What Happens When Capitalism Doesn't Need Workers Anymore?

0:00 Everybody has some level of anxiety over what our AI future will look like.

0:04 Somewhere between Skynet and a post-guest at Utopia,

0:06 the most immediate concern for most people is that this technology

0:10 will end up doing their job better than they can.

0:12 So far one side of the argument points out

0:14 that big new technologies in the past have only

0:16 ever made economies wealthier and whatever jobs they replace

0:19 they end up making more better jobs somewhere else.

0:22 The other side argues that yeah sure when we

0:24 replaced our muscles with machinery in the past it

0:27 let us leverage our minds which are clearly what

0:29 humans have invested most of our evolutionary traits into.

0:32 But if machines replace that what else do we have left to offer?

0:36 Now nobody can predict the future least of all

0:39 economists but we don't really need to because there

0:42 are certain economies that are going to see

0:44 the widespread impacts of these changes well before most others.

0:46 In fact they kind of already are.

0:49 In places like the Philippines and Bangladesh

0:51 the threat of AI is much more imminent.

0:53 The threat to jobs to entire industries

0:55 and the economic growth they've spent decades building.

0:57 These economies have spent the last 30

0:59 years constructing entire industries around outsourced service work.

1:03 Things like call centers, data entry, transcription and basic software support.

1:07 These jobs were once considered safe

1:09 from automation because they required language skills,

1:12 context and that special human touch that machines just couldn't replace.

1:15 Well it turns out machines got a lot better at replicating that human touch.

1:19 Tools like LLMs can now handle those tasks in seconds

1:22 at a fraction of the cost and these jobs which make

1:24 up a big share of GDP in many developing countries

1:26 are looking like they might be the first dominoes to fall.

1:29 In the Philippines the IMF estimates that a staggering 89% of outsourced

1:33 service jobs are at higher risk of being automated by AI.

1:35 That's over a million people whose jobs could disappear in just a few years.

1:39 In other words AI is already making the world's richest countries even

1:42 richer and is making it harder for everybody else to catch up.

1:46 And that's just the beginning of the story.

1:48 Even in rich countries AI is starting to divide the economy into those who

1:51 can leverage it and those who are going to get replaced by it.

1:54 The US Bureau of Labor Statistics

1:55 predicts that roles like cashiers, bank tellers,

1:58 postal staff and customer service representatives are all on track to shrink.

2:01 One estimate suggests 7.1 million jobs could disappear in the next five years

2:05 with up to 47% of current roles at risk of being replaced by AI.

2:08 Of course it's also worth remembering

2:10 that companies and their investors have now plowed

2:12 trillions of dollars into developing this technology

2:15 so they want to eventually see a return.

2:17 As heartless as it is cutting millions of workers off payroll

2:20 is probably the most immediate way to start seeing those returns.

2:23 So there is an incentive to play out the scare campaign because what

2:27 sounds horrifying to most people sounds like

2:29 opportunity to those actually writing the checks.

2:31 But even still the trend lines are clear.

2:33 AI is already reshaping who gets ahead,

2:36 who falls behind and most importantly how fast the gap is widening.

2:39 So as always we've got some important questions to answer.

2:43 Why is AI super charging growth in rich

2:45 countries while simultaneously threatening the economic survival of others?

2:48 In a world where one person armed with AI

2:50 can replace five people what exactly happens to the other

2:53 four and perhaps most importantly can workers or even

2:56 entire economies adapt fast enough to survive the shift?

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3:47 According to the Centre for Economic Policy Research the US could see

3:51 a 5.4% booster GDP over the next decade thanks to AI-driven productivity gains.

3:55 The UK, Germany and South Korea aren't far behind with similar projections.

3:59 Meanwhile lower income countries are looking

4:01 at much more modest gains closer to 2.7 to 3.5% which would be a departure

4:05 from the expectations about developing countries, well developing faster.

4:09 The Philippines is a good example.

4:11 For years it's been one of the world's

4:14 premier destinations for business process outsourcing,

4:15 a $37 billion industry that includes customer service,

4:18 billing, transcription and tech support.

4:20 The tech sector employs more than 1.3 million people

4:22 and contributes over 7% of the country's total GDP.

