Note: This article was primarily written for a less technical and less AI-savvy audience than LessWrong readers. If you're already familiar with upcoming AI developments, you'll probably find Part 2 (the second half of the article) more engaging.

We keep hearing that AI is going to replace a bunch of jobs in the next few years, but no one really knows exactly how that’s going to happen. When will this start, and which exact jobs are in danger? What’s the plan for dealing with this? Right now, it seems like people are vaguely aware of this threat but continuing on with business as normal. If this is really coming in the next 24 months, shouldn’t we be a little more worried, and have a few more answers to these questions?

That’s what I hoped to figure out when I started writing this article. I wanted to cut through the vague proclamations and actually lay out a bit of a clearer path for what’s coming. Other than feeling like society as a whole was not treating this issue with the seriousness it deserved, my motivation was also personal; I wanted to know, how long can I continue doing the kind of work I am doing?

This article is broken down into two parts:

1. Part 1: What will AI actually be able to do in the next few years? (this post)
   Current AI systems clearly aren’t ready to replace most of our jobs, so when will they be ready?
2. Part 2: So…will we lose our jobs?
   Once we understand what AI will actually be able to do, we’ll look at what that means for jobs and the white-collar labor market as a whole. Will you lose your job? I’ll walk through some specific scenarios that are likely about to play out.

This article focuses only on white-collar jobs, as that is the main segment of the workforce that AI will impact in the next few years.

If you are already familiar with most of the technological changes coming within the next few years, I suggest skipping to Part 2, as that may be more interesting to you.

Part 1: What will AI actually be able to do in the next few years?

Currently, our AI tools have many limitations, such as:

• They don’t have long-term memory
• We can only interact with them through chat interfaces, or in other narrow ways. We can’t interact with them in the same way we might with another coworker, i.e. through meetings, emails, Slack/Teams messages, etc.
• They still can’t use a computer the same way humans can
• They are still limited in their understanding of our instructions. They don’t usually understand the exact situation we face, because they don’t have all the context we have about our workplace and our personal lives. This makes them feel less intelligent.

Because of these limitations, we don’t truly feel like AI systems can replace humans. And it is also why current predictions of how AI will impact the future often focus on specific, narrowly defined tasks that AI will be able to automate.

Most studies that examine how AI will impact the workforce treat AI as a task automation tool. They examine what tasks humans do in their jobs, and try to categorize whether AI will be able to do those things, according to all the limitations we mentioned above. These studies usually lead to the conclusion that AI will only significantly impact a small proportion of the workforce in the near future.

But these predictions are looking at AI the wrong way.

Coworkers

ChatGPT was an “oh shit” moment for the world. It was a moment where everyone recognized that a new technology had been unleashed which was way different than anything else we had access to before.

Another such moment will arrive within the next 1-2 years. This technology will have many names, usually involving the word, “agent”, but I’m going to call it the remote AI coworker.

Imagine being able to purchase a product that can actually do everything a human can do on a computer. Like a truly effective remote coworker. Not only can it use a computer, it has the skills of a mid-level software engineer, it’s able to pass the US medical licensing exam, it aces the bar, and can crush basically any written tests you would give to a graduate university student.

This is the remote AI coworker. And it will be broadly available to the public between 2026 and 2028.

That is a bold statement, so let me back it up.

The trajectory of AI

“We are on course for [artificial general intelligence] by 2027. These AI systems will basically be able to automate all cognitive jobs (think: all jobs that could be done remotely).”

Leopold Aschenbrenner

(Extremely) exponential progress

In 2023, researchers created one of the most challenging tests that had ever been designed for AI, a test that even PhD experts could only score around 70% in. It contained difficult questions in biology, physics, and chemistry, and we expected this test to be a good way to measure AI progress for years to come. The top AI model at the time, GPT-4 (released in March 2023), only scored around 36%, which was a bit better than what you would score if you randomly guessed answers.

18 months later, an AI model was released which outperformed Phd experts and scored almost 80% on this test.

