+ THREE INSIGHTS FOR THE WEEK |
1. Innovators have aspired in recent years to curb climate change with new technologies — think green concrete, green hydrogen, green aviation fuel, and more. But many of these ideas have so far failed to take off.
“There are a lot of key green technologies that have been developed … but the funding needed to scale them can be quite substantial,” said Florian Berg, a principal research scientist with the MIT Sloan Sustainability Initiative.
Enter the new MIT Catalytic Climate Finance Project, co-founded by Berg and Sustainability Initiative director Jason Jay, which aims to help nascent technologies navigate the “missing middle” between proving that a technology will work and making it affordable enough to compete in the market.
Formed around the premise that climate-focused capital should be catalytic — that is, it should accelerate action — the CCFP intends to provide climate startups with strategic funding from philanthropists and private companies for early-stage and scale-up costs.
The goal is to make climate technologies less risky and more attractive to private investors. “Once we scale [these] technologies, they become cost-competitive and can outcompete brown commodities and technologies and draw in a lot of additional traditional capital,” Berg said.
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2. When artificial intelligence outperforms humans, companies might be tempted to replace employees with automation.
But this leads to worse outcomes in the long run, MIT Sloan professor Sinan Aral said in a recent interview with creativity strategist Natalie Nixon for Fast Company.
Aral’s research has produced two related findings about the dangers of outsourcing creativity to AI:
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- In one study, human-AI teams produced more marketing ads than human-human teams. But the ads looked similar to one another — an example of what Aral calls “diversity collapse.”
- In a newer research paper, Aral and co-author Michael Caosun found that when employees outsource tasks to AI that they could do themselves, their ability to do those tasks erodes.
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What can individuals and organizations do about these risks? Be intentional about designing human-AI collaboration, Aral said. This includes measuring human skill levels independently of AI output and designing workflows so humans engage with AI outputs instead of simply accepting them.
Aral’s research indicates that “the organizations that will win in the imagination era are not those that replace the most humans with AI, but those that become genuinely excellent at human-AI collaboration,” Nixon writes.
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3. Kevin Warsh was sworn in as chair of the Federal Reserve last Friday. In the latest episode of their “Power and Consequences” podcast, Gary Gensler, former chair of the Securities and Exchange Commission, and Nobel economist Simon Johnson, both MIT Sloan professors, frame Warsh’s ascendance as a historically consequential leadership challenge.
Warsh has said he wants a narrower Federal Reserve with a smaller balance sheet — but shrinking it would push long-term interest rates higher, including mortgage rates, which President Trump has said he does not want.
Gensler draws a historical parallel to a 1951 standoff between President Truman and the Fed that ended with the central bank reasserting its independence — and Truman calling his own Fed chair a traitor.
“Will Kevin Warsh be independent enough that later Donald Trump calls him a traitor?” Gensler asks.
The pair also discuss how Warsh might grapple with the energy shock caused by the war in Iran and the inflationary implications of the boom in AI infrastructure investment.
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What leaders still get wrong about AI
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Research from the MIT Center for Information Systems Research uncovers common AI implementation mistakes and how to overcome them.
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Many organizations have yet to parlay artificial intelligence experimentation into large-scale initiatives that deliver a return on investment.
“They govern AI like legacy IT,” said Nick van der Meulen, a research scientist at MIT CISR. “[They’re] applying yesterday’s best practices to an inherently different technology.”
Van der Meulen and his colleagues at MIT CISR have identified five common mistakes that impede AI success, among them:
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- Treating AI as something you do, not a tool to get results. Organizations can be so consumed with doing something AI-related that they forget that the technology is another tool in the toolbox — and a complex one at that. To generate real value from AI, organizations must invest in the right capabilities and practices to do the work properly; they shouldn’t expect instantaneous results.
- Mistaking productivity gains for value. Tailored generative AI solutions aimed at achieving strategic business goals operate on a broader scale than tools intended to enhance personal productivity. These strategic solutions need to be integrated with processes and systems — a more complex undertaking than employing generative AI for personal use.
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A measurable dip in performance followed by stronger growth in output, revenue, and employment that often occurs when a firm implements artificial intelligence.
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