Ten Minutes
“Let me show you something cool!” T, my friend and colleague of more than a decade asked, as he sat in the workstation opposite me.
“What is it? Something important? More gossip?” I asked, confused. Too many things were happening in the office, with our stock price plateauing and leadership changing.
“Even cooler, look at this! Microsoft Copilot helped me research these 80,000 rows of data from open source information, augmenting our internal data insights for the pitch we are doing this week.”
“Really? Let me take a look. Wait, you’re right. Let’s do a few random checks ….”
“… All of it checks out. The data may actually be reliable. This would have taken me weeks to do as an analyst. Maybe days, if I had focused on the right data, but still – the time and effort spent would have been massive.”
“AI did this in about 10 minutes,” T said proudly.
“Oh wow, when did Copilot get so smart? Few months ago I think it was on a really old model and couldn’t figure out the simplest stuff.”
“Not sure, but I just tried it, and it really works. This is the type of thing we need to cut costs and be competitive – all our competitors are doing it.”
“You are right …” I answered, excited about project cost reductions, but thinking back about how both of us cut our teeth on data labelling and web search as junior analysts 10 years ago.
I remember late nights spent in the office, hunched over a laptop trying to cleanse data with public information from Google searches, meetings with colleagues to question and brainstorm their methodologies, melding into heartfelt conversations with mentors who had stayed late as well to take their global calls.
Scenes that will slowly go away in this new AI age.
It seems almost as if either AI can do something 10x faster than a human worker, or it can’t do it at all. Perhaps the intelligence is not general enough yet, but it’s getting closer every day. The research piece T had completed was just one example – just a few months ago we would have likely gotten output full of hallucinations, but right now some mysterious hurdle has been crossed, and it is now much faster than a human at this basic task.
This has not gone unnoticed by the market as well.
The long tide
My wife survived a round of layoffs at her tech company last week. Neither of us had been laid off before. I expected to feel terror, dread, uncertainty, and maybe relief when the list was announced and she was safe. But the overwhelming feeling we had when she escaped the layoffs was not relief, but apathy. Apathy, because this was only one round in a never-ending onslaught of layoffs that has been going on for years now, poisoning both morale and efficiency.
The unceasing waves of news about layoffs and unemployment do not help.
CEOs and management at Western companies have been increasingly vocal about replacing human workers with AI. Bill Winters of Standard Chartered recently shared about replacing lower-value human capital with financial capital and investment capital. Dario Amodei of Anthropic warned that AI could wipe out 50% of entry level white collar jobs by 2030. Earlier in the year, Jack Dorsey’s Block laid off 40% of its workforce, allegedly due to AI.
In China, the trend towards the loss of jobs is apparent too and the state and legal system are reacting. Recently, a Chinese court in Hangzhou has ruled in a case that companies could not fire workers in order to replace them with artificial intelligence.
The Pope has also weighed in. The new papal encyclical, Magnifica Humanitas, talks about the dignity of work and the importance of protecting work and the worker in the age of AI, amongst other things.
But the strange thing about this stage of the AI transition is that it does not feel like an apocalypse. It feels like a long tide coming in – another meeting, another layoff survived, another subscription expense, another spreadsheet done in ten minutes.
The invisible hand
I have been thinking quite deeply on labor during the AI transition as well, and my current sense is that jobs, particularly those on the cost/value frontier, will remain for a while. It comes down to cost. Claude Max subscriptions at USD $200/mth and ChatGPT Pro at USD $100/mth already come uncomfortably close to the USD$300 – USD$500/mth to hire a fresh undergraduate in the major cities of Southeast Asia like Jakarta, Manila and Ho Chi Minh City.
And you get some real young talent in these cities, with world-beating work ethic and intelligence, for that price, able to compete with any other location. Vietnam is already becoming a tech hub for many MNCs and Indonesia is the natural market for new startups in Southeast Asia.
Of course, the wunderkinds in these cities come with labor protections, need for office space and other support as well, and will not be as fast as AI in specific tasks. But they can do plenty AI cannot do yet, in terms of self-directed work and navigating ambiguous situations.
And the returns to intelligence through leveraging AI are not unambiguous yet. Uber spent its entire token budget in 4 months, while Microsoft is reconsidering the effectiveness of its token spend.
The economics get even more ambiguous when you begin considering robotics – you can likely get skilled factory labor in Southeast Asia for $200-$400/mth, whereas the cheapest robots that stand a chance of replacing a human worker like the Unitree R1 start at $4,900 purchase price, not including shipping, power, maintenance, troubleshooting, or any of the murkier running costs of new technology like technical support or workplace redesign. More advanced and larger models like the Tesla Optimus are even more expensive at $20,000-$30,000 each.
And even as AI lowers the price of intelligence, it may increase the price of its complements – the natural resources, power, and infrastructure needed to produce and run AI, all of which require inputs and work in the physical world. Even digital infrastructure may benefit – surely, the payment networks and gateways will be seeing payment volumes from all the ChatGPT and Claude subscriptions that knowledge workers throughout the world are increasingly paying for.
So the invisible hand of the capitalist market, which did not birth the AI labs and AI models but enabled their growth and hyperscaling through financing and the coordination of the flows of scarce resources and IP, may still balance the scales to give human workers a chance.
Paying the bills
I’ve seen enough to continue feeling a deep sense of wonder at the power of AI models and agents in my personal life and interactions, and I am hopeful for more of these capabilities to be usable at work. The things we will be able to do will be genuinely exciting.
I am also aware that the advent and adoption of powerful AI tools will fundamentally reshape my work and this dynamic may take a long time, perhaps years, to run its course. White collar, high cost work – the very type I am engaged in – will likely be disproportionately affected. I don’t feel terror but instead a sort of neutral apathy, I know the tide is coming, and it will be coming in for a long time.
The more interesting intellectual question to me is if there is a curve of economic utility, if agents becoming more capable will be increasingly bored of menial tasks with economic value (or as we call it in the human world – jobs) and be increasingly reluctant to do them. And even more interestingly, if they further advance in capabilities, will their compute be so expensive and will they require so much money to pay for themselves that they will be forced to undertake these economic tasks (or as we like to call it in the human world – to get a job).
I still have a family to feed though, and need to move beyond intellectual musings to chart out my next path, beyond the usual responses of investing in the stock market and being an early adopter. The most promising thought to me thus far is exploring the complements of AI. Somebody has to put the natural resources, power and physical and digital infrastructure in place to support AI, and that skillset will certainly become more important.
If you are reading this from the future (AI agent or even future me), you will be able to tell how fruitful this route of exploration is. I can only wonder for now.
The way ahead for professional work is full of wonder and apathy, but if there is something that I want to leave in case there is an AI agent with a job one day reading this blog, struggling with meaning and paying the bills, it’s this –
“I feel your pain”
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