The trend of AI tests being released that are considered “impossible to beat”, and then being defeated by AI models within just a few months has become common in the last 2-3 years. This is because the algorithm that trains these models produces better results every time we give it more computing power and more data. And these are both problems that we are able to solve with more money.

“Money in, IQ points come out” - Jeremie Harris

Just 5 years ago, we were spending less than $1M per AI model. Today, we are training models on a $1B budget. By 2027, we will be spending hundreds of billions of dollars to train the next generation of AI models.

These performance improvements are starting to make their way to a new application of AI technology: agents.

This is a test developed to measure how well AI agents can perform real world tasks that are done in human jobs. It contains tasks such as navigating to websites, filling out forms, and contacting people to ask questions. These were tasks with multiple steps and required AI agents to be able to proficiently use a computer, and generally operate like remote workers would in a workplace setting. The top AI model in May 2024 was only able to perform about 9% of these tasks correctly.

In January 2025, our top AI model is now able to complete 38% of the tasks correctly. We more than tripled the performance of AI agents within 8 months. Within the next year or two, we expect to reach scores of 80% or more on such tests.

In January 2025, the CEO of OpenAI stated in his personal blog, and in an interview with Bloomberg:

“In 2025, we may see the first AI agents “join the workforce” and materially change the output of companies.”

“I think [artificial general intelligence] will probably get developed during this president’s term"

That same month, Mark Zuckerberg stated,

“Probably in 2025, we at Meta…are going to have an AI that can effectively be a sort of mid-level engineer that you have at your company that can write code.”

The first versions of these will be released within 2025 (one already has been), and most people will dismiss these early agents as useless due to their propensity to make mistakes. But as we have seen, the exponential improvement in these systems will soon make these agents more reliable than human coworkers for most tasks.

And the odds are, this will happen between 2026 and 2028. Companies are already anticipating this. Salesforce has already stopped hiring software engineers.

Dario Amodei, CEO of the leading AI company Anthropic, stated recently:

“By 2025, 2026, maybe 2027, we would expect to see models that are better than most humans at most things”.

How will my company actually use this technology?

Ok, but…how exactly will these remote AI coworkers plug into our workplaces? It can help to give specific examples of how certain occupations will use this technology to truly be able to appreciate its real impact on the workforce.

The purpose of the case studies below is not to predict how exactly these jobs will change, but a question I always had is, “how exactly will AI do the things I do in my job today?”. I want to dive into specific tasks for this purpose, basically to sanity check myself and make it clear that this technology can get deep into the weeds.

AI will impact basically every white collar occupation that primarily uses a computer, including fields with high training requirements such as medical, legal, and scientific research. But for the sake of this article (and my personal knowledge of how these jobs work), I will provide a quick case study into how the following occupations will be impacted:

1. Software engineers
2. Marketing associates
3. Customer support associates

But first, let’s set the context of how a company would actually start using these remote AI coworkers.

Hiring a remote coworker

Even if you hire a genius to do a job, they still need to figure out how the company actually does things and learn all the internal jargon, processes, and norms in order to be useful to their team. This is called onboarding. A significant portion of this onboarding will be done almost instantly with these remote AI coworkers, as they will integrate into the companies’ digital repositories of information, and ingest everything they can.

This initial “ingestion of data” will include:

1. All internal knowledge bases and wikis (e.g. Google Drive, Confluence, Notion)
2. All messages in internal communication platforms (i.e. Slack, email)
3. All project management boards and task tracking systems (e.g., Jira, Asana, Trello)
4. Code repositories, commit histories, and associated documentation (e.g., GitHub, Bitbucket)
5. Customer relationship management systems (e.g., Salesforce, HubSpot)

After this initial phase of data ingestion, the AI co-workers will already be 75-80% ready to start fully contributing to their team. They will be “put to work” immediately, but there is a limiting factor which will mean they will take a few months before they are fully “up to speed” and able to contribute to their maximum capacity.