4:25 But here's the economic nightmare scenario.

4:27 Most of these jobs are exactly the kind of repetitive,

4:30 tech-based tasks that large-language models like

4:33 ChatGBT are rapidly learning to automate.

4:35 Jobs in the Philippines are at high risk

4:37 of being replaced by AI and it's already happening.

4:40 Roughly two thirds of outsourcing companies in the country are

4:42 now using AI tools to cut costs and speed up workflows.

4:45 Major US companies like AT&T,

4:47 Google and Accenture outsource work to the Philippines but if AI can

4:51 perform the same task faster and cheaper and without requiring health insurance,

4:54 vacation days or human resource departments,

4:56 those jobs will be amongst the first casualties.

4:58 Bangladesh is in a similar boat.

5:00 Its outsourcing sector has grown to 400 firms employing over 80,000 people

5:04 but the vast majority of that work still centres around customer service,

5:08 transcription and data entry, which again is precisely the kind of job

5:11 that AI is becoming increasingly capable of automating.

5:13 If AI can deliver the same quality of work

5:16 at a better speed for significantly less money,

5:18 there's simply no compelling economic reason to continue outsourcing.

5:21 A single slot in a server rack could soon replace

5:23 an entire core centre in Manila or Dakar and that means

5:26 companies could start to re-sure bringing jobs back to wealthy

5:29 nations where local automation can rival offshore labour on price.

5:32 That completely flips the script

5:33 on the entire outsourcing model that emerging economies

5:35 have built their entire growth strategies around

5:37 for the past three decades and it's

5:39 a big reason why the gap between wealthy and poorer nations is set

5:42 to widen after a few decades of these economies actually slowly catching up.

5:46 AI also rewards exactly the kind of specialised

5:48 skills that are hardest to scale globally.

5:50 Building and training large AI models requires advanced education,

5:53 reliable municipal infrastructure and access to advanced technologies.

5:56 Those resources are overwhelmingly concentrated in wealthy nations.

5:59 That means the most valuable AI jobs are

6:02 also the least accessible to workers in emerging markets

6:04 and while workers in those countries do manage

6:06 to gain access to those highly sought after skills,

6:08 they often don't stick around.

6:10 Talented engineers have been recruited by global tech

6:12 companies or relocating entirely to hubs like San Francisco,

6:15 London, Berlin or even centres within China.

6:17 The result is an accelerating brain drain

6:19 that leaves poorer nations with fewer start-ups,

6:21 fewer teachers and researchers and dramatically fewer chances to catch up

6:24 in the global AI race and it's clear which countries are leading that race.

6:28 In short, the countries least equipped to absorb

6:30 disruption are the ones getting hit first and hardest

6:32 while the country's best positioned to benefit from AI

6:35 are already pulling ahead because they control the capital,

6:37 infrastructure, talent and resources shaping the future of AI.

6:40 But AI isn't just dividing countries long economic lines,

6:44 it's also creating stark divisions between

6:46 the people within those same countries.

6:47 This technology is not impacting all people in the same way,

6:51 it's making some workers nearly obsolete while making others far more valuable.

6:55 That's because AI represents a very specific kind of capital

6:58 and understanding this distinction is

6:59 crucial for predicting its economic impact.

7:01 In the past, most new technologies

7:03 functioned as what economists call complementary

7:05 capital meaning these were machines and technologies

7:08 that made human workers more productive.

7:10 For example, a combined harvester didn't eliminate farm workers,

7:13 instead it made each individual worker dramatically more efficient.

7:16 Before mechanization,

7:17 harvesting a single field might require 20 people working for several days while

7:21 with a harvester one person could do the same job in a fraction of the time.

7:25 Labor and capital worked together and as productivity

7:27 increased so did wages and living standards.

7:29 Workers remained essential to the process,

7:30 they just became much more productive.

7:32 For many high skilled roles, AI will become more complementary capital

7:36 boosting productivity without replacing the human worker.

7:38 A financial analyst using AI to scan reports and spot anomalies

7:41 can get insights faster and can focus more time on strategic thinking.

7:44 A doctor leveraging AI for diagnostics can

7:46 spend more time on direct patient care.

