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Ethan Mollick writes about AI agents doing real work and a workflow for experts to delegate, review and retry tasks. He reports a paper’s estimates for speed and cost, and argues that people must decide which work is worth doing. Read 3 viewpoints with supporting evidence and source links.
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Synthesis
Mollick says that AIs can now perform real, economically relevant work.
Original excerpt
AIs have quietly crossed a threshold: they can now perform real, economically relevant work.
Ethan Mollick · Paragraph 2
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AI and the economy →Mollick describes a workflow from an OpenAI paper: delegate a task to AI, review the result, try corrections or better instructions, and do the work yourself if those attempts fail. He reports the paper’s estimate that following this workflow would let experts finish work 40% faster and at 60% lower cost while retaining control over AI.
Original excerpt
The OpenAI paper suggested that experts can work with AI to solve problems by delegating tasks to an AI as a first pass and reviewing the work. If it isn’t good enough, they should try a couple of attempts to give corrections or better instructions. If that doesn’t work, they should just do the work themselves. If experts followed this workflow, the paper estimates they would get work done forty percent faster and sixty percent cheaper, and, even more importantly, retain control over the AI.
Ethan Mollick · Paragraph 16
If we don’t think hard about WHY we are doing work, and what work should look like, we are all going to drown in a wave of AI content. What is the alternative?
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AI products →Mollick contrasts agents reproducing academic papers with producing unnecessary PowerPoint decks. He argues that people should use judgment to decide what work is worth doing, rather than only what AI can do.
Original excerpt
Agents are here. They can do real work, and while that work is still limited, it is valuable and increasing. But the same technology that can replicate academic papers in minutes can also generate 17 versions of a PowerPoint deck that nobody needs. The difference between these futures isn’t in the AI, it’s in how we choose to use it. By using our judgement in deciding what’s worth doing, not just what can be done, we can ensure these tools make us more capable, not just more productive.
Ethan Mollick · Paragraph 17
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AI products →Attributed passages with the context to verify them. Open the original text to check the source.
Original excerpt
AIs have quietly crossed a threshold: they can now perform real, economically relevant work.Original excerpt
The OpenAI paper suggested that experts can work with AI to solve problems by delegating tasks to an AI as a first pass and reviewing the work. If it isn’t good enough, they should try a couple of attempts to give corrections or better instructions. If that doesn’t work, they should just do the work themselves. If experts followed this workflow, the paper estimates they would get work done forty percent faster and sixty percent cheaper, and, even more importantly, retain control over the AI.If we don’t think hard about WHY we are doing work, and what work should look like, we are all going to drown in a wave of AI content. What is the alternative?
Original excerpt
Agents are here. They can do real work, and while that work is still limited, it is valuable and increasing. But the same technology that can replicate academic papers in minutes can also generate 17 versions of a PowerPoint deck that nobody needs. The difference between these futures isn’t in the AI, it’s in how we choose to use it. By using our judgement in deciding what’s worth doing, not just what can be done, we can ensure these tools make us more capable, not just more productive.These viewpoints are linked to their original sources. Paraphrases are labeled and are not verbatim quotes.
Open transcript or source material (opens in a new tab)Report an issueEthan Mollick on AI products, Agent delegation in coding tools, AI agent coordination design. Explore 12 viewpoints by topic, with evidence from 3 sources.
A word-level transcript score does not test the whole job promised to a customer. Francisco identifies call behaviors and outcomes that it misses; Walling asks what a vendor can promise and stand behind. Mollick’s expert-led workflow also keeps review and fallback in scope. Comparing these sources clarifies the gap between measuring transcript accuracy and evaluating a replacement promise; it supplies no universal passing score.
More sources on topics discussed here. Shared topics do not imply agreement.