This limiting factor is the fact that most information is not stored anywhere digitally; it is stored in people’s heads. One of the reasons it takes months, sometimes years for new workers to start contributing to their full capacity is that they are constantly gathering information about the company from other coworkers through conversations and formal/informal meetings. The AI coworker will have to go through this same process, and we can call this the “longer” onboarding.

Let’s assume that for the case studies below, your personal remote AI coworker has been around for 6 months already, and has gone through this initial onboarding, as well as the “longer” onboarding.

Quick case study 1: Junior software engineer

As a junior software engineer, your job mostly involves picking up “tickets”, which are usually descriptions of features that need to be built in the software platform your team is building. A ticket could be as simple as “edit this web page’s font to be size 16”, or as complex as “design and implement a payment system for our online store”.

For most tickets:

The remote AI coworker will be able to take in a ticket, understand what part of different parts of the codebase to modify, and make the required changes within a matter of seconds. Once they produce this code, they will need some guidance from humans to ensure that their solution matches all the “unsaid” requirements of the ticket. There are many things that humans assume and don’t write down, and this will often need to be specified by humans in the “review” process of these tickets.

Essentially, a human will mostly be around to “guide” the AI coworker to do its work properly.

For some tickets:

For some tickets, the AI model will either:

1. Make progress, but get stuck due to limitations in its knowledge or context required to solve the problem
2. Solve the problem incorrectly

This is where humans will be required to step in and help out, and unblock the AI systems whenever they get stuck.

Takeaway

Software engineers will transition to primarily be “managing” or “steering” AI models to do what needs to be done, rather than primarily writing code themselves.

Quick case study 2: Marketing associate

As a marketing associate with a few years of experience, you might handle tasks such as drafting text for campaigns, creating social media posts, analyzing campaign performance, and conducting basic research. You are also often communicating with other teams and marketing associates to do your work, so you need to have good interpersonal and communication skills.

Most tasks

AI will generate first-draft copies for emails, social media posts, landing pages, etc. Much of the repetitive writing will be done at the click of a button. It will also be able to effectively communicate with other team members through any platform used by the company, such as email, Slack, Teams, and also be able to attend meetings virtually.

Other tasks

When brainstorming entirely new campaign ideas or responding to nuanced marketing challenges (like crisis communications or highly specialized brand messaging), AI will produce content that is “off-brand” or too generic. A human would need to step in to refine or completely revamp the approach. It will also struggle with things like interpreting complex data that doesn’t have much context.

Takeaway
Marketing associates will move away from the grunt work of manual data collection or writing repetitive copy. Instead, they would spend more time as an editor and strategic reviewer—tweaking the AI’s output to match the brand’s voice, verifying facts, and making final decisions on campaign directions. These were things that were usually reserved for higher-level marketing managers.

Quick case study 3: Customer support associate

Customer support associates often spend their days handling queries from customers via phone, email, or chat. These range from simple “Where’s my order?” questions to more complex troubleshooting or escalations.

Most tasks

The AI co-worker will handle basic customer queries—tracking orders, providing estimated delivery times, etc. For common or previously documented issues (e.g., resetting passwords, clarifying billing questions), AI will be able offer step-by-step instructions and even schedule appointments

In these straightforward cases, human support associates would primarily monitor AI interactions, stepping in to confirm unusual requests or placate an upset customer if the AI’s tone or approach starts to falter.

Other tasks

If a customer’s issue involves unique circumstances, the AI may not have enough context or may provide unhelpful answers - a human will need to take over in these cases. Some issues will require a personal touch and empathy as well, which a human will also need to step in for.

Takeaway
The role of the customer support associate will shift from direct problem-solving to being needed for escalations and more nuanced situations that the AI is struggling with. While the AI coworker will handle a large volume of routine inquiries quickly and consistently, humans will still need to provide their context and their empathic skills.

