Juan Mateos-Garcia

"Artificial Intelligence and the Labor Market"
Nice analysis of LinkedIn data to estimate the impact of AI on jobs between 2010 and 2023.

It illustrates the complexity of AI economic impacts: what exposure taketh away, complementarities and productivity giveth.
www.menakahampole.com

All this highlights the need to consider AI economic impacts holistically, and to create incentives and infrastructure to reproduce AI economic impact analyses regularly to track longer term impacts via improved capabilities and job redesign.

The results are consistent with their hypotheses: AI drives firm growth, occupations with high mean exposure to AI decline but this is often more than offset by complementarities and productivity growth. Big AI impacts with different signs lead to small aggregate changes.

They use LinkedIn job ads to measure firm-level AI adoption and occupation / task distribution, and embedding distance to ONET tasks to quantify exposure. They use instruments to reduce selection biases and mitigate mis-measurement (read the paper for full details)

In their model, AI displaces an occupation if its mean task exposure to AI is high, and augments it if its exposure dispersion is high (some tasks are exposed to AI and others untouched). They also consider impacts on labour demand though AI-powered firm growth.

"Artificial Intelligence and the Labor Market"
Nice analysis of LinkedIn data to estimate the impact of AI on jobs between 2010 and 2023.

It illustrates the complexity of AI economic impacts: what exposure taketh away, complementarities and productivity giveth.
www.menakahampole.com

Metascience and AI postdoctoral fellowship
Cool @sloanfoundation.bsky.social opportunity for anyone interested in studying AI x science adoption and impact. It is also very aligned with the evidence and experimentation recommendation in our recent essay!
sloan.org/programs/dig...

Intl Conf on the Science of Science & Innovation abstracts open! 👇👇👇

📢 𝗡𝗲𝘄 𝗽𝗮𝗽𝗲𝗿 alert! We document how 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗶𝘀 𝗿𝗲𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲𝗿 𝘀𝗸𝗶𝗹𝗹𝘀. 💼 💻

📊 We analysed 𝟯𝗠+ 𝗷𝗼𝗯 𝗽𝗼𝘀𝘁𝘀, used embeddings to cluster them into 𝟭𝟬𝟬+ 𝗳𝗶𝗻𝗲-𝗴𝗿𝗮𝗶𝗻𝗲𝗱 𝘀𝗸𝗶𝗹𝗹𝘀, and LLMs to classify them as s𝘂𝗯𝘀𝘁𝗶𝘁𝘂𝘁𝗮𝗯𝗹𝗲, 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗿𝘆, or 𝘂𝗻𝗮𝗳𝗳𝗲𝗰𝘁𝗲𝗱.

2. AI is being used to identify venom proteins that have antimicrobial properties. The haystack is the space of proteins present in venoms (40M!).

Both studies validate their results in the lab, highlighting complementarities between AI & experiments.

"Antivenom Renaissance"
Nice illustration of how AI can accelerate science by helping find needles in different types of haystacks.
1. AI is being used to design proteins that neutralise venoms. The haystack is the space of all possible protein designs.
blog.asimov.com/p/antivenom-...

This is a VC investment so it doesn’t make sense to frame as something that has to be repaid. If the investment is creates improvements in model performance that generate massive value in the future then it makes sense. That’s the expectation.

I guess those models are generating income now + you need to invest to go down the learning curve?

Hi Everyone!

We're hosting our Wharton AI and the Future of Work Conference on 5/21-22. Last year was a great event with some of the top papers on AI and work.

Paper submission deadline is 3/3. Come join us! Submit papers here: forms.gle/ozJ5xEaktXDE...

Could much of the recent drama about DeepSeek be rooted on flawed causal thinking i.e. neglecting the counterfactual? Dario Amodei makes a compelling case here.

darioamodei.com/on-deepseek-...

"Employment dynamics in a rapid decarbonization of the US power sector"

Cool complexity economics analysis of a scenario that sadly looks a bit sci-fi in the current political circumstances. It would be interesting to study labour market impacts of AI with this approach.

Employment dynamics in a rapid decarbonization of the US power sector We analyze the employment dynamics of a rapid decarbonization of the US power sector, reducing emissions by 95% before 2035. We couple an input-output…

This paper is a timely reminder that tech impacts on jobs are dynamic and complex - so we need dynamic and complex models and data to track / anticipate / respond to them!
Read it here: www.sciencedirect.com/science/arti...

They also forecast skill mismatches based on the network structure of job impacts e.g. whether a job’s neighbours see higher demand (bad for employers) or lower demand (bad for workers). This could inform timely policies to accelerate deployment and reduce disruption.

They find that the net impact of rapid decarbonisation on jobs is positive but depends on the stage of the deployment cycle. Many jobs see growth during the “scale up phase” (build up of low-carbon infrasructure) but decline afterwards.

They forecast the impact of energy tech adoption scenarios on output and labour demand in exposed sectors, and the implications for employers and workers based on impacts in related occupations.

"Employment dynamics in a rapid decarbonization of the US power sector"

Cool complexity economics analysis of a scenario that sadly looks a bit sci-fi in the current political circumstances. It would be interesting to study labour market impacts of AI with this approach.

Hello #EconSky, a simple question but one where I have not yet found the answer. What should I read to get a quantitative empirical understanding of the contemporary relationship, both size and timing, between scientific discovery and productivity growth?

"Suspense and surprise in technological innovation"
Neat analysis of the link between expectations and outcomes in technological evolution.

Suspense and surprise in the book of technology: Understanding innovation dynamics We envision future technologies through science fiction, strategic planning, or academic research. Yet, our expectations do not always match with what actually unfolds, much like navigating a story wh...

Some limitations: use of a top down coarse taxonomy; innovation isn't always link-creation; neglects hierarchical nature of innovation; patents offer a narrow view of societal expectations; expectations not evenly distributed...
Still really worth reading - check it here:
arxiv.org/abs/2412.08092

They analyse the distribution of suspense and surprise in technology codes within higher level technologies and characterise a technology lifecycle of surprise - suspense - popularity. This is what it looks like for AI. It would be interesting to drill down further!

They show that suspenseful innovations tend to receive more citations and be commercially valuable. They speculate that this could be because society is more prepared to adopt suspenseful innovations than surprising ones (this underscores the value of tech forecasting / DARPAs)

Using patents, they create a model to predict if two technologies will be connected. If their model predicts the connection before it happens it is suspenseful. If not, it is surprising.

"Suspense and surprise in technological innovation"
Neat analysis of the link between expectations and outcomes in technological evolution.

Charles Dickens on Victorian UBI (from Bleak House

Artefact