerful input falls, the economy does not politely stand still. Costs fall, quality rises, speed rises, new products become viable, and demand moves outward. Jevons Paradox reigns supreme. When fossil fuels first made energy cheap and plentiful, we did more than just put whalers and woodchoppers out of business; we invented plastics!

Contra-doomers, there’s every reason to expect that AI will have a similar effect. Now that AI will carry an increasingly large share of the cognitive load, humans are free to tackle even more ambitious frontiers than ever before.

If history is any guide, we can expect that technological transformation will enlarge the size of the pie.

Every “dominant economic sector” has given way to an even larger successor . . . which, in turn, has made the economy only that much larger.

Tech today is bigger than finance, railroads, or industrials ever were, but still smaller as a fraction of the economy or the market as a whole. Far from being negative-sum, productivity gains have been a positive-sum force on steroids. The net result of having delegated so much of our efforts to machines is that the economy and labor market have only gotten bigger, more diverse, and more complex.

Doomers want you to ignore the history of innovation, freeze-frame the collapsing cost of cognition, and call it the whole movie. They see task-substitution and just stop.

“We’re going to 10x our cognitive output, but rather than do more thinking, we’re going to pat our tum-tums and hit lunch early, and so is everyone else,” reflects not only a massive failure of imagination, but of basic observation. Doomers call it “realism,” but it’s just not what happens, ever!

Luddite Fails

Let’s take a look at what does actually happen, when great leaps forward in productivity ripped through the economy.

Agriculture

In the early 20th century before widespread adoption of farm mechanization, roughly a third of U.S. employment was in farming. By 2017, it was about 2 percent.

If automation caused permanent unemployment, the tractor should have broken the labor market forever. Instead, farm output almost tripled, which supported a massive increase in population—and far from being permanently unemployed, those workers flowed into previously unimagined industries, factories, stores, offices, hospitals, labs, and eventually services and software.

So, sure, you could say that technology upended the career prospects for the median farmhand, but in doing so, it unlocked a global labor (and resource) surplus, and an entirely new economy.

Electrification

Electricity tells a similar story.

Electrification did not just swap one power source for another. It replaced shafts and belts with individual motors, forced factories to reorganize around entirely new workflows, and created entirely new categories of consumer and industrial goods.

This is exactly what we expect to see during the distinct phases of technological revolutions, as documented by Carlota Perez in Technological Revolutions and Financial Capital: huge upfront investment and financial interest, huge declines in the costs of durable goods, and then a generational run for durable goods manufacturers.

It took time for electricity to work its productive magic. At the turn of the 20th century, only 5 percent of American factories used electricity to power their machines, and fewer than 10 percent of homes had electricity at all.

By 1930, electricity supplied almost 80 percent of manufacturing power, and labor productivity growth doubled for decades.

Far from destroying demand for labor, more productivity meant more manufacturing, more salespeople, more lending, and more commercial activity—not to mention the second-order effects of labor-saving devices, like washing machines and cars, both of which pulled more people into higher value endeavors than was previously possible.

As prices for cars fell, both auto production and employment exploded.

That is what a real general-purpose technology does: it reorganizes the economy and expands the frontier of useful work.

We see this again and again. Did VisiCalc and Excel doom the bookkeepers? Emphatically, no. Vastly more efficient computational technology led to an explosion of bookkeepers, and created an entire industry of FP&A.

We lost ~1M “bookkeepers” and gained ~1.5M “financial analysts.”

Those new service-sector jobs

It’s of course not always the case that task-substitution leads to job-growth in some adjacent part of the economy. Sometimes, the productivity surplus materializes as net-new job-growth in an entirely unrelated industry.

But what if AI means that some people will become fantastically wealthy, leaving the rest behind?