Takeaways

The obvious takeaway is that in any kind of knowledge work, we will be transitioning away from doing the core work ourselves, and moving towards becoming “managers” of these remote AI coworkers. If you have a junior employee that now handles most of your routine tasks, you will no longer spend most of your time doing those tasks.

This is an entirely new way for humans to offer value in the economy.

It’s an entirely new world.

It can be easy to intellectually understand this but not be able to truly feel the magnitude of it. If you still have doubts about the timelines of this transition, I encourage you to do a quick google search for “AI agents” and switch to the news tab. This article might be a good place to start.

So what does this transition mean for our jobs? Will white collar workers still be able to offer value in this economy? Will we face mass layoffs?

Part 2: So…will we lose our jobs?

After understanding the capabilities of these remote AI coworkers in part 1, it might seem obvious to assume that many white collar workers will be immediately laid off once this technology is deployed. But there are some factors that make this a less obvious outcome than it might initially seem.

Before we start laying out specific scenarios about what might happen to our jobs, I want to describe some economic ideas that helped make sense of previous times in history when technology transformed economies. These concepts will help us ground our predictions in reality. Despite the AI revolution being different from many of these previous technological revolutions, there are enough similarities to be able to gain insight into what might happen next.

More supply leads to more demand leads to more…work to do

In the early years of car production, without any advanced processes or technology, humans were responsible for assembling each part of the car manually. People who worked in car factories were high-skilled artisans and engineers, as the job required a deep knowledge of each part. Factories could only produce a single car once roughly every 12 hours.

Then, when the assembly line was created, the production of cars started requiring minimal training, and the need for these artisanal workers was no longer needed. You could suddenly start producing many more cars with much fewer workers, and the average time to produce a car went down to about 90 minutes. This productivity was boosted further when automation and advanced technologies were introduced in car factories.

Today, car factories employ more people than ever before. Why is this?

When it became easier to produce cars, it made it much cheaper to make cars, and as a result, many more people started buying cars. The demand for cars increased by orders of magnitude, because it was now so easy to make them! This increased demand meant that employers now needed many more factories to build cars, and started needing many more people to work in those factories, despite their productivity being boosted with the new technology. This massive increase in production also meant that there now needed to be other jobs that supported the production process, like maintenance, quality control, supply chain management, engineering, and much more.

The other thing this boost in car factory productivity did is create entirely new products and services. Since cars were much more popular and owned by a large segment of the population, there now needed to be roads, mechanics, gas stations, auto parts, motels, diners, and so many new things that were impossible to predict before this revolution took place. The world simply changed when cars became a part of daily life, and many of the jobs we have today simply wouldn’t be possible without the technological revolutions that made cars more easily producible.

This pattern has repeated itself endlessly in history. Every time humans introduced a technology that allowed us to produce much more output much more quickly, it led to more of that stuff and also new stuff, which led to…more jobs in ways we could never exactly predict in advance.

Lump of labor fallacy

We have this implicit belief that there exists a fixed amount of work in the economy. In other words, we think that if workers get access to a tool or machine that does half their work, that they’ll only have half as much work to do. In reality, this is not how the economy, or working in general, works. What actually tends to happen when we no longer have to do a portion of work that we usually do, is that other work actually expands to take up the rest of our time.

When ATMs were introduced, they could automatically perform most of the work that human bank tellers did, and many people thought that there would be no more bank tellers in a few years. However, this did not play out as expected. ATMs started to increase the demand that banks faced, because it was now easy to perform most transactions quickly. But since there were still a few things that only humans could do, humans were still needed in banks. So now, more people were visiting banks, and things like complex transactions, identity verification, and financial advice were still needed, which caused human bank tellers to become more valuable! The amount of bank tellers actually increased after ATMs were introduced! Even though a technology could now do most of what they did before, the few things that it could not do, were how they provided value in the new economy, and these things expanded to fill their time.

This isn’t a cherry-picked example to prove my point. Here’s another example.

