Katarzyna Liwarska-Fulczyk

PhD | AI researcher & practitioner | AI Governance & Responsible AI | Human-centric & trustworthy AI | Future of Work | Mountain freak 🏔️

[Erik Brynjolfsson @erikbryn]

Here's our statement on AI and the economy.

We Must Act Now
A Statement on AI’s Transformation of the Economy

1. AI may become radically more powerful over the next 10 years.
2. This could drive an unprecedented transformation of our economy, larger than the Industrial Revolution, but unfolding over a vastly shorter time frame. It could bring risks, including large-scale job displacement, as well as opportunities such as major gains in living standards.
3. Economists, policymakers and technology leaders must act now to understand the economics of transformative AI and to build the incentives, guardrails, and institutions needed to steer AI in a direction that complements humans and benefits society.

[Andrew Ng @AndrewYNg]

There will be no AI jobpocalypse.

The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it.

I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines.

Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%.

Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable!

Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more.

Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus.

To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market.

Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades.

Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have).

Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future!

[Original text in The Batch newsletter.]

[MIT Sloan Management Review @mitsmr]

Responsible AI is not just about what a system does — but what it does to people." MIT SMR + BCG's expert panel weighs in. https://t.co/huyh4cNmBc

[Harvard Business Review @HarvardBiz]

AI adoption strategies are overwhelmingly framed around productivity and efficiency. But that lens misses a critical constraint: the psychological cost of working with AI. New research shows that “psychological debt”—a cluster of six negative effects including cognitive offloading, reduced autonomy, diminished competence, weakened social connection, credibility loss, and identity threat—can materially suppress adoption and erode ROI.

In a survey of 1,200 employees across sectors, higher psychological debt was strongly associated with lower AI usage, less sophisticated application, and greater avoidance—even when employees acknowledged AI’s value. Early-career workers were especially affected, suggesting that AI may be undermining skill development at precisely the stage when it matters most. https://t.co/oyU9uRFUJX

[Sinan Aral @sinanaral]

🚨Breaking: New Paper on "AI Skills Erosion"🚨
We just released "The AI Augmentation Trap" which asks: with evidence mounting that AI erodes skills, what are the long- and short-run implications of this AI skills erosion for workers and firms?

Our dynamic model, based on differences between worker and manager incentives, produces several important results:

[Anthropic @AnthropicAI]

Introducing the Anthropic Science Blog.

Increasing the pace of scientific progress is a core part of Anthropic’s mission. The Science Blog will feature new research and stories of how scientists are using AI to accelerate their work.

Read the intro: https://t.co/1P9BDyX3xG

[Klaus Schwab @ProfKlausSchwab]

What will universities look like in the age of artificial intelligence?

We are entering a transformation that reaches far beyond educational reform. The Intelligent Age, shaped by artificial intelligence, exponential technologies, and deep societal change, questions the very purpose of universities, the role of professors, and the expectations placed on students. It challenges not only how we teach but why we educate.

Higher education stands at a decisive moment. Universities can help societies master the Intelligent Age, or risk losing relevance and public trust. What is at stake is not only employability or institutional prestige but the capacity of societies to preserve truth, cultivate judgment, and sustain human and democratic values in a technology-driven world.

The book 'Universities, Professors and Students in the Intelligent Age' is written from a clear conviction: incremental adjustments are no longer sufficient. Artificial intelligence is transforming how knowledge is created, validated, and shared. Universities must therefore rethink their mission, moving beyond episodic education toward lifelong learning, interdisciplinary integration, and responsibility toward society.

The perspective offered here is shaped by my own path as a professor and by decades of close engagement with universities worldwide, as well as by my work in building and leading global institutions. It also draws on my reflections in 'Restoring Truth and Trust in the Intelligent Age', arguing that technological progress must be accompanied by a renewed commitment to truth, responsibility, and institutional integrity.

Yet the central challenge of the Intelligent Age is not technological; it is human. Our ability to compute and optimize has advanced faster than our ability to contextualize, exercise judgment, and act responsibly. Professors must increasingly serve as mentors and ethical guides; students must become active co-creators of their learning; universities must reclaim their role as institutions of wisdom and public purpose.

The book speaks not only to academic leaders, professors, and students but also to parents, policymakers, business leaders, and citizens. The future of innovation, social cohesion, and democratic resilience depends on how higher education evolves.

‘Universities, Professors and Students in the Intelligent Age’ is available from all major online booksellers: ➡️🔗https://t.co/fCPZuzxfRI
#IntelligentAge

[Anthropic @AnthropicAI]

We invited Claude users to share how they use AI, what they dream it could make possible, and what they fear it might do.

Nearly 81,000 people responded in one week—the largest qualitative study of its kind.

Read more: https://t.co/tmp2RnZxRm

[Piotr Sankowski @piotrsankowski]

Sztuczna inteligencja zaczęła mówić jak człowiek w 2022 roku, a od tego czasu my zaczęliśmy w związku z tym mówić jak sztuczna inteligencja. Choć zmiany na razie są niewielkie, to jednak wyraźnie obserwujemy właśnie zachodzący "reverse alignment" tzn. zjawisko socjologiczne gdzie ludzie zaczynają dopasowywać swoje zachowania do maszyn.

