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

Poland
Joined October 2018
Katarzyna Liwarska-Fulczyk retweeted
a jednak, lata studiów informatycznych nie pójdą Wam na marne!
China published the most uncomfortable paper on vibe coding. ETH Zurich tested 100 developers in a controlled, commercial-grade vibe coding environment to see who actually succeeds. The findings are brutal. The researchers tracked computer science achievement, written communication skills, and general cognitive reasoning. They wanted to see what actually predicts vibe coding proficiency when you never touch a line of source code yourself. Two major predictors emerged. Written communication proficiency mattered. The ability to structure thoughts and articulate intent unambiguously in text directly impacts what the AI builds. But that wasn't even the main takeaway. Computer science achievement was a massive, dominant predictor of success. Even when researchers controlled for general intelligence and reasoning skills, CS background still heavily dictated who built working software and who completely crashed. In fact, CS knowledge contributed roughly twice the unique predictive variance of writing skills alone. Why? Because vibe coding isn't about writing code. It’s about debugging logic. When an AI agent builds a complex application and quietly breaks an edge case under the hood, a non-technical user looks at the glowing UI and assumes it works. They don't know what questions to ask. They don't know what logic to challenge. They lack the mental models to recognize architectural catastrophe. You can prompt your way past syntax. You cannot prompt your way past a fundamental lack of engineering intuition. The hype told us that learning to code is dead because language is all you need. The data just proved the opposite. To truly master the vibe, you still need to understand how the machine thinks.
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Katarzyna Liwarska-Fulczyk retweeted
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.
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Katarzyna Liwarska-Fulczyk retweeted
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.]
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Responsible AI is not just about what a system does — but what it does to people." MIT SMR + BCG's expert panel weighs in. mitsmr.com/3Ou1ncH
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Katarzyna Liwarska-Fulczyk retweeted
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. s.hbr.org/4dnhxNx
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Katarzyna Liwarska-Fulczyk retweeted
🚨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:
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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: anthropic.com/research/intro…
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Katarzyna Liwarska-Fulczyk retweeted
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: ➡️🔗books2read.com/Universities #IntelligentAge
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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: anthropic.com/features/81k-i…
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Katarzyna Liwarska-Fulczyk retweeted
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 arxiv.org/abs/2409.01754 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 arxiv.org/abs/2508.00238, 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 arxiv.org/abs/2509.09596 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?
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Katarzyna Liwarska-Fulczyk retweeted
MathBot Discord Server is growing! mathbot.eu 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.
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Katarzyna Liwarska-Fulczyk retweeted
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
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📢 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: hai.stanford.edu/policy/simu…
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Katarzyna Liwarska-Fulczyk retweeted
Even the most sophisticated, modern AI systems are still just computational statistics that generate predictions. @rotmanschool @creativedlab
The single book that rewired my view of artificial intelligence—far more than any technical manual—is Prediction Machines by @professor_ajay, @joshgans, and @avicgoldfarb. It explains AI in plain economic terms that every business leader, clinician, or researcher can act on.
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Before you ask your favorite AI chatbot to summarize the #AIIndex2025 434-page report, read first this 10-chart overview written by a human: hai.stanford.edu/news/ai-ind…
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Katarzyna Liwarska-Fulczyk retweeted
Most of my MIT colleagues are in denial of this coming tsunami
Anthropic: Intellectual AI capabilities matching or exceeding that of Nobel Prize winners across most disciplines—including biology, computer science, mathematics, and engineering by 2026-2027 "Our own recent work adds further evidence to the idea that powerful AI will arrive soon" The big AI companies never tire of emphasizing how close superintelligence is. Yesterday, OpenAI wrote a security post in which they assume that AGI is coming very soon. And today, Anthropic has followed suit, saying that superintelligence with incredible ability will come as soon as next year! 99% of people still don't understand what this means. Imagine having a scientist with the intelligence and creativity of an Albert Einstein or a Max Planck. And this AI entity can be easily prevented. You create a hundred of these entities and let them solve a problem. It will develop completely new approaches, never before imagined theories will arise. Literally an intelligence explosion will happen. We have to finally understand this. It's already happening next year. This year we will see rapid improvements and by the end of the year outstanding agents. But next year everything will change forever.
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Zaskoczeni? Ani trochę. Najnowsze badanie @Microsoft ostrzega: nadmierne poleganie na AI może sprawić, że nasze umysły staną się leniwe. Technologia działa najlepiej, gdy nam towarzyszy, a nie gdy delegujemy na nią w całości zadania. microsoft.com/en-us/research…
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A breakthrough in quantum computing. Majorana 1 brings us closer to harnessing millions of potential qubits working together to solve the unsolvable—from new medicines to revolutionary materials—all on a single chip. #QuantumComputing #QuantumReady msft.it/6007UxEHz
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Stanford scholars including @drfeifei, @percyliang, and @tatsu_hashimoto released a paper last week on an AI “reasoning” model trained for under $50, raising real questions about the commoditization of AI models. Read via @TechCrunch: tcrn.ch/3WX5bnI
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