The architecture of AGI. Solving the human condition.
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Joined June 2020
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Machine Learning X Doing Incorporated retweeted
One of the most important challenges in science is learning to distinguish what we can observe from what actually explains what we observe.
The visible world is full of patterns. Understanding the mechanisms beneath them is a much harder (and potentially much more consequential) task.
Machine Learning X Doing Incorporated retweeted
A genuinely intelligent system should be able to engage with questions about alternative possibilities.
What would have happened otherwise? What changes if we intervene? Which outcomes are actually attributable to a particular decision?
The counterfactual is central to understanding the world, not merely describing it.
Machine Learning X Doing Incorporated retweeted
Prediction is important, but it is not the endpoint of intelligence.
In science, industry, engineering and everyday decision-making, we ultimately want to understand what causes outcomes and how those outcomes can be changed.
The frontier is to solve your world.
Machine Learning X Doing Incorporated retweeted
It's a new era.
It's not about economists as individuals. It never really was. It's about economic problems being solved, not just understood. How that happens is increasingly beside the point.
AI and other technologies will transform economic research. But the challenges facing developing countries aren't merely computational. They involve infrastructure, institutions, capital, production and implementation.
Development economics won't simply be displaced by technology. Its future, like that of computer science and mathematics before it, will increasingly be industrial in scope.
Academic economics isn't going anywhere. Cooking at home and eating at restaurants can coexist. So can academic and industrial research. And they will.
Part of what made economists like Milton Friedman influential was that their ideas were useful beyond a university. Of course, his communication skills, institutional networks, public visibility and intellectual contributions also mattered. But even he would probably approach things rather differently today.
The profession has an extraordinary opportunity ahead. It's a remarkable time to be an economist, even if the profession will never look quite the same.
It's time to join the adventure.
Machine Learning X Doing Incorporated retweeted
When I was lecturing at UC Berkeley a Korean student of mine told me about this Ghanaian young man.
Sam Okyere apparently went to Korea on a computer science scholarship and somehow ended up becoming an award-winning TV personality and celebrity there.
Machine Learning X Doing Incorporated retweeted
🏀♥️⚽️ cc @mlxdoing
Machine Learning X Doing Incorporated retweeted
A statistical overview of reinforcement learning: arxiv.org/abs/2610.41903
Machine Learning X Doing Incorporated retweeted
Great work, although much work remains to ensure that AI has a positive and significant economic impact across the board.
I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.
How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.
We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).
We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.
Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.
We are really excited about this paper. Excited to hear people’s thoughts.
Link: institute.deepmind.com/essay…
Machine Learning X Doing Incorporated retweeted
The future of work is here.
Flow.
Human ingenuity meets intelligence.
The future of flow.
#MachineLearningXDoing #AI
#FutureOfWork #Innovation
Machine Learning X Doing Incorporated retweeted
The best is yet to come.
This is one of the most important pieces of social science on human and AI interaction that I’ve seen in some time. Experts follow agentic advice the vast majority of the time (causally), but only a tiny fraction credit the agent—majority think they came to decision on their own volition.
Reminds me of the classic Nisbett and Wilson study on people following outside influence without realizing it
“ But as more work is handed to AI—including AI safety research—these judgment calls are the main levers for people's own values to influence outcomes.
If people defer to AI without noticing, then "human in the loop" might provide less assurance than people expect.”
Machine Learning X Doing Incorporated retweeted
The AI revolution should eventually render superficial intellectual rivalries of all kinds obsolete.
The fatal challenge to heterodox economics is that there isn't actually orthodox economics.
Just take a few recent Nobels: behavioral, information failure, institutions, bubbles, financial fragility, labor monopsony (sort of), Schumpeterian creative destruction, etc.
Not to mention RCTs which are emblematic of how much economic research is empirical and only lightly if at all structural.
The real difference is methods. Heterodox economics is an avowedly political project not a social scientific effort to better understand the world. And I have nothing against political projects just shouldn't confuse the two.
Machine Learning X Doing Incorporated retweeted
Great perspective.
Grit is a person's courage and strong determination. It is the inner strength that helps people keep working hard through failures, boredom, or obstacles instead of just giving up. Studies show that grit can be a stronger indicator of long-term achievement than raw talent or even IQ.
Psychologists define grit as a deep combination of sustained passion and long-term perseverance for meaningful goals.
Pedigree is overrated. Grit isn't.
Bloomberg on who he'd hire: "the person whose father never was there, whose mother was in a drug treatment program. And he had to go and work three shifts at McDonald's to take care of his siblings."
"No matter how bad things are — looks on the bright side, takes the bull by the horns, puts in the sweat capital. That's where the results are."
Machine Learning X Doing Incorporated retweeted
Giving toddlers the space to navigate challenges independently is a powerful gift.
In these quiet moments of struggle, they are building the critical strategy and focus needed to become lifelong problem solvers.
By stepping back, we allow them to develop true resilience and action-oriented confidence, ensuring they grow into individuals who actively shape solutions rather than just talking about them.
Machine Learning X Doing Incorporated retweeted
Inspiring.
Sports analytics is one of the clearest examples of how curiosity and quantitative skills can open doors that pure athleticism never will.
Professional teams, college programs, media companies, and sportsbooks all need people who can do exactly that.
The global sports analytics market is already several billion dollars and is projected to grow at double-digit rates through the early 2030s (various reports put it in the $4–6B range recently, heading toward $10B–$20B+).
NBA analytics staff alone grew from roughly 10 people league-wide in 2009 to well over 100 by the early 2020s; every major league and most college programs now have dedicated roles.
It’s not a guaranteed path, but it is certainly far more accessible than making a roster. The demand keeps rising as tracking data, wearables, and AI tools multiply the volume of information teams collect.
My son tried out for his high school basketball team three years in a row and got cut every time. He's a good kid, but he's five foot six and not very fast, and the third time it broke his heart. He'd practiced every single day that summer. He came home from the final cut list and went straight to his room. The next morning, the coach called our house and asked to talk to him. He said he had a problem.
Machine Learning X Doing Incorporated retweeted
AI debates for in-person interviews: Evidence from management consulting
As part of a new interview module, entry-level consultants at McKinsey must engage in a 10-minute conversation via laptop with an AI-generated client avatar. The avatar mimics realistic client personalities and tests how recruits think on their feet, present recommendations, and handle unexpected or difficult dialogue.
There is human oversight. The AI avatar does not score the candidate. Instead, partners and interview assessors sit physically in the room to observe and grade how well the applicant listens, adapts, and addresses the client's concerns.
The exercise aims to replicate about 80% of everyday scenarios a consultant might face, serving as a way to measure human-centered soft skills under pressure.
WSJ: wsj.com/lifestyle/careers/mc…
