Research scientist @MIT and Chief Economist @linuxfoundation studying AI, digitization, crowds, open source software, cybersecurity, & the future of work.
New paper alert: We (w/ @ichbinsnicht, @samjboysel, Sida Peng, Kevin Xu) just released the working paper version of our study “Generative AI and the Nature of Work” - https://t.co/wgFFbD8k1e. We seek to build upon research on #AI and productivity to better understand how #GenAI
"There's a lot to be learned from the way that junior people are interacting with AI."
MIT research scientist and advising Chief Economic Officer for the Linux Foundation, @frank_nagle recently sat down with @Entrepreneur to discuss how artificial intelligence is rewiring the workplace.
Read the full article by @WriteSherin here: https://t.co/oElY4E7BUm
Is AI the end of the developer? Or the end of developer "toil"? Linux Foundation Chief Economist and @MIT Research Scientist, @frank_nagle joins @BloombergTV to discuss how AI restores the balance for engineers, moving them toward specialized innovation.
#FutureOfWork #SoftwareEngineering
The Vibe Coding Era: Why AI Won’t Replace Software Engineers https://t.co/59Yea6tuil
“Our research has shown that these junior [engineers] are the ones who are able to change their job and get the most out of using these tools. If we aren’t hiring them at the same rate as before, then we aren’t going to be able to take advantage of that.”
That’s according to @frank_nagle, Linux Foundation Chief Economist, on the future of the software engineer under AI. In an interview with Bloomberg’s @EdLudlow, he unpacks how the lower barrier to entry on coding has created a new world for both non-expert and expert engineers.
Listen to Nagle’s perspective on modern coding tools, hiring prospects for entry-level candidates, and how leaders should be adapting to a vibe-coded future, here: https://t.co/zUCkDckErf
It also discusses the broader impacts on learning and teamwork (or the lack thereof) that can occur after increased use of #AI - important tradeoffs from the productivity gains achieved.
Thanks to @MITSloan Ideas Made to Matter for the great coverage of our research (w/ @ichbinsnicht, @samjboysel, Sida Peng, & Kevin Xu) in this article "Generative AI changes how employees spend their time" (https://t.co/y8oWZdZpnD) written by @Brian_Eastwood .
The article highlights some of our key findings (original paper here https://t.co/wgFFbD8k1e) - use of #GenAI tools increases the proportion of time software developers allocate to coding and decreases that allocated to project management.
A new paper co-authored by MIT IDE researcher @frank_nagle finds that optimal reallocation of demand from closed to open models could save the global AI economy about $25 billion annually. https://t.co/8InR200Wcc
Today we launch the Agentic AI Foundation (AAIF) with project contributions of MCP (@AnthropicAI), goose (@blocks) and https://t.co/KhZ4k71lmO (@OpenAI), creating a shared ecosystem for tools, standards, and community-driven innovation.
Learn more about this major step toward: https://t.co/JHvfl8D0UP
AI Reality Check: Open models deliver a stunning 90%+ of performance at a fraction of the cost. The result of their underuse? $48B in unrealized value every year.
Check out the full blog by LF’s Chief Economist @frank_nagle: https://t.co/rLmw9oRVTj
#OpenSource#AI#OpenModels
CCutting entry-level roles in the AI era is a "profound strategic error."
The LF’s Chief Economist @Frank_Nagle explains in a new @FortuneMagazine article why sacrificing junior talent jeopardizes future leadership and institutional knowledge.
Read the full piece: https://t.co/LrzW8dD82k
#AI #FutureOfWork #OpenSource
• Shines light on the debate about the future of the AI economy - will it be an oligopoly or will AI turn into a commodity?
https://t.co/hSDyJU6QdM
Thanks to @mit_ide & @linuxfoundation for support!
Open model AI is a sleeping giant.
Our new paper (w/ @dyuephoria) shows:
• Closed models: 96% of revenue, 80% of usage
• Open models: near-parity performance (and catching up faster), 6× cheaper
• Many closed models are dominated by open ones - better performance & cheaper
• Switching could save $20–48B/yr (primary estimate is $24.8B)
• Indicates price and performance are not the only factors driving demand - switching costs, brand trust, institutional risk aversion, and information frictions are likely at play
Why do consumers go with closed models when open options are often more powerful and less expensive? @frank_nagle points out people often think they can't use "open" models because the world would know their data - which is exactly backwards as open models can be run entirely privately #AIConference #AIExpert #MITFutureTech