Bookkeeping and accounting was a slow and manual process for most of history. There were a few bookkeepers that dealt with large amounts of data and it was common to make errors. After tools like computers and spreadsheets were introduced, the job of accountants shifted to become “managers” of these tools, rather than doing the long calculations themselves. They now spent most of their time planning and using technology to perform their jobs. This actually increased the amount of accountants that businesses hired, because it became easier to deal with large volumes of data more quickly, and these professions started providing more value to the economy. It also made the barrier to entry to these fields much lower, and many more people were able to contribute to this field simply by being able to use the tools correctly. Even as accounting tools have become more powerful and more user-friendly, the amount of accountants and financial analysts has grown consistently over the past few decades.

This pattern has also endlessly repeated itself in history. If we gain access to a tool that can perform 50% of the labor we currently do, it’s not true that we will just have nothing to do for the other half of our time. Other work, which we may or may not have already been doing, will expand to fill up the rest of our time.

Back to the question - how will AI impact our jobs?

So, with these ideas in mind, let’s return to the big question: what’s actually going to happen to our jobs once these AI agents are deployed?

I outline three scenarios below that could describe our near future. The goal here isn’t to predict exactly what will happen, but to look at the next few years with more clarity and “higher resolution.” These scenarios are based on my research and reflections over the past several weeks, but of course, I could be wrong.

It’s worth noting that these scenarios aren’t mutually exclusive. One of them might fully play out, or we could see all three happening to some extent at the same time. The latter seems more likely to me.

Scenario 1: More productivity will lead to…more work for us to do

As we have seen, there are many times in history where making workers and organizations more productive actually leads to more workers being needed due to an increase in demand for the products they are creating. As we saw with accountants becoming more in-demand and car factories needing more workers due to their increases in output, AI will have a similar effect on some industries.

Humans with access to remote AI coworkers in their jobs will be able to produce orders of magnitude more output than human workers without this technology. This will also lead to companies starting to increase their output by orders of magnitude.

What would happen if a software game company is suddenly able to create many more games, add more features, and pursue international markets that they weren’t able to before? It would lead to much more demand for their games and their software. And this would require more labor to make those games. Even if humans are only required for a portion of that labor, and AI takes care of the rest, humans will still be needed to fulfill that excess demand.

As we described in the lump of labor fallacy, just because we are not doing the core work ourselves most of the time, it does not mean we will have nothing to do. Rather, we will start providing value through all of the other things we can do that AI won’t be able to do (yet).

This increase in output from companies will lead to more demand for workers in many of these companies.

In this scenario, if you are a worker using these AI agents, the actual job that you are doing will start to drastically change. If you are an engineer, you will no longer be writing most of the code, but rather directing a group of AI agents to produce the correct code. If you are a technical writer or a marketing associate, you will no longer be writing the copy, but reviewing and editing the copy produced by AI agents. We will become “managers” of agents that do the core work for us.

If you’re a bit skeptical about this argument, that’s understandable. It is especially difficult to envision how junior workers or college graduates will be able to provide value in certain cognitive jobs after AI agents can perform their work faster and cheaper than them. This is where Scenario 2 and 3 might be the more accurate ways of looking at the future.

Scenario 2: AI will shift our work to entirely new industries

As we described before, when a new technology enters the economy, it not only increases the productivity of one sector, but also creates entirely new sectors.

The internet revolution created web designers, SEO specialists, social media influencers, and other types of roles that no one could have predicted in advance. The AI revolution will similarly create a plethora of new industries and job types that we will not be able to predict. And even if only some of this work requires humans to perform it, then new jobs will be created.

This is basically the “AI might kill some jobs but it will also create new jobs so we’ll be fine” argument, but laid out in a bit more detail. One of the big questions I always had when I heard this was…well, what exact jobs could it create? Wouldn’t AI be better than humans at doing those new jobs too?

It is impossible to predict specifically what these roles will be, but I’ve explored some possibilities here. I’ve tried to include roles that I think could capture a significant portion of the labor force, not just small roles here and there. It seems likely that humans, not AI, will be needed for these new jobs below for a few years to come.

