The shift to artificial intelligence models that can carry out tasks, not just generate content, hasn’t yet taken off in the way the tech industry desperately needs it to.
Salesforce Inc. has struggled to get traction with its nearly two-year-old Agentforce service for businesses. Australia’s Commonwealth Bank reversed plans to cut 45 customer-service staff after its rollout of agents led to a surge in user calls. And a Gartner review found less than 10% of agent deployments by businesses were leading to “robust” return on investment. You could argue that we’re still in the experimentation phase for this technology, but it can’t remain this way for another two or three years with so much money on the line.
The trillions of dollars of spending on data centers is largely underpinned by the idea that agents, rather than humans, will burn through most of the new computing power to carry out myriad tasks for businesses and consumers. To justify that kind of investment, the use of AI agents would need to increase by around 2,000% to 3,000%.
So far, the general public seems wary of agents. Out of the 320 million trips processed by Uber Technologies Inc. and Uber Eats over the week of Halloween, just 500 food deliveries and four ride-hailing trips were made via agent, the company’s Chief Financial Officer Balaji Krishnamurthy said this week on an earnings call. “It is very, very early and very limited traction that we have seen so far,” he added.
Meta Platform Inc.’s Muse as been one of the more successful agents, hitting 10 million users in the US and Canada within about six weeks of its launch, with more than a quarter of those people using it daily, often to help them shop for online deals. (Muse isn’t available in Europe yet.) It also has a retention rate of 30% after one week, better than that of ChatGPT, according to market intelligence firm Sensor Tower.
But Muse has problems. It can’t access services from Apple Inc., Amazon.com Inc. or several large banks, and some users have found themselves babysitting the agent — verifying that it read a bill properly or confirming items in a cart before purchase — not least because it can hallucinate.
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A software engineer named Ugo Nwune recently complained on Threads that Muse had sent him a message about how it couldn’t find an email from Anthropic PBC in his Gmail account, even though he hadn’t asked it to do so. Nwune found the incident disconcerting enough to stop using Muse. Meta later said that a bug caused the model to hallucinate and that the issue had been fixed.
Businesses face the same trust challenge. The promise of generative AI is that it can offer general-purpose assistance and deal with unpredictable problems. But Gartner has found that AI agents work best when designed for very specific tasks — like matching invoices with purchase orders or handling payroll questions — rather than for broadly autonomous work.
If agents are most effective when they follow clear rules, that begs an uncomfortable question: Can’t cheaper, traditional software do that? The industry’s obsession with expanding AI’s capabilities has, in some cases, meant its customers are needlessly replacing old automation with expensive new tech. Agents may still be a hammer looking for a nail.
That echoes a broader, perennial problem with the business of AI. Making general-purpose technology that’s as revolutionary as electricity can create decision paralysis and leave end users unsure of where and how to use it. Tech firms should focus more on giving their enterprise customers guidance on where agents can solve real problems.
Consumer-focused agents already do this to some extent. Meta’s Muse has an “Ideas” tab that suggests ways it can assist you based on your past conversations and activity or broader goals you’ve shared. San Francisco-based Wabi, another AI agent for consumers, creates separate mini-apps with images or maps for each task that it carries out, while inviting friends who are also using the app to message the agent and each other about those tasks.
The app might create a text thread where friends can vote on where to meet for a weekend dinner, sharing a map and suggested itinerary. Agents should be “more than just text” and include visuals and apps, communicating with users in ways that are already familiar, like a group chat, says Wabi’s founder, Eugenia Kuyda.
She’s turned an agent into a communications and admin hub for herself, creating separate text threads with her family members to organize activities. For instance, Wabi asks Kuyda and her boyfriend a question about their relationship each day, and only reveals their answers when they’ve both replied.
The people building agents probably need to think more creatively to offer similarly fresh and unusual functions. Consider how social media sites introduced new ways of interacting through likes and comments. Agents, too, could usher in useful ways of working or communicating that we haven’t considered before.
But the way people and businesses are using agents today points to a more modest future than the one sketched out by the industry’s grand (and expensive) predictions. The winning products might not take over broad swathes of our lives, but rather allow us to hand off narrow, specific kinds of drudgery. That means agents may still be a long way from the demand explosion needed to justify a multi-trillion-dollar AI buildout. The tech industry must find more nails for its increasingly expensive hammer.
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