Just trying to kill boredom without killing anyone in the process | Anything unrelated to actual (super niche) area of expertise | Dubito ergo sum
Joined October 2021
- Tweets10.2K
- Following168
- Followers750
- Likes7.9K
Pinned Tweet
A. Distributed knowledge, discovery, and spontaneous order
Hayek, F. A. (1937). “Economics and Knowledge.” Economica*, 4(13), 33–54 doi.org/10.2307/2548786
I know exactly why I’ll never be a solo founder.
But also, one critical block is missing.
MTS-ification is flattening most categories of tech work
It's probably the most interesting and import idea to absorb and study to understand the future dynamics of the job market, and how to take advantage of them
TLDR is that titles used to describe "what we do", post-AI they describe "what we own". Depth is rentable, so passing work around costs more than doing the work itself. AI can fill in the "average" so what’s left to stand out is a combination of agency, judgement, and spike: the thing(s) you do better than almost anyone.
Invest in and become fluent with as many relevant AI tools (and techniques) as possible, be able to explain simply and clearly how you can use them to expand your capacity and span adjacent specialties complementing your main.
Emerging categories to position for (beyond MTS):
Growth staff (me): marketing / sales / partnerships / community / devrel
Operations staff: finance / people / legal ops / internal tools
Customer staff: support, customer success, account management, solutions engineering, implementation
In interviews and on resumes
1. Lead with your "spike", have ultra-crisp stories where it was the reason behind a great success
2. Have ultra-crisp stories exemplifying agency and good judgement that have driven some outcome end to end and the impact
3. Say “I,” not “we,” and be ready to describe exact details
There are big changes and things are being shaken up, with all disruption though there are always tons of opportunities, the people who move quickly and get in front of them the fastest will have the most upside. There are SO many new companies with crazy growth, pre-unicorns and new unicorns hiring like crazy, you want to be part of a company that will 10x or 100x over the next 10 years, not one that is about to have to reshape everything because they themselves are being disrupted.
Just leave it to Influencers who have already started positioning themselves with a rhetoric along the lines of "AI is going to destroy 25% of the jobs, sign-up here to learn how to keep yours"
The most locked-in team I backed as an investor includes a former F1 engineer and a former Americas cup engineer. It not just about pure technical skills - they would solve any problem for the sake of it -, it’s about being able to execute as a team
PITTI retweeted
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…
To illustrate this: given a list of choices, the models would often return a number corresponding to the position in the list (similar to levels for decision models now) so I had to sum probabilities for both actual labels and positions of labels.
Decision models initially put me in imposter syndrome territory because, for my own training library, I was just used masked language modeling for zero-shot classification (using ModernBERT after a paper by answerai a couple of years ago). But that basically it’s how it’s done
The answerai paper (@bclavie): arxiv.org/html/2502.03793v2
Since modernbert, I had tried it many different encoders. But I never thought about training a model for several questions at once lol
Also, it’s all about data, not architecture
I think it’s much deeper than this. Employees are very happy to give full access to basically everything and so they do. If you implement a bottleneck (e.g to monitor the data coming out - database queries, automatic forwarding) they’ll ask their agents to go around it
It’s impossible to keep pace ; AI enhanced employees move too fast. Worse, we gave them the illusion of automating although it’s just one-offs. Now they’d rather spend 3 man hours debating how to “automate” rather than doing something manually in 20 minutes.