OECD 研究估計，如果發 Basic Income (BI)，把現行的社福支出都用上，老人與窮人所能得到的金額會減少，因為原本針對他們的社福給付現在要分給所有人了 (BI 倡議的原始目標之一就是廢掉福利國家，把原本由社福官僚掌控的預算發給大家，自己在市場上愛怎麼花就怎麼花)。而如果要給所有人足以維持生活需求的收入，則需要增稅才能支應。
至於說給 BI 會讓人失去工作動力，大概是無謂的擔心。不僅許多研究指出人們會繼續因為社會歸屬與成就感而工作，更重要的是，產生 BI 的這整個社會過程，不會讓 BI 高到讓你不工作還能過上 “好" 生活。
The second wave of the second machine age brings with it new risks. In particular, machine learning systems often have low “interpretability,” meaning that humans have difficulty figuring out how the systems reached their decisions. Deep neural networks may have hundreds of millions of connections, each of which contributes a small amount to the ultimate decision. As a result, these systems’ predictions tend to resist simple, clear explanation. Unlike humans, machines are not (yet!) good storytellers. They can’t always give a rationale for why a particular applicant was accepted or rejected for a job, or a particular medicine was recommended. Ironically, even as we have begun to overcome Polanyi’s Paradox, we’re facing a kind of reverse version: Machines know more than they can tell us.
這確實是想把 AI 應用到經濟學時，須克服的一大障礙。在我學 ABM 的過程中，我發現這也是 ABM 或其他的 complexity 理論，在經濟學界不太受歡迎的原因之一。經濟學家畢竟不是工程師。
Then as now, one of the key new capabilities was vision. When animals first gained this capability, it allowed them to explore the environment far more effectively; that catalyzed an enormous increase in the number of species, both predators and prey, and in the range of ecological niches that were filled. Today as well we expect to see a variety of new products, services, processes, and organizational forms and also numerous extinctions.
以視力發展的比喻來說，用 AI 來識別 patterns 或許是比較簡易可行的應用？
The most effective rule for the new division of labor is rarely, if ever, “give all tasks to the machine.” Instead, if the successful completion of a process requires 10 steps, one or two of them may become automated while the rest become more valuable for humans to do.
Over the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t.
這主要是 inequality between firms.