🔮 Slow AI?; Chatbot therapists; Mis-misinformation; Blockchain genesis; Long covid ++ #458

Hi, I’m Azeem Azhar. I advise governments, some of the world’s largest firms, and investors on how to make sense of our exponential future. Every Sunday, I share my view on AI and other exponential technologies in this newsletter.

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Another week, another study exploring AI’s role in automation. However, unlike previous ones, this MIT study by Svanberg et al. didn’t just ask whether AI could perform a task, but also if it’s affordable to use AI for it. This is a more complicated question to answer, hence the decision to only study computer vision because of more information about its costs and applicability.

At current costs, the study shows, most businesses would not consider automating vision tasks. It was found that automating these tasks would be cost-effective for only about 23% of the wages currently allocated to them. This indicates that while the technical feasibility of automation with computer vision exists, it would be economically viable in only a quarter of cases.

The study suggests that AI will spread slowly, giving us more time to handle its impact on jobs, as mentioned in last week’s newsletter. The authors showed that the pace of vision task automation depends on how fast costs decline. Even at a 50% annual cost decline, adoption would be slower than the rate of US job loss between 2017-2019. Overall, the fear of AI replacing jobs, especially in computer vision, seems exaggerated.

But LLMs are different. A foundational language model can generalise across tasks more easily than an image model. For example, I don’t have to fine-tune ChatGPT to produce ad copy for a marketing campaign. But for vision tasks, you need to tailor it to specific jobs, like spotting defects in a product. Another difference is that text data for fine-tuning LLMs is often cheaper and more available than images. One of the authors said that:

While AI systems are certainly rolling out quickly, their improvements are remarkably predictable, as work in our lab and others demonstrate.

I don’t necessarily agree with this. Yes, while scaling laws have shown to be predictable, this assumes no other architecture or technique improvements occur. This is unlikely given the sheer amount of financial and labour resources that have been thrown at AI in the past year.

Overall, this is a great study, highlighting that AI exposure doesn’t mean economic feasibility, but it doesn’t extend to LLMs. Hopefully, it sets the groundwork for a similar study focusing on the feasibility of AI replacing text-based cognitive tasks.

See also: ServiceNow, a software business with a $150bn market cap, credits genAI for driving the majority of their new customer contracts in the last quarter of 2023. The CEO claims genAI has improved “developer innovation speed by 52%”.

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Digital solace. Just a couple of weeks ago, Eric Topol and I discussed the surprising competency of LLMs in empathetic interactions. A survey in Nature of over a thousand lonely students found that using Replika, a chatbot, helped them feel supported. The findings go even further: 3% of students reportedly stopped having suicidal thoughts after chatting with Replika. Replacing therapy with chatbots is a complicated proposition, one that needs more research and expert consideration. There’s a worry that people would get too attached to the chatbots, or that AI’s responses might be inappropriate. On the positive side, chatbots are always available and don’t judge. After all, the reality is that we live in a world where therapy is scarce and unaffordable for many — 6 in 10 psychologists in the US no longer have openings for new patients. Perhaps chatbots could be a valuable ally for mental health.

Decentralised supply chains. Sam Altman is looking to raise money for a network of globally decentralised chip factories. A smart idea, given the precarity of chip supply chains. Due to their centralisation, there are numerous potential points of failure, Taiwan being the most obvious one. Even TSMC is spreading its risks by building factories outside of Taiwan. It’ll take a long time for TSMC to establish cutting-edge fabs elsewhere. The Arizona fab is building on a five-year-old technology, the 5nm process node, and is struggling to recruit. Meanwhile, in the face of sanctions, China is creating its own supply chain to wean itself off Western dependency. It imported $1.1 billion of lithography equipment from the Netherlands (home of ASML), an increase of 1000% from a year earlier.

Poisoned Apple? Apple has updated its AppStore policies to comply with the EU’s Digital Markets Act. The Act mandates that large platforms provide access to alternative App stores and payment mechanisms. The changes are enormous: the firm claims at least 600 new APIs and a new process for “notarising” how apps were developed. Apple itself claims the changes will worsen the user experience (they probably will), while 3rd party developers, like Daniel Ek, argue the changes will result in untenable economics. Many other software vendors agree. I doubt this is the end of the saga: figuring out how to manage the complexity of platform ecosystems and how value is apportioned is not an easy tradeoff. Hygiene, security and convenience — which the App store delivered — don’t come for free. But how much should they cost?

Two truths, one lie. AI will undoubtedly decrease the price of producing misinformation and increase its persuasiveness, as highlighted by the fake Biden robocall this week. However, studies on the impact of fake news suggest that fear of misinformation itself may have been hyped. This Brookings report provides a nuanced view of the reality on the ground in the US. Rather than focusing solely on fake news, which makes up a very small portion of the information diet, we need to assess the health of the information ecosystem — if people distrust the environment in which the news is created and delivered, they will more easily fall into the trap of believing true news to be fake.