Virtual employees could join workforces this year and transform how companies work, according to the chief executive of OpenAI. The first artificial intelligence agents may start working for organisations this year, wrote Sam Altman, as AI firms push for uses that generate returns on substantial investment in the technology. Microsoft, the biggest backer of the company behind ChatGPT, has already announced the introduction of AI agents – tools that can carry out tasks autonomously – with the blue-chip consulting firm McKinsey among the early adopters. "We believe that, in 2025, we may see the first AI agents'join the workforce' and materially change the output of companies," wrote Altman in a blogpost published on Monday. OpenAI is reportedly planning to launch an AI agent codenamed "Operator" this month, after Microsoft announced its Copilot Studio product and rival Anthropic launched the Claude 3.5 Sonnet AI model, which can carry out tasks on the computer such as moving a mouse cursor and typing text.

If 2024 was the year of large language models (LLMs), then 2025 looks like the year of AI "agents". These are quasi-intelligent systems that harness LLMs to go beyond their usual tricks of generating plausible text or responding to prompts. The idea is that an agent can be given a high-level – possibly even vague – goal and break it down into a series of actionable steps. Once it "understands" the goal, it can devise a plan to achieve it, much as a human would. OpenAI's chief financial officer, Sarah Friar, recently explained it thus to the Financial Times: "It could be a researcher, a helpful assistant for everyday people, working moms like me. In 2025, we will see the first very successful agents deployed that help people in their day to day."

Since Shein became the world's most popular online shopping destination--with seemingly unbeatable prices, and influencers posting "haul" videos to show off their purchases on social media--the Chinese fast-fashion giant has raised questions over how it produces its plethora of merchandise at dizzying speeds. The answer: AI-powered algorithms that allow the company to pick up changes in customer demand and interest, allowing it to adjust its supply chain in real time. As a result, Shein reportedly lists as many as 600,000 items on its online platform at any given moment, selling to customers in over 220 countries and regions globally. But the company has also long been under scrutiny for its poor record on environmental sustainability, becoming fashion's biggest polluter in 2023. Investigations into Shein's supply chains have found severe labor rights violations, with factory workers in Southern Chinese manufacturing plants reporting grueling 75-hour work weeks to keep up with demand. Shein claims AI is the answer to solving these problems, too.

Earlier this month, a woman in San Francisco was hit by a car while crossing the street. Had the story ended there, it would have been just another one of the small tragedies that occur on America's roads, where roughly 100 people die every day. She was hit again, this time by a robotaxi from the start-up Cruise. The car braked, coming to a stop with her pinned underneath. Then it started driving again, dragging the woman along with it for an agonizing 20 more feet.

The reason we can't just wish away or "fix" complexity is that every solution--whether it's a technology or methodology--redistributes complexity in some way. When microservices emerged (a software architecture approach where an application or system is composed of many smaller parts), they seemingly solved many of the maintenance and development challenges posed by monolithic architectures (where the application is one single interlocking system). However, in doing so microservices placed new demands on engineering teams; they require greater maturity in terms of practices and processes. This is one of the reasons why we cautioned people against what we call "microservice envy" in a 2018 edition of the Technology Radar, with CTO Rebecca Parsons writing that microservices would never be recommended for adoption on Technology Radar because "not all organizations are microservices-ready." We noticed there was a tendency to look to adopt microservices simply because it was fashionable.

In all the frenzied discourse about large language models (LLMs) such as GPT-4 there is one point on which everyone seems to agree: these models are essentially stochastic parrots – namely, machines that are good at generating convincing sentences, but do not actually understand the meaning of the language they are processing. They have somehow "read" (that is, ingested) everything ever published in machine-readable form and create sentences word by word, at each point making a statistical guess of "what one might expect someone to write after seeing what people have written on billions of webpages, etc". Ever since ChatGPT arrived last November, people have been astonished by the capabilities of these parrots – how humanlike they seem to be and so on. But consolation was drawn initially from the thought that since the models were drawing only on what already resided in their capacious memories, then they couldn't be genuinely original: they would just regurgitate the conventional wisdom embedded in their training data. That comforting thought didn't last long, though, as experimenters kept finding startling and unpredictable behaviours of LLMs – facets now labelled "emergent abilities".

In the first four months of the Covid-19 pandemic, government leaders paid $100 million for management consultants at McKinsey to model the spread of the coronavirus and build online dashboards to project hospital capacity. It's unsurprising that leaders turned to McKinsey for help, given the notorious backwardness of government technology. Our everyday experience with online shopping and search only highlights the stark contrast between user-friendly interfaces and the frustrating inefficiencies of government websites--or worse yet, the ongoing need to visit a government office to submit forms in person. The 2016 animated movie Zootopia depicts literal sloths running the DMV, a scene that was guaranteed to get laughs given our low expectations of government responsiveness. More seriously, these doubts are reflected in the plummeting levels of public trust in government.

When we talk about artificial intelligence, we rely on metaphor, as we always do when dealing with something new and unfamiliar. Metaphors are, by their nature, imperfect, but we still need to choose them carefully, because bad ones can lead us astray. For example, it's become very common to compare powerful A.I.s to genies in fairy tales. The metaphor is meant to highlight the difficulty of making powerful entities obey your commands; the computer scientist Stuart Russell has cited the parable of King Midas, who demanded that everything he touched turn into gold, to illustrate the dangers of an A.I. doing what you tell it to do instead of what you want it to do. There are multiple problems with this metaphor, but one of them is that it derives the wrong lessons from the tale to which it refers.

Global private investment and the number of AI startups decreased in 2022, while the industry's adoption of the technology has plateaued compared to previous years, according to new data. This revelation hits at a time when AI hype is at an all-time high. Commercial tools capable of generating images, text, code, video, audio, and even music are rapidly improving and becoming increasingly convincing. Companies across different industries are looking to deploy generative AI features to revamp existing products and services or create new ones. Analysts are predicting the boom will increase global productivity and change the labor force, while experts are debating whether the technology poses an existential threat to humanity.

How to engage customers--and keep them engaged--is a focal question for organizations across the business-to-consumer (B2C) landscape, where disintermediation by digital platforms continues to erode traditional business models. Engaged customers are more loyal, have more touchpoints with their chosen brands, and deliver greater value over their lifetime. Yet financial institutions have often struggled to secure the deep consumer engagement typical in other mobile app–intermediated services. The average visit to a bank app lasts only half as long as a visit to an online shopping app, and only one-quarter as long as a visit to a gaming app. Hence, customer service offers one of the few opportunities available to transform financial-services interactions into memorable and long-lasting engagements.