7:48 In these cases, AI multiplies what skilled professionals can

7:51 do and makes their expertise more valuable in the marketplace.

7:54 But for more routine, process driven work,

7:56 AI increasingly acts as what economists call substitutive capital,

7:59 replacing human labor altogether instead of enhancing it.

8:02 An AI-powered chatbot doesn't make a customer

8:05 support agent faster, it replaces them.

8:07 A sophisticated co-generator doesn't assist

8:09 a junior developer, it replaces them.

8:11 In other words, the more capable our capital becomes,

8:14 the less it actually needs human labor to function.

8:16 And in the AI economy,

8:18 capital ownership is more concentrated than it ever has been in modern history.

8:21 Most of the major breakthroughs in artificial intelligence are coming

8:24 from a handful of elite firms in the US and China.

8:27 Since 2017, the US has produced 135 large scale AI systems.

8:31 China is not far behind with 110, but the gap widens quickly.

8:35 From there, the UK has managed 25 and France 24.

8:39 And the companies leading the charge with these breakthroughs are experiencing

8:41 exponential growth thanks to what is known as the data network effect.

8:45 The more data they collect,

8:46 the better their AI model performs, the better their model,

8:49 the more users they attract,

8:50 and the more users they attract, the more data they generate.

8:52 This creates a powerful feedback loop where market power

8:55 and profits concentrate in just a few dominant companies.

8:58 PWC estimated that AI could add $15.7 trillion to global GDP by 2030,

9:02 but 70% of that wealth is projected to go to just two countries,

9:07 the USA and China, because they own AI.

9:10 In 2024 alone, over 1,100 US-based AI companies raised major funding rounds.

9:15 That's more than double all of Europe combined.

9:18 IBM and Microsoft alone hold thousands of AI-related patents,

9:21 giving them long-term control over everything

9:23 from enterprise tools to foundational models.

9:25 Smaller firms, even those in wealthy countries,

9:27 are becoming increasingly dependent on licensing tools

9:29 and models that they didn't build and don't control.

9:32 And that extends beyond software.

9:33 The physical machines that power AI, CPUs and GPUs are overwhelmingly designed

9:37 and manufactured in just five countries.

9:39 More than 90% of that hardware comes from the US,

9:42 Taiwan, China, South Korea and Japan,

9:44 and that means a tiny handful of countries don't just run AI systems,

9:47 but also manufacture the foundational components

9:49 that make AI possible in the first place.

9:52 That's the reality of AI as capital.

9:54 It primarily benefits those who already own the assets,

9:57 while replacing those who don't.

9:58 The more you can leverage AI as a productivity multiplier,

10:01 the more economically valuable you become in the marketplace.

10:04 But for workers in routine roles,

10:06 especially those without access to retraining programs,

10:08 the future is looking far less promising.

10:10 Now, even if you weren't aware of these exact figures,

10:13 they probably aren't surprising.

10:14 And that's exactly the point.

10:15 This is a reality that people are noticing.

10:18 Nearly one third of Americans in a recent survey said

10:20 they're fairly or very worried about losing their jobs to automation.

10:23 This isn't some hypothetical scenario we're speculating about.

10:26 We've witnessed similar disruptions before.

10:28 When industrial automation and large-scale outsourcing

10:30 ramped up in the 1980s and 1990s,

10:32 it hit manufacturing hard, especially in places like the US and Western Europe.

10:36 In America alone, more than 7 million

10:38 factory jobs disappeared between 1980 and 2010,

10:41 and most of them didn't come back.

10:43 These factory jobs may have been replacing US workers with Chinese workers,

10:46 but there is no critical reason why

10:48 human workers couldn't be replaced with clankers.

10:50 The Midwest bore the brunt of this economic transformation.

10:53 Cities like Detroit,

10:54 Cleveland and Youngstown were once packed with well-paying jobs in steel,

10:57 cars and textiles, but then came robotic welders,

10:59 computer-run assembly lines and cheaper labor overseas.

11:02 Suddenly, those stable middle-class jobs evaporated, factories closed,

11:05 unemployment spiked, and entire local economies started to fall apart.

