The Latest News And Insights From The FutureTech Team.
The Trump administration hopes to enlist the U.S. tech industry in its AI race.
What does effective AI in the workplace actually look like? Not hype. Not fear. Research. In our latest blog, MIT Initiative on the Digital Economy (IDE) researchers break down what the evidence really shows about AI at work.
Trump’s ‘Tech Corps’ initiative is turning unpaid workers into Silicon Valley salespeople.
Find out with a free online calculator. The tool, developed by MIT researchers, predicts when a quantum system will be able to solve specific problems faster than a comparably priced classical computer.
MIT FutureTech Director Neil Thompson presented at the World Bank Headquarters in Washington, D.C. at the 4th Annual AI in Action Conference — Making AI Work: What Firms and Workers Need.
AI systems built or run with limited resources could soon perform on par with leading larger models while costing much less, according to new research. This could upend how companies build and use AI by boosting access and lowering barriers t
Time magazine releases an article titled "What the Numbers Show About AI’s Harms" citing the work of Simon Mylius as an affiliate researcher at MIT FutureTech.
Insights from our new paper on the scale-dependence of algorithmic progress.
At the IMF Workshop on Global Economic and Financial Implications of Artificial Intelligence
The emerging market for intelligence: the supply, demand, and usage of LLMs.
In this episode What does AI really mean for jobs, productivity, and the future of work? Recorded with a live audience at the MIT Museum!
What history can teach us about the AI job apocalypse.
An overview of some key insights from the 2025 FutureTech conference.
FutureTech's annual conference on AI automation achieved record attendance, bringing together leading experts and researchers.
FutureTech joins Substack to share research insights, analysis, and updates on AI and computing innovation.
FutureTech announces its annual conference: "How Fast will AI Automation Happen?" featuring leading researchers and policymakers.
FutureTech joins Substack as a Mixture of Experts, where MIT's expert minds can share ideas, data, and discoveries from the intersection of computing, AI, economics, and policy.
FutureTech Director Dr. Neil Thompson presents at the World Summit, sharing insights on AI and computing innovation.
The MIT AI Risk Repository releases comprehensive findings mapping the AI governance landscape.
"Negaflop" as a unit of measurement for algorithmic efficiency improvements.
Financial Times covers research by Dr. Neil Thompson and Dr. David Autor examining which jobs are safe from AI automation.
FutureTech announces joint research collaboration with Carnegie Mellon University on labor implications of AI.
The Bipartisan House Task Force on Artificial Intelligence cited FutureTech research in their policy recommendations.
At the recent MIT FutureTech Workshop on the Role of AI in Science, leading researchers, technologists, and policymakers gathered to explore how AI is rapidly transforming scientific process.
Forbes explores the convergence of AI and quantum computing, featuring MIT FutureTech's Quantum Economic Advantage Calculator at the Business of Quantum Summit.
Ana Trišović, a research scientist at MIT CSAIL's FutureTech lab, reflects on how MIT OpenCourseWare shaped her career path from Serbia to MIT.
FutureTech research and operations featured at Nvidia GTC conference, highlighting our work in AI and computing innovation.
In this article, we provide a high-level overview of another key trend in AI models: that progress in hardware underpins further improvements in AI systems. This is the third in a series of articles on AI trends.
MIT FutureTech hosts a workshop on the Economics of AI and Automation. The workshop is invite-only.
The FutureTech workshop on The Role of AI in Science brings together scientists from different fields to discuss the role of AI in Science. The workshop is invite-only.
MIT FutureTech release the AI Risk Repository: a comprehensive and accessible living database of 700+ risks posed by AI that will be expanded and updated to ensure that it remains current and relevant.
We compare projections of Generative AI’s economic impact with that of other technologies from both modern and historical times. We consider impact along two dimensions - GDP and productivity.
In this article, we provide a high level overview of a key trend in AI models: that progress in algorithms leads to performance improvements in AI systems. This is the second in a series of articles on AI trends.
In this article, we provide a high level overview of a key trend in AI models; that AI models’ data requirements may be growing faster than the supply of suitable data. We then explore some implications.
This summer, the DRUID conference in Nice, France, from June 13 to 15, will host a debate on the impact of AI development on knowledge-intensive industries, focusing on the potential for significant deskilling.
Neil Thompson along with Maja S. Svanberg and Wensu Li from the MIT FutureTech, Martin Fleming from The Productivity Institute, and Brian C. Goehring from IBM's Institute for Business Value, have published new article
A study of 300 large firms reveals open innovation is pervasive but recent. Companies increasingly collaborate with external partners for digital technologies, but internal innovation remains key for competitive advantage.
