Latest news with #Brynjolfsson
Techday NZ
14-05-2025
- Business
- Techday NZ
OpenAI forum explores AI's economic impact and direction
At the recent forum hosted by OpenAI, Chief Product Officer Kevin Weil and Stanford professor Erik Brynjolfsson explored the challenges, opportunities, and economic implications of artificial intelligence, offering candid reflections on AI's role in productivity, policy, and how it complements or competes with human labour. Brynjolfsson, a leading voice on the economics of technological change, acknowledged the ongoing debate about whether AI is delivering tangible gains. "Right now, if you look at the official productivity statistics last quarter, it was 1.2 percent, which is not that impressive," he said. "In the 90s, it was more than twice as high. In the early 2000s, it was more than twice as high." He argued that the current underwhelming figures are partly a result of how value is measured. "GDP measures a lot of things, but it doesn't do a good job of measuring things that have zero price," he said, citing digital goods like ChatGPT and Wikipedia, which generate value without costing users money. The other key issue, Brynjolfsson suggested, is structural. "These general purpose technologies... require re-skilling, changing your business processes, figuring out better ways of using the technology," he explained. This delay in payoff is what he and others call the "productivity J-curve". However, he was cautiously optimistic: "I think it's happening a lot quicker this time." Weil compared previous technological transitions—such as electricity and the internet—to the adoption of AI, noting that AI tools like ChatGPT require far less specialised knowledge. "You don't need to learn a new arcane coding language," he said. "It does... maybe you have to learn a little bit of prompting." The conversation turned to the potential for AI to disrupt existing business structures by empowering new entrants. "Can they make the cycle go faster because they're actually able to punch above their weight class?" Weil asked. Brynjolfsson concurred but noted that America's rate of business dynamism is decreasing. "There are actually fewer startups... nationwide. And there's less movement between companies, there's less geographic mobility." To measure AI's value beyond traditional economic indicators, Brynjolfsson described a new approach: "We've introduced a tool called GDP-B. The B stands for measuring the benefits rather than the costs." Using online choice experiments, his team estimates the consumer surplus of digital goods by asking participants how much compensation they would require to forgo a digital service for a time. "It's meant to be a representative market basket of what's in the economy," he said. Both speakers also questioned how society currently benchmarks intelligence in AI. Weil noted that evaluations like GPQA aim to assess AI models by comparing them to talented graduate students. "But that's not necessarily the right way to think about some of these models," he said. Brynjolfsson took the critique further: "With all due respect to my fellow humans, we are not the most general kind of intelligence." He advocated for benchmarks that measure intelligence beyond human-like capabilities. "There are all sorts of other kinds of intelligence... And it's not just an intellectual debate. It has to do with the direction of technology." The discussion also touched on the risks of over-centralising AI. Brynjolfsson warned of a future where a single AI system might dominate information and decision-making: "Maybe that will be more efficient if you have enough processing power. But... the humans wouldn't have a lot of bargaining power." Weil countered by highlighting the fragmented nature of data access. "No public model... will have access to all of the data that's relevant to solve the totality of problems... The vast majority of the world's data is private." This, he argued, makes it likely that multiple models will always coexist. In discussing trust in AI, Brynjolfsson offered a candid anecdote: "There was an article... where they had three treatments: the human-only, the AI-only, and the doctor plus the AI. And... the doctor plus the AI did worse than the AI alone." He attributed this to current systems being insufficiently interpretable. "They have to be able to trust and know... if the AI system just says, 'cut off the patient's left leg,' and the doctor's like, 'why?'... it's got to explain all the reasoning." As the event closed, both speakers emphasised the importance of supporting innovation through infrastructure like OpenAI's API. "Every time we drop the price and offer more intelligence, people can solve more problems," said Weil. Brynjolfsson emphasised the same idea: "Some people derisively call these things wrappers... Actually, I think that's where a ton of the value is going to be coming... customised for a particular vertical." In sum, the discussion underscored that while AI holds the potential to dramatically shift productivity and economic structures, its full impact will depend on how it is adopted, measured, and integrated with human capabilities.
Mint
26-04-2025
- Business
- Mint
Companies are struggling to drive a return on AI. It doesn't have to be that way.
AI adoption among companies is stunningly high, but most of them are struggling to put it to good use. They intuit that AI is essential to their future. Yet intuition alone won't unlock the promise of AI, and it isn't clear to them which key will do the trick. As of last year, 78% of companies said they used artificial intelligence in at least one function, up from 55% in 2023, according to global management consