A very good read from a respected source!


FT. Opinion. Artificial intelligence. It’s already time to think about an AI tax.

Jobs will be displaced or erased by the next chapter of the tech revolution, and companies must share the social costs.


The writer is international policy director at Stanford University’s Cyber Policy Center and special adviser to the European Commission

After years of wrangling, a global minimum corporate tax rate of 15 per cent is finally in effect. These groundbreaking new rules were driven by the desire to prevent big companies, often in the tech sector, from flocking to tax havens or jurisdiction shopping. There are a host of public policy solutions that the anticipated $220bn in annual collection can help address. But even though the ink on the treaty is barely dry, it is time to start talking about a new one: targeted at artificial intelligence companies.

Generative AI is already bringing a host of societal challenges. Global job losses are one key expected effect. While the political debate remains largely focused on safety and security harms, various studies foresee deep disruptions to labour because of the technology. It was Elon Musk who raised the future of work on the margins of last year’s AI safety summit. He casually mentioned, in a conversation with UK Prime Minister Rishi Sunak, that we must anticipate a society in which “no job is needed”. The reverberations of that are unimaginable.

And it is not just tech mavericks like Musk who predict major disruption. A study by Goldman Sachs projects almost $7tn of additional growth for the global economy over 10 years, while expecting that roughly two-thirds of US jobs will be at risk of being impacted by AI. McKinsey anticipates up to 30 per cent of worked hours in America will be affected by automation in the next six years. Twelve-million people will need “occupational transitions” in addition to those already facing obsolescence.

While consultants are optimistic that AI will “enhance” jobs rather than replace them, research by ResumeBuilder found that more than a third of business leaders said AI had already replaced workers in 2023. There is no indication that the more sophisticated versions of generative AI would lead to a slowdown in impact on employment.

Although scenarios differ, they all signal similar trends. Jobs will be displaced, and even if there may be an economic upside in the longer term, the transition will require significant public policy efforts. Governments need to zoom in on the specifics of their own national economies and anticipate the impact of AI, sector by sector.

On the corporate side, there are also unprecedented shifts taking place. Already, we see AI companies as a major component of the most highly valued corporations in the world. In the US, tech companies helped drive gross domestic product growth in 2023. At the same time, AI threatens to exacerbate the concentration of capital into the hands of even fewer companies.

“Over the past four decades, automation has raised productivity and multiplied corporate profits, but it has not led to shared prosperity in industrial countries,” say Daron Acemoğlu and Simon Johnson in a paper for the IMF. In other words, the benefits of automation are not shared automatically. (More research is needed into the specific effects on jobs across the global south.)

Without intervention, the next chapter of the technological revolution risks once again privatising profits while pushing the costs of mitigating its harms on to the public. Paying for welfare and reskilling laid-off workers are not just economic downsides: they signal the kinds of societal shifts that easily lead to political unrest. For generations, work has been the foundation not just of family income but also of people’s routine and sense of purpose. Try imagining what you would do without your job.

To rebalance the cost-benefit impacts of AI in favour of society — as well as to make sure the necessary response is affordable at all — taxing AI companies is the only logical step. I had not anticipated starting 2024 by agreeing with Bernie Sanders and Bill Gates, both of whom have proposed a tax on job-taking robots in the past, but here we are. An updated version of their plan, taking in generative AI’s progress, is needed.

A debate resulting in global political consensus may take years and should start now. Agreement must be reached around the percentage of revenue or profit to be taxable and the purpose of the tax — should it be focused on mitigating job losses specifically or on addressing the multiple societal impacts of AI more broadly? And given that China and the US are both leading AI developers and have not yet implemented the minimum corporate tax rate rules domestically, incentives and enforcements will have to be effective.

It took years to get a minimum global corporate tax base in place. Considering the impending costs to society, a conversation about a targeted tax for billion-dollar AI companies cannot wait.


Learn More: Humans Need Not Apply (15:00)

“This time it’s different.” – 16,429,536 views. 13 Aug 2014.

Every human used to have to hunt or gather to survive. But humans are smart…ly lazy so

we made tools to make our work easier. From sticks, to plows, to tractors we’ve gone

from everyone needing to make food to, modern agriculture with almost no one needing to

make food — and yet, we still have abundance.

Of course, it’s not just farming, it’s everything. We’ve spent the last several

thousand years building tools to reduce physical labor of all kinds. These are mechanical muscles.

Stronger, more reliable, and more tireless than human muscles ever could be.

And that’s a good thing. Replacing human labor with mechanical muscles frees people to specialize

and that leaves everyone better off – even those still doing physical labor. This is how economies

grow and standards of living rise.

Some people have specialized to be programmers and engineers whose job is to build mechanical

minds. Just as mechanical muscles made human labor less in demand so are mechanical minds

making human brain labor less in demand.

This is an economic revolution. You may think we’ve been here before, but we haven’t.

This time is different.

## Physical Labor

When you think of automation, you probably think of this: giant, custom-built, expensive,

efficient, but really dumb robots blind to the world and their own work. They were a

scary kind of automation but they haven’t taken over the world because they’re only

cost effective in narrow situations.

But they’re the old kind of automation, this is the new kind.

Meet Baxter.

Unlike these things which require skilled operators and technicians and millions of dollars,

Baxter has vision and can learn what you want him to do by watching you do it.

And he costs less than the average annual salary of a human worker. Unlike his older

brothers he isn’t pre-programmed for one specific job, he can do whatever work is within the

reach of his arms. Baxter is what might be thought of as a general purpose robot and

general purpose is a big deal.

Think computers, they too started out as highly custom and highly expensive, but when cheap-ish

general-purpose computers appeared they quickly became vital to everything.

A general-purpose computer can just as easily calculate change or assign seats on an airplane

or play a game or do anything just by swapping its software. And this huge demand for computers

of all kinds is what makes them both more powerful and cheaper every year.

Baxter today is the computer of the 1980s. He’s not the apex but the beginning. Even

if Baxter is slow his hourly cost is pennies worth of electricity while his meat-based

competition costs minimum wage. A tenth the speed is still cost effective when it’s a

hundredth the price. And while Baxter isn’t as smart as some of the other things we will

talk about, he’s smart enough to take over many low-skill jobs.

And we’ve already seen how dumber robots than Baxter can replace jobs. In new supermarkets

what used to be 30 humans is now one human overseeing 30 cashier robots.

Or take the hundreds of thousand baristas employed world-wide? There’s a barista robot coming

for them. Sure maybe your guy makes the double-mocha-whatever just perfect and you’d never trust anyone

else — but millions of people don’t care and just want a decent cup of coffee. Oh, and

by the way this robot is actually a giant network of robots that remembers who you are

and how you like your coffee no matter where you are. Pretty convenient.

We think of technological change as the fancy new expensive stuff, but the real change comes

from last decade’s stuff getting cheaper and faster. That’s what’s happening to robots

now. And because their mechanical minds are capable of decision making they are out-competing

humans for jobs in a way no pure mechanical muscle ever could.

## Luddite Horses

Imagine a pair of horses in the early 1900s talking about technology. One worries all

these new mechanical muscles will make horses unnecessary.

The other reminds him that everything so far has made their lives easier — remember all

that farm work? Remember running from coast-to-coast delivering mail? Remember riding into battle?

All terrible. These city jobs are pretty cushy, and with so many humans in the cities there

will be more jobs for horses than ever.

Even if this car thingy takes off – he might say – there will be

new jobs for horses we can’t imagine.

But you, dear viewer, from beyond 2000 know what happened — there are still working horses,

but nothing like before. The horse population peaked in 1915 — from that point on it was

nothing but down.

There isn’t a rule of economics that says better technology makes more better jobs

for horses. It sounds shockingly dumb to even say that out loud, but swap horses for humans

and suddenly people think it sounds about right.

As mechanical muscles pushed horses out of the economy, mechanical minds will do the

same to humans. Not immediately, not everywhere, but in large enough numbers and soon enough

that it’s going to be a huge problem if we are not prepared. And we are not prepared.

You, like the second horse, may look at the state of technology now and think it can’t

possibly replace your job. But technology gets better, cheaper, and faster at a rate

biology can’t match.

Just as the car was the beginning of the end for the horse so now does the car show us

the shape of things to come.

## Automobiles

Self-driving cars aren’t the future: they’re here and they work. Self-driving cars have

travelled hundreds of thousands of miles up and down the California coast and through

cities — all without human intervention.

The question is not if they’ll replaces cars, but how quickly. They don’t need to be perfect,

they just need to be better than us. Humans drivers, by the way, kill 40,000 people a

year with cars just in the United States. Given that self-driving cars don’t blink,

don’t text while driving, don’t get sleepy or stupid, it’s easy to see them being better

than humans because they already are.

Now to describe self-driving cars as cars at all is like calling the first cars mechanical

horses. Cars in all their forms are so much more than horses that using the name limits

your thinking about what they can even do. Lets call self-driving cars what they really


Autos: the solution to the transport-objects-from-point-A-to-point-B problem. Traditional cars happen to be human

sized to transport humans but tiny autos can work in warehouses and gigantic autos can

work in pit mines. Moving stuff around is who knows how many jobs but the transportation

industry in the United States employs about three million people. Extrapolating world-wide

that’s something like 70 million jobs at a minimum.

These jobs are over.

The usual argument is that unions will prevent it. But history is filled with workers who

fought technology that would replace them and the workers always lose. Economics always

wins and there are huge incentives across wildly diverse industries to adopt autos.

For many transportation companies, humans are about a third their total costs. That’s

just the straight salary costs. Humans sleeping in their long haul trucks costs time and money.

Accidents cost money. Carelessness costs money. If you think insurance companies will be against

it, guess what? Their perfect driver is one who pays their small premiums and never gets

into an accident.

The autos are coming and they’re the first place where most people will really see the

robots changing society. But there are many other places in the economy where the same

thing is happening, just less visibly.

So it goes with autos, so it goes for everything.

## The Shape of Things to Come

It’s easy to look at Autos and Baxters and think: technology has always gotten rid of

low-skill jobs we don’t want people doing anyway. They’ll get more skilled and do better

educated jobs — like they’ve always done.

Even ignoring the problem of pushing a hundred-million additional people through higher education,

white-collar work is no safe haven either. If your job is sitting in front of a screen

and typing and clicking — like maybe you’re supposed to be doing right now — the bots

are coming for you too, buddy.

Software bots are both intangible and way faster and cheaper than physical robots. Given

that white collar workers are, from a company’s perspective, both more expensive and more

numerous — the incentive to automate their work is greater than low skilled work.

And that’s just what automation engineers are for. These are skilled programmers whose

entire job is to replace your job with a software bot.

You may think even the world’s smartest automation engineer could never make a bot to do your

job — and you may be right — but the cutting edge of programming isn’t super-smart programmers

writing bots, it’s super-smart programmers writing bots that teach themselves how to

do things the programmer could never teach them to do.

How that works is well beyond the scope of this video, but the bottom line is there are

limited ways to show a bot a bunch of stuff to do, show the bot a bunch of correctly done

stuff, and it can figure out how to do the job to be done.

Even with just a goal and no knowledge of how to do it the bots can still learn. Take the

stock market which, in many ways, is no longer a human endeavor. It’s mostly bots that taught

themselves to trade stocks, trading stocks with other bots that taught themselves.

As a result, the floor of the New York Stock exchange isn’t filled with traders doing their

day jobs anymore, it’s largely a TV set.

So bots have learned the market and bots have learned to write. If you’ve picked up a newspaper

lately you’ve probably already read a story written by a bot. There are companies that

teach bots to write anything: sports stories, TPS reports, even say, those quarterly

reports that you write at work.

Paper work, decision making, writing — a lot of human work falls into that category

and the demand for human metal labor is these areas is on the way down. But surely the professions

are safe from bots? Yes?

## Professional Bots

When you think ‘lawyer’ it’s easy to think of trials. But the bulk of lawyering is actually

drafting legal documents, predicting the likely outcome and impact of lawsuits, and something

called ‘discovery’ which is where boxes of paperwork gets dumped on the lawyers and they

need to find the pattern or the one out-of-place transaction among it all.

This can be bot work. Discovery, in particular, is already not a human job in many law firms.

Not because there isn’t paperwork to go through, there’s more of it than ever, but because

clever research bots shift through millions of emails and memos and accounts in hours

not weeks — crushing human researchers in terms of not just cost and time but, most

importantly, accuracy. Bots don’t get sleepy reading through a million emails.

But that’s the simple stuff: IBM has a bot named Watson: you may have seen him on TV

destroy humans at Jeopardy — but that was just a fun side project for him.

Watson’s day-job is to be the best doctor in the world: to understand what people say

in their own words and give back accurate diagnoses. And he’s already doing that at

Slone-Kettering, giving guidance on lung cancer treatments.

Just as Auto don’t need to be perfect — they just need to make fewer mistakes than humans —

the same goes for doctor bots.

Human doctors are by no means perfect — the frequency and severity of misdiagnoses are

terrifying — and human doctors are severely limited in dealing with a human’s complicated

medical history. Understanding every drug and every drug’s interaction with every other

drug is beyond the scope of human knowability.

Especially when there are research robots whose whole job it is to test thousands of new

drugs at a time.

And human doctors can only improve through their own experiences. Doctor bots can learn from

the experiences of every doctor bot. Can read the latest in medical research and keep track

of everything that happens to all their patients world-wide and make correlations that would

be impossible to find otherwise.

Not all doctors will go away, but when the doctor bots are comparable to humans and they’re

only as far away as your phone — the need for general doctors will be less.

So professionals, white-collar workers and low-skill workers all have things to worry about

from automation. But perhaps you are unfazed because you’re a special creative snowflake. Well

guess what? You’re not that special.

## Creative Bots

Creativity may feel like magic, but it isn’t. The brain is a complicated machine — perhaps

the most complicated machine in the whole universe — but that hasn’t stopped us from

trying to simulate it.

There is this notion that just as mechanical muscles allowed us to move into thinking jobs

that mechanical minds will allow us to move into creative work.

But even if we assume the human mind is magically creative — it’s not, but just for the sake of argument —

artistic creativity isn’t what the majority of jobs depend on. The number of writers and poets

and directors and actors and artists who actually make a living doing their work is a tiny,

tiny portion of the labor force. And given that these are professions dependent

on popularity they’ll always be a very small portion of the population.

There can’t be such a thing as a poem and painting based economy.

Oh, by the way, this music in the background that you’re listening to? It was written by a bot.

Her name is Emily Howell and she can write an infinite amount of new music all day for free.

And people can’t tell the difference between her and human composers

when put to a blind test.

Talking about artificial creativity gets weird fast — what does that even mean?

But it’s nonetheless a developing field.

People used to think that playing chess was a uniquely creative human skill that machines

could never do right up until they beat the best of us. And so it will go for all human talents.

## Conclusion

Right: this may have been a lot to take in, and you might want to reject it — it’s

easy to be cynical of the endless and idiotic predictions of futures that never are. So

that’s why it’s important to emphasize again that this stuff isn’t science fiction. The robots

are here right now. There is a terrifying amount of working automation in labs and warehouses

around the world.

We have been through economic revolutions before, but the robot revolution is different.

Horses aren’t unemployed now because they got lazy as a species, they’re unemployable.

There’s little work a horse can do that do to pay for its housing and hay.

And many bright, perfectly capable humans will find themselves the new horse: unemployable

through no fault of their own.

But if you still think new jobs will save us: here is one final point to consider. The

US census in 1776 tracked only a few kinds of jobs. Now there are hundreds of kinds of

jobs, but the new ones are not a significant part of the labor force.

Here’s the list of jobs ranked by the number of people who perform them – it’s a sobering

list with the transportation industry at the top. Continuing downward, all of this work existed

in some form a hundred years ago and almost all of them are targets for automation. Only

when we get to number 33 on the list is there finally something new.

Don’t that every barista or white collar worker need lose their job before things are a problem.

The unemployment rate during the great depression was 25%.

This list above is 45% of the workforce. Just what we’ve talked about today, the stuff that

already works, can push us over that number pretty soon. And given that even in our modern

technological wonderland new kinds of work aren’t a significant portion of the economy,

this is a big problem.

This video isn’t about how automation is bad — rather that automation is inevitable. It’s

a tool to produce abundance for little effort. We need to start thinking now about what to

do when large sections of the population are unemployable — through no fault of their own.

What to do in a future where, for most jobs, humans need not apply.