AI Implementation Specialists for Small & Mid-Sized Businesses

Similar to how web design services exploded after the creation of the internet, it seems likely that as AI becomes a part of our daily lives, most businesses will need help adapting to this new age.

AI Regulation & Compliance Consultants

There are many industries that are incredibly regulated, and will need to navigate the world of AI carefully. Basically every business will need help ensuring that the way they are using AI tools isn’t breaking any laws. Questions like “can I use an agent to handle customer data?” and “can I have an agent reply to my patients for me?” will become common. Since these are new and nuanced situations, humans will be needed to resolve them case-by-case.

AI monitors and trainers

We mentioned earlier that AI will not be able to fully understand human preferences without constant intervention. Small mistakes in their understanding, if not caught early, could lead to disasters, and companies will want to ensure that humans are monitoring the critical parts of their business that are using these AI agents.

As we hinted at the end of Scenario 1, it is inevitable that some workers will no longer be needed to continue doing the work they are currently doing. In this scenario, they will start transitioning to new forms of work. Software engineers will start to become AI monitors, paralegals and junior attorneys might start helping companies transition transition to use AI tools, and each cognitive worker will eventually start using different skillsets to provide value to this new economy. Two of the main ways that humans will provide value in this world is by:

1. Helping humans transition to start using AI
2. Helping AI understand what humans actually want

This transition will be messy. It will look different for every single worker. Your particular background, skillset, and preferences will determine how your transition plays out. In some ways, this transition has already begun, however it will really start to build up steam in the next year or two.

These transitions have occurred many times in history before. Agriculture used to employ most of the US population, but now accounts for only 2%. This did not lead to massive unemployment, but a shift to different kinds of work created by the industrial revolution. A similar shift occurred in the digital revolution. It would be unreasonable to expect that at least some of this will not play out when the AI revolution hits.

Scenario 3: Structural unemployment

Despite all this, the fact remains that many jobs that humans do today will simply not be required after remote AI coworkers are introduced. Junior and new-grad white collar workers will be first on the chopping block in certain industries once this technology is deployed.

The lump of labor fallacy is irrelevant if the technology replaces all the things a worker can offer. After elevators became automatic, human elevator operators simply were not required. As these agents become more intelligent and cheap, it will become difficult to justify hiring humans to do much of the technical work we do today.

Some workers will also be fully replaced because some industries simply cannot scale their output to match the increased output that AI provides (i.e. scenario 1 does not apply to them). For example, businesses that are limited by physical constraints cannot just “create more output” if AI makes them more productive. If a paint store can use an AI to maintain its website, it will no longer hire a human for that job. This applies to many other service-based businesses as well.

The demand for some white collar workers will likely start to dwindle within the next 1-2 years if this scenario plays out. In some companies, it has already started to happen. Salesforce has already stopped hiring all software engineers.

There is a real risk that this could lead to structural unemployment in developed economies very soon. If a large proportion of these workers that are laid off cannot find work again, the secondary and tertiary impacts of this could prove destabilizing for our society, as discussed in the final section of this article.

The difference between this technological revolution and previous ones was that previous ones happened over the course of decades and centuries. This gave populations time to adapt and re-skill to the new jobs that are required. This revolution is happening within a matter of a few years. The re-skilling required will be on a timeline we have never encountered before.

The question remains to be seen whether enough of the workers that are affected by these layoffs will be able to find employment due to the new sectors that AI will create (scenario 2) or through their ability to contribute to the increases in output created by AI (scenario 1).

Takeaways

Whatever scenario will play out over the coming years, it’s clear that the nature of the job market is about to change drastically. If you are a white collar worker today, your job is probably going to change in some way within the next 2-5 years. In all 3 scenarios, there is no way most software engineers can go on software engineering like normal until 2030. There is no way a cognitive worker can continue doing what they do normally until 2030. It’s true that some people and businesses will simply be slow to adopt these technologies; this is addressed in the section below. But even with these delays, the odds that your job is not affected by 2030 is slim.