Nur Ahmed and Neil Thompson recently wrote an article for the Brookings Institute about the growing influence of industry in AI research
The MIT Initiative on the Digital Economy interviewed Neil Thompson, Tamay Besiroglu and Peter Slattery about our AI Scaling Workshop
At a recent lab meeting, Benjamin Manning explained how we might use Large Language Models (LLMs) to revolutionize social science research.
Neil Thompson and Nur Ahmed recently featured in an Italian documentary about Artificial Intelligence.
The FutureTech workshop on AI scaling and its Implications aims to gather computer scientists, engineers, and economists to think about scaling laws and their implications for AI development, automation, and more. The workshop is invite-only.
Breakthrough inventions arise unpredictably. Better exploration can identify surprising opportunities.
AI Portability Problem: Innovation Suffers. New research shows machine learning frameworks lose significant functionality and performance on non-standard hardware, hindering research and potentially slowing AI progress.
Research on Go matches from 2003 through 2021 illustrates how interactions between human professionals and artificial intelligence, reshape openings and invasion strategies and showcase a dynamic blend of old wisdom and new AI insights.
Analysis of researches from the University of California from 1997 through 2007 indicates that licensing academic patents typically increases citations
MIT FutureTech Research Lead Neil Thompson, MIT FutureTech Postdoctoral Associate Nur Ahmed, and co-author Muntasir Wahed of Virginia Tech released a new article published in Science.
MIT FutureTech Research Lead Neil Thompson released a new article published in Georgetown Public Policy Review.
MIT FutureTech organized a workshop to examine how computing evolves and what these changes will mean for economic prosperity. Recent shifts in computing overturning long-standing assumptions will be of particular interest.
Businesses in China increasingly source their innovations from customers, competitors, and front-line employees, bucking trends seen elsewhere in the world
Judges are using Wikipedia for legal guidance, a study finds. Law student-written articles on legal cases are shaping judicial decisions and opinions, highlighting a concerning reliance on unauthoritative online sources.
New research identifies two key innovation strategies: "tech-focus," prioritizing exploration, and "market-focus," emphasizing exploitation. This framework better explains firm performance than traditional openness measures.
VC firms impact newly public firms post-IPO, holding significant shares and influencing strategic decisions. This study shows that post-IPO VC ownership boosts R&D, CAPEX investment, and market value (Tobin's Q).
Neil Thompson, Silviu-Marian Udrescu, and Stephan Zhang propose substituting equations for neural network operations to improve efficiency and sustainability in deep learning.
International strategic alliances (ISAs) are vital for local firms. Research indicates that firms in brokerage positions within competition networks are more likely to form ISAs, especially upstream ones.
Compute and data advances drive ML progress. Training compute doubled every 20 months before 2010, accelerated to every 6 months with Deep Learning in 2010s. These trends define three ML eras: Pre Deep Learning, Deep Learning and Large-Scale.
Firm revenue rises with AI adoption intensity, especially with complementary tech investments and strong R&D strategies. Low AI adoption shows minimal growth, but increased intensity boosts revenue, notably with investments in cloud computing
Algorithms are crucial to computing, yet improvement rates were anecdotal. Analyzing 57 textbooks and 1,137 papers, 13% of algorithm families show improvements, and 30%-45% have advances comparable to Moore's Law for moderate-sized problems.
Deep learning's success in translation and medical analysis has risen since the 2000s. However, its 1958 origins reveal a critical issue: immense computational demands. As advancements continue, the cost of improvement becomes unsustainable.
Computing is moving from general-purpose to specialized processors due to the breakdown of Moore's Law. This shift creates 'fast lane' applications with specialized processors and slows applications with general-purpose processors.
Neil Thompson discusses why Deep Learning's economic and environmental footprints are growing worryingly fast
As transistor miniaturization reaches its limits, future performance gains will rely on software, algorithms, and hardware. These gains will be uneven and sporadic, with big system components playing a crucial role
Introducing a bioinformatics method to distinguish natural from synthetic genes using nucleotide sequences, achieving 97.7% accuracy. This tool aids in biosurveillance, showing how gene synthesis allows exploration of diverse genetic sources
Analysis shows the U.S. as a key contributor to the "Algorithmic Commons," with major roles by universities and labs like IBM. Historical shifts reflect geopolitical changes, with Europe and Asia's rising influence in recent decades.
MIT FutureTechMIT Computer Science and Artificial Intelligence Laboratory32 Vassar Street, Office 386, Cambridge, MA 02139617-258-5030
© 2026 FutureTech.All rights reserved.
-p-1600.png)