11:09 The consequences extended far beyond simple job loss.

11:11 A lot of these towns saw life expectancy drop,

11:14 opioid addiction rise, and schools struggled to keep up.

11:16 The jobs that eventually did return often paid less and didn't

11:19 offer the stability or benefits

11:21 that had previously supported entire communities.

11:23 The UK experienced something similar.

11:25 Coal mining, shipbuilding and steel plants across Northern England

11:28 and Scotland shut down its automation and privatisation to coal.

11:31 Even today, places like Sheffield and Sunderland still lag behind the rest

11:34 of the country when it comes to income and social mobility.

11:37 The lesson is clear.

11:38 Even when the long-term picture improves,

11:40 the short-term impact of technological disruption can be devastating,

11:43 and once inequality takes root in an economy,

11:46 it becomes extremely difficult to reverse.

11:48 So, what can we actually do about this looming challenge?

11:51 Because at this point,

11:52 it's clear that AI is already transforming the global economy,

11:55 but whether it deepens existing inequality or helps us solve it depends

11:58 on the actions that countries and individuals take in the coming years.

12:02 First, the good news is, we can already see what's coming our way.

12:05 In lower-income countries like the Philippines and Bangladesh,

12:07 the front-line effects of AI are unfolding in real-time.

12:10 These economies show us which jobs go first,

12:12 where the risks the highest, and what happens when governments act or don't.

12:16 For example, the government of the Philippines has launched a national AI

12:19 strategy with the goal of retraining over a million workers by 2028.

12:22 Bangladesh, meanwhile,

12:23 has released a draft policy framework focused on developing AI talent,

12:26 modernising its education system and supporting tech startups.

12:29 The goal is to position Bangladesh as a competitive

12:32 player in the AI enabled services market,

12:34 while safeguarding jobs through upskilling digital inclusion programs.

12:38 Whether those efforts will prove sufficient remains to be seen,

12:40 but they offer a clear warning and a playbook for wealthier nations to follow.

12:44 Two distinct sides of our economies need to do two things simultaneously,

12:47 invest heavily into AI infrastructure and invest

12:49 just as heavily into their people.

12:51 This includes educational investments into computer science, yes,

12:53 but also the kind of skills AI struggles to automate,

12:56 critical thinking, complex problem solving,

12:58 effective communication and creative decision making.

13:01 A recent analysis of 12 million job postings

13:03 in the US found that AI adoption tends

13:05 to increase demand for these distinctly human skills

13:07 far more often than it eliminates jobs entirely.

13:10 Building an accessible digital economy is equally important because right now

13:13 nearly 2.6 billion people worldwide still don't have access to the internet.

13:17 Without that basic connectivity, there's simply no opportunity to compete

13:21 or even participate in the emerging AI economy.

13:23 The World Bank estimates that every 10% increase

13:25 in broadband access can boost GDP growth in developing

13:27 countries by up to 1.4% and that's before factoring

13:30 in the additional benefits that AI capabilities could provide.

13:33 So along with retraining, countries need policies that expand broadband access,

13:37 reduce the cost of devices and give more people

13:39 the digital skills they need to benefit from AI.

13:41 Social safety nets matter too.

13:43 They function as economic buffers that give displaced

13:45 workers the time and resources they need to adapt,

13:48 retrain and re-enter the labour market from a position of strength.

13:51 But if AI allows businesses to grow while workers lose their income,

13:54 the economy starts to hollow out.

13:56 Productivity rises, but consumption falls.

13:58 Innovation continues,

13:59 but inequality grows and it becomes a serious drag on overall economic growth.

14:02 If we want AI to boost productivity broadly, not just corporate profits,

14:06 we'll need to rethink how we design and share the value it

14:09 creates and that includes fundamental questions about who gets to build AI,

14:12 who governs its development and deployment and who

14:14 ultimately benefits from the massive productivity gains it generates.

14:16 If you want to see just how far this could go,

14:19 what happens if AI keeps getting better and most

14:21 people end up with nothing of value to trade?

14:23 We made an entire video about that thought experiment two years ago.

14:25 You should be able to click to that on your screen now.

14:28 Thanks for watching, mate.

14:30 Bye.