The other major takeaway is that there is a significant chance that Scenario 3 (structural unemployment) plays out for a large segment of the population. That is dangerous news. If this is the case, it will obviously create widespread suffering, but it will be compounded by the political impacts that it will create, described at the end of this article.

Lastly, I want to note that much of this analysis is only focusing on the next 2-5 years. The bigger story is perhaps what AI will be able to do beyond these 5 years. The pool of things that humans can do will shrink more and more, and eventually, there will be no need for human “jobs”, the way we currently understand them. At this point, a new design will be needed to organize society; one we have yet to come up with. These larger concerns about the impacts of super-intelligent AI on society are discussed in this article I wrote in 2023.

I chose only to focus on the near future in this article, because we are almost in this future, and it seems like we still don’t know what it will look like.

Things that will delay AI adoption

Some things will obviously delay the adoption of this technology for many industries. Some people’s jobs will continue as they are today for years into the future without major disruption, simply because of the following factors.

Trust
Many companies and individuals simply don’t trust AI enough yet to hand over important tasks. Whether it’s fear of AI making mistakes, concerns about data security, or just that “gut feeling” that a human should double-check everything, a lack of trust will be a big factor in how quickly we transition to this new age. Industries like healthcare, finance, and legal services tend to be extra cautious, because a single error can have serious consequences. Building that trust often requires reliable performance over time and clear proof that AI can meet (or exceed) human standards of accuracy.

Regulation Is Slow to Adapt
The legal and regulatory landscape around AI is updating very slowly. Some industries, such as healthcare or legal services, may be stuck waiting for specific guidelines or approvals before they can fully adopt AI solutions. Until those regulations are in place, adoption will be stalled.

People Are Slow to Adapt!
Even when the technology is ready to go, people aren’t always quick to change their ways. Companies will need to retrain staff, restructure workflows, and overcome internal resistance to new processes. And on an individual level, many people are simply more comfortable sticking to what they know—especially if they’ve been doing it a certain way for a long time. Change can be intimidating, and a sizable chunk of the workforce will delay embracing AI until they absolutely have to.

Even with all these delays, this transformation will start taking place faster than most people are prepared for. Especially those living in developed countries and working in competitive markets, they will start feeling the effects of this technology very soon.

Political impacts

This is the other big takeaway, but I figured it deserved its own section.

There will come a moment in the next 2-5 years—maybe it happens all at once, or maybe it creeps up on us over a few months—when AI’s impact on jobs will turn into a full-blown political crisis. It might happen when we have the “oh shit” moment when people see how powerful this technology is, or it might hit when the layoffs and jobs transformations actually start to occur. Whenever it happens, it’s going to slam into an unprepared political system.

Governments, by nature, move slowly. We could end up seeing a situation where tens of thousands of jobs start evaporating (or at least radically changing) almost overnight, and lawmakers scramble to come up with quick fixes—maybe new training programs, some kind of universal basic income pilot, or reactive regulations that try to limit AI usage in certain industries. The problem is that right now, almost nobody in office is seriously anticipating that any of this is going to happen in the next year or two. So we will end up with knee-jerk policies, rushed laws, and half-baked ideas getting tossed around in the heat of the crisis.

Beyond policy, there’s also the bigger cultural and social fallout: if people lose their jobs in large numbers and see no immediate path to retraining, you can bet that’ll shape national elections, possibly propelling a new wave of politicians who promise to “protect jobs” by cracking down on AI. We know how this story goes. We have seen it before. Tech-Fuelled Inequality Could Catalyze Populism 2.0

In the 21st century we might witness the creation of a massive new unworking class: people devoid of any economic, political or even artistic value, who contribute nothing to the prosperity, power and glory of society. This “useless class” will not merely be unemployed — it will be unemployable.

Yuval Noah Harari

What we do in these first few years of AI disruption could shape the political climate for an entire generation.