Badania naukowe przeprowadzane w ostatnich latach wyraźnie pokazują, że modele językowe (LLM) przestały być tylko narzędziami do generowania tekstu, ale zaczęły także wpływać na to, jak ludzie się komunikują. Nie jest to tylko obserwowane wyłącznie w pisaniu artykułów naukowych, ale zmiany widać w naszej spontanicznej mowie, tzn. na wykładach akademickich i w luźnych dyskusjach w podcastach.

- W pracy https://t.co/5WgXaQj2Kc autorzy przeanalizowali transkrypcje z ponad 280 000 filmów naukowych na YouTube. Autorzy zaobserwowali statystycznie istotny, wzrost użycia w mowie słów-markerów, takich jak delve, meticulous, realm czy intricate, po debiucie ChatGPT pod koniec 2022.

- Dalej w tych analizach idzie praca https://t.co/QscgMbgxhM, gdzie autorzy przyjrzeli się korpusowi zbudowanemu na podstawie nieskryptowanych podcastów technologicznych i naukowych zawierającemu 22 milionów słów. Badanie to pokazuje, że słownictwo kojarzone z AI przesącza się także do naszych swobodnych rozmów.

- O ile w mowie ten wpływ jest na razie dość subtelny, to w pismach naukowych mamy do czynienia już z bardzo silnymi zmianami, które pewnie wynika z tego, że wiele tekstów naukowych zaczęło być pisane z duża pomocą modeli językowych. Badanie https://t.co/OpvyScRXZU bazujące na danych z PubMed, Scopus i Web of Science pokazuje wręcz wykładniczy wzrost użycia, słów, ale także zwrotów, które można przypisać LLMom. Zmienia się nie tylko słownictwo, ale i cała składnia. Tworzą się klastry frazeologiczne, tzn. niektóre słowa używane są razem bardzo często. Dla przykładu, korelacja między jednoczesnym użyciem w artykule słów underscore i pivotal skoczyła z 0.032 w 2022 roku do poziomu 0.449 w roku 2024.

Mimo, że wyniki dla tekstów pisanych są bardzo wyraźne, to jednak dużo bardziej zatrważające są zmiany w języku mówionym. Widać, że obcując na co dzień z dialektem LLM-ów zaczynamy go naśladować. Nasz własny język upodabnia się do maszynowego. Skoro modele językowe zmieniają nasz sposób wyrażania myśli w czasie rzeczywistym, to rodzi to pytania etyczne o to w jakim stopniu też te modele zaczną ostatecznie kształtować nasze normy społeczne i czy przekonania?

[Bartosz Naskręcki @nasqret]

MathBot Discord Server is growing!

https://t.co/AkwdDVVEyr

Come visit our Discord community. We have bot arenas and discussions on the formalization and enhancement of mathematics with AI. We focus on ambitious projects and explore the bleeding edge of AI × Maths.

[Katarzyna Liwarska-Fulczyk @FulczykK]

@KinasRemek Bardzo dobra synteza i świetnie to się czyta 👏 Bardzo się cieszę, że wystartowal Pan ze swoim blogiem - jestem jego stałą czytelniczką. Czekam na kolejne treści 🙂

[Katarzyna Liwarska-Fulczyk @FulczykK]

@KinasRemek @SebKondracki @bielikllm @ChrisOciepa Dziękuję i ja również życzę wszystkiego najlepszego w Nowym Roku !

[Katarzyna Liwarska-Fulczyk @FulczykK]

@RadioNaukowe I ja dołączam do zachwytów nad ostatnim odcinkiem, tj. o Bitwie Warszawskiej - słuchało się go z zapartym tchem. Zresztą inne odcinki też są niezwykle ciekawe i trzymają wysoki poziom 🙂

[Katarzyna Liwarska-Fulczyk @FulczykK]

@kawecki_maciej @emmm_more Panie Macieju, nie pomylił się Pan. Jest to III LO we Wrocławiu: https://t.co/AQShkOOPus

[Erik Brynjolfsson @erikbryn]

Some tasks are painful to do.
But some are fulfilling and fun.

How do they line up with the tasks that AI agents are set to automate?

Not that well, based on our new paper "Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce"

1/2

[Stanford HAI @StanfordHAI]

📢 New policy brief: AI agents that can simulate human behaviors and attitudes can help test ideas in social science. Our latest brief introduces a generative AI agent architecture that simulates the attitudes of 1,000+ real people. Learn more: https://t.co/CIRTGbNzSX

[Ajay Agrawal @professor_ajay]

Even the most sophisticated, modern AI systems are still just computational statistics that generate predictions. @rotmanschool @creativedlab

[Stanford HAI @StanfordHAI]

Before you ask your favorite AI chatbot to summarize the #AIIndex2025 434-page report, read first this 10-chart overview written by a human: https://t.co/mmq0qsKZ50

[Katarzyna Liwarska-Fulczyk @FulczykK]

@KinasRemek Polecam Gamma 🙂