In a 1987 article in the Times Book Review, Robert Solow, a Nobel-winning economist at M.I.T., commented, "You can see the computer age everywhere but in the productivity statistics." Despite massive increases in computing power and the rising popularity of personal computers, government figures showed that over-all output per worker, a key determinant of wages and living standards, had stagnated for more than a decade. The "productivity paradox," as it came to be known, persisted into the nineteen-nineties and beyond, generating a huge and inconclusive body of literature. Some economists blamed mismanagement of the new technology; others argued that computers paled in economic importance compared to older inventions such as the steam engine and electricity; still others blamed measurement errors in the data and argued that once these were corrected the paradox disappeared. Nearly forty years after Solow's article, and almost three years since OpenAI released its ChatGPT chatbot, we may be facing a new economic paradox, this one involving generative artificial intelligence.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Last Friday, the A.I. lab Anthropic announced in a blog post that it has given its chatbot Claude the right to walk away from conversations when it feels "distress." In its post, the company says it will let certain models of Claude nope out in "rare, extreme cases of persistently harmful or abusive user interactions." It's not Claude saying "The lawyers won't let me write erotic Donald Trump/Minnie Mouse fanfic for you." It's Claude saying "I'm sick of your bullshit, and you have to go." Anthropic, which has been quietly dabbling in the question of "A.I. welfare" for some time, conducted actual tests to see if Claude secretly hates his job.

In his latest column for Digital Health, Andrew Davies, digital health lead at the Association of British HealthTech Industries (ABHI), explores the use of artificial intelligence (AI) in healthcare. Most of us will have seen films or read sci-fi books about malevolent robots taking over the world. Although usually, in best Hollywood style, humanity wins out in the end. This is the scary end of AI, the out-of-control robot that is autonomous and bent on world domination. Of course, the reality of the situation today is far from this, but that is not to say that AI cannot cause harm if deployed carelessly.

Investments in artificial intelligence and machine learning are finally on the rise in healthcare. While the industry has been slow to adopt AI in comparison to other sectors like financial services and manufacturing – with 70% of health systems yet to establish a formal program – a recent survey found that 68% of health system executives plan to invest more in AI in the next five years to help reach their strategic goals. And the investments are expected to be significant; the global AI in healthcare market size is estimated to reach $120.2 billion by 2028. The opportunities for AI in healthcare are widespread, spanning both operational and clinical use cases including fraud prevention, voice-assisted charting, registration, remote patient monitoring and more. AI holds particular promise for connected medical devices and telehealth – an integral part of the Internet of Medical Things (IoMT) – as it enables faster triage, intake, detection and decision making.

The damage from pandemic-induced lockdowns, office and school closures and consumer retrenchment continue to reverberate through the economy. As the crisis drags into its seventh month, it has left businesses facing hard choices in adjusting to what now seems like many permanent changes. Required actions to address the COVID-19 crisis can be divided into three major stages: Respond, Recover and Thrive. These three stages are interspersed with two additional interim stages, and culminate in a long-term operating environment we call the'next normal'. The early months were focused on business survival through a series of reactionary changes, which was followed by mid-term operational stabilization in a world with diminished demand, continued socio-political restrictions and unpredictable events.

Even before the dawn of AI and machine learning, we humans have constantly been searching for ways to get others to do our work for us. At the moment, AI -- and in our case chatbots -- seem like the least disruptive way of doing just that. According to a recent survey done by American Express, 60% of its customers prefer to use websites, chatbots, and other virtual self-service tools to answer simple queries over contacting human representatives. In fact, many companies around the world have warmed up to this practice of using chatbots. Yet, what about its application in the banking sector and what does it all mean for the future of this industry?

Artificial intelligence has long caused fear of job loss across many sectors as companies look for ways to cut costs, support workers and become more profitable. But new research suggests that even in STEM-based sectors like cybersecurity, AI simply can't replace some traits found only in humans, such as creativity, intuition and experience. There's no doubt, AI certainly has its place. And most business leaders agree that AI is important to the future success of their company. A recent survey found CEOs believe the benefits of AI include creating better efficiencies (62 percent), helping businesses remain competitive (62 percent), and allowing organizations to gain a better understanding of their customers, according to Ernst and Young.

The next 10 years look just as topsy-turvy. Artificial intelligence and machine learning promise to change the competitive landscape for many companies. At the same time, talented professionals will continue to demand more from their jobs through increased calls for transparency around pay and fairness and more flexibility in work-life balance. It's a lot for companies to navigate, and they're struggling with it: an analysis by Korn Ferry of more than 150,000 leadership profiles shows that only 15% of business leaders today have the right blend of skills to be the leaders of tomorrow. But such disruption can be a boon to workers who are agile and forward thinking.

Digital fraud continues to flourish, with recent surveys finding that security breaches have increased 67 percent since 2014 and 11 percent since 2018. Casualties of these breaches in the first half of 2019 alone include 4.1 billion personal records exposed in a variety of ways: 52 percent through hacking; 33 percent via phishing; and 32 percent through social engineering, with many involving more than one method. Organizations and security developers are investing billions of dollars in fighting these fraud attempts. Worldwide spending on security systems is projected to hit $131 billion by the end of 2020, and $174 billion over the next two years. Artificial intelligence (AI) and machine learning (ML) applications often form the core of these cybersecurity systems and are being deployed across banks, retailers, telecommunications companies and many other businesses.

In the coming year there will be less isolated experimentation around AI; many companies will move out of the pilot phase and toward enterprise-wide deployment. Although AI at scale is rare today, many companies are setting this as a core objective over the next three years. To rapidly pursue AI, many companies are looking to third-party AIaaS vendors. While embracing third parties does not eliminate the need to build up internal capabilities, it opens up more options. In a sample we surveyed of large global companies, we estimated that roughly two-thirds of their total AI spending is currently allocated to internal build – and only one-third is dedicated to buying services from vendors.