News & Insights

The latest news and insights from the FutureTech team.

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AI and the Future of Scientific Discovery

AI and the Future of Scientific Discovery

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.

Event
April 29, 2025
What drives progress in AI? Trends in Compute

What drives progress in AI? Trends in Compute

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.

Resource
January 3, 2025
FutureTech hosts a workshop on the Economics of AI and Automation

FutureTech hosts a workshop on the Economics of AI and Automation

MIT FutureTech hosts a workshop on the Economics of AI and Automation. The workshop is invite-only.

Event
December 4, 2024
FutureTech announces workshop on The Role of AI in Science

FutureTech announces workshop on The Role of AI in Science

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.

Event
October 16, 2024
Presenting the AI Risk Repository

Presenting the AI Risk Repository

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.

Pre-print
August 14, 2024
Putting the economic impact of GenAI into scale

Putting the economic impact of GenAI into scale

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.

Other
June 20, 2024
What drives progress in AI? Trends in Algorithms

What drives progress in AI? Trends in Algorithms

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.

Resource
May 20, 2024
What drives progress in AI? Trends in Data

What drives progress in AI? Trends in Data

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.‍

Resource
March 19, 2024
DRUID Conference: Debating AI's Impact on Knowledge-Intensive Industries

DRUID Conference: Debating AI's Impact on Knowledge-Intensive Industries

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.

Talk/Interview
February 28, 2024
Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?

Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?

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

January 22, 2024
Sourcing Innovation in the Digital Age

Sourcing Innovation in the Digital Age

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.

Publication
December 5, 2023
What should be done about the growing influence of industry in AI research?

What should be done about the growing influence of industry in AI research?

Nur Ahmed and Neil Thompson recently wrote an article for the Brookings Institute about the growing influence of industry in AI research

December 5, 2023
What’s next in AI scaling: a report on our workshop

What’s next in AI scaling: a report on our workshop

The MIT Initiative on the Digital Economy interviewed Neil Thompson, Tamay Besiroglu and Peter Slattery about our AI Scaling Workshop

Resource
November 17, 2023
Presentation: What Can We Learn from Homo Silicus? How Large Language Models (LLMs) may revolutionize social science research

Presentation: What Can We Learn from Homo Silicus? How Large Language Models (LLMs) may revolutionize social science research

At a recent lab meeting, Benjamin Manning explained how we might use Large Language Models (LLMs) to revolutionize social science research.

November 15, 2023
Neil Thompson and Nur Ahmed feature in Italian Artificial Intelligence documentary

Neil Thompson and Nur Ahmed feature in Italian Artificial Intelligence documentary

Neil Thompson and Nur Ahmed recently featured in an Italian documentary about Artificial Intelligence.

October 31, 2023
FutureTech announces workshop on AI Scaling and Implications

FutureTech announces workshop on AI Scaling and Implications

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.

Event
September 15, 2023
Unleash the Unexpected for Radical Innovation

Unleash the Unexpected for Radical Innovation

Breakthrough inventions arise unpredictably. Better exploration can identify surprising opportunities.

Other
September 12, 2023
The Grand Illusion: The Myth of Software Portability and Implications for ML Progress

The Grand Illusion: The Myth of Software Portability and Implications for ML Progress

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.

Publication
September 12, 2023
Standing on the Shoulders of AI: Pushing the Knowledge Frontier by Learning from AI

Standing on the Shoulders of AI: Pushing the Knowledge Frontier by Learning from AI

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.

Publication
July 24, 2023
University licensing and the flow of scientific knowledge

University licensing and the flow of scientific knowledge

Analysis of researches from the University of California from 1997 through 2007 indicates that licensing academic patents typically increases citations

July 1, 2023
The Growing Influence of Industry in AI Research

The Growing Influence of Industry in AI Research

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.

March 2, 2023
America’s lead in advanced computing is almost gone

America’s lead in advanced computing is almost gone

MIT FutureTech Research Lead Neil Thompson released a new article published in Georgetown Public Policy Review.

February 28, 2023
Workshop on the Computational Foundations of Prosperity

Workshop on the Computational Foundations of Prosperity

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.

Event
September 30, 2022
Why Innovators in China Stay Close to the Market

Why Innovators in China Stay Close to the Market

Businesses in China increasingly source their innovations from customers, competitors, and front-line employees, bucking trends seen elsewhere in the world

Other
September 13, 2022
Trial by Internet: A Randomized Field Experiment on Wikipedia’s Influence on Judges’ Legal Reasoning

Trial by Internet: A Randomized Field Experiment on Wikipedia’s Influence on Judges’ Legal Reasoning

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.

Publication
August 1, 2022
Sources of Innovation Revisited: Patterns in Firms’ Usage of Internal and External Search

Sources of Innovation Revisited: Patterns in Firms’ Usage of Internal and External Search

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.

Publication
July 1, 2022
Post-IPO VC Ownership Effect on Innovation-Enhancing Investment of Newly Public Firms

Post-IPO VC Ownership Effect on Innovation-Enhancing Investment of Newly Public Firms

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).

Publication
July 1, 2022
Here's How to Make Deep Learning More Sustainable

Here's How to Make Deep Learning More Sustainable

Neil Thompson, Silviu-Marian Udrescu, and Stephan Zhang propose substituting equations for neural network operations to improve efficiency and sustainability in deep learning.

Publication
May 9, 2022
International alliance formations: The role of brokerage in technology competition networks

International alliance formations: The role of brokerage in technology competition networks

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.

Publication
May 1, 2022
Compute Trends Across Three Eras of Machine Learning

Compute Trends Across Three Eras of Machine Learning

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.

Publication
February 1, 2022
When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy

When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy

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

Publication
December 1, 2021
How Fast do Algorithms Improve?

How Fast do Algorithms Improve?

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.

Publication
October 1, 2021
Deep Learning's Diminishing Returns: The Cost of Improvement is Becoming Unsustainable

Deep Learning's Diminishing Returns: The Cost of Improvement is Becoming Unsustainable

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.

Publication
October 1, 2021
The Decline of Computers as a General Purpose Technology

The Decline of Computers as a General Purpose Technology

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.

Publication
March 1, 2021
How can we counter the increased demand for computation? Neil Thompson interviewed by Synced

How can we counter the increased demand for computation? Neil Thompson interviewed by Synced

Neil Thompson discusses why Deep Learning's economic and environmental footprints are growing worryingly fast

October 15, 2020
There’s plenty of room at the Top: What will drive computer performance after Moore’s law?

There’s plenty of room at the Top: What will drive computer performance after Moore’s law?

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

Publication
June 1, 2020
Gene synthesis allows biologists to source genes from farther away in the tree of life

Gene synthesis allows biologists to source genes from farther away in the tree of life

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

Publication
October 1, 2018
Building the algorithm commons: Who discovered the algorithms that underpin computing in the modern enterprise?

Building the algorithm commons: Who discovered the algorithms that underpin computing in the modern enterprise?

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.

Publication
July 1, 2017

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