Well, at a minimum, those fantastically wealthy people will need to spend their money somewhere, creating whole new service industries from scratch, just like they did before:

Massive productivity gains and subsequent wealth-creation led to entirely new lines of work that may never have come to fruition without rising incomes and worker availability (even though they were technologically possible, well before the 90s). However one feels about service industries that cater to the wealthy, the net result left everyone better off, as more demand led to a massive ramp in median wages (leading to more “wealthy” people).

Ernie Tedeschi, Stripe’s in-house economist, offers a fascinating “all-in-one” example of a job disrupted, transformed, and remade with technology: travel agents.

Did technology reduce demand for travel agents? Yes, absolutely:

Travel agency payrolls are today about half of what they were at the turn of the century, almost certainly because of technology.

So, does that mean technology was a job-killer? No, again, because travel agents didn’t just end up permanently unemployed. They found work elsewhere in the economy, which overall has about the same employment:population ratio now, as it did in 2000 (when adjusted for aging).

Meanwhile, for the travel agents who did remain in the now tech-enabled industry, increased productivity meant higher wages than before:

“Average weekly earnings at travel agencies were 87% of overall average weekly earnings back in the heyday of 2000. By 2025, the ratio had reached 99%, meaning travel agency wages had outpaced the rest of the private sector over that span.”

So, even then, while it’s true that tech devastated travel agent employment, in the aggregate, working-age people are just as employed as they were before, and the remaining travel agents are doing better than ever.

Augmentation > Substitution (and the Jobs that Don’t Yet Exist)

That last point is very important, and reflects yet another way that doomers are only telling one small part of the story.

For some jobs, AI is an existential threat. True. But for others, AI is a force-multiplier—which will make those jobs that much more valuable. For every job at-risk of AI-Substitution, there are other jobs that stand to benefit:

Goldman’s estimated “AI Substitution” effects are more than balanced-out by the effects of “AI Augmentation.”

Management teams also appear to be much more focused on augmentation than substitution, for what it’s worth:

As of now, AI-as-augmentation out-mentions AI-as-substitution on earnings calls by ~8:1.

While Goldman doesn’t even include them on their “augmentation” list, software engineers are probably the perfect example of an AI Augmented role.

AI is a force-multiplier for coding. Not only are git pushes skyrocketing (as are new apps and new business formation), but it appears as though demand for SWE is inflecting upwards:

Software Development jobs (both by count, and a percent of the overall job market) have been increasing since the beginning of 2025.

Is that because of AI? Truthfully, it’s probably too soon to tell, but AI most definitely augments the work of software engineering, not to mention that AI is top-of-mind for every executive at every company.

With everyone trying to figure out how to incorporate AI into their businesses, it stands to reason that there would be substantial hiring efforts underway to make that happen, making certain employees more valuable, not less:

AI-exposure seems to be driving above-trend wage-growth (which is especially true for systems design).

Those gains may be somewhat narrow for now, but it’s still so, so early. As expertise widens, so too will the opportunities. In all events, it’s not the data that the doomers want you to see.

Meanwhile, according to Lenny Rachitsky (of Lenny’s Newsletter, one of the great tech-insider communities), open PM jobs continue to climb (off their rate-driven collapse) and are now more plentiful than they’ve been since 2022:

Hiring growth in both software engineers and product managers is a concise example of why the “lump of labor” fallacy is wrong. If AI substituted thinking 1:1, then you might plausibly expect, “PMs need fewer engineers,” or you could argue “engineers need fewer PMs,” but that isn’t what we see. We see demand for both continuing to rebound, because what matters is people are getting more work done.

That’s why the doomer failure is primarily a failure of imagination. They focus on the tasks that get automated away, and ignore a new frontier of demand that will create jobs we haven’t even conceived of yet:

The majority of new jobs created since 1940 didn’t even exist in 1940. And in 2000, it was pretty easy to imagine all the travel agents that would be out of a job, but it was probably much harder to imagine an entire middle-market tech services industry built around “cloud migration,” since, of course, the cloud was more than a decade away.

Continue reading in the a16z Newsletter: https://www.a16z.news/p/the-ai-job-apocalypse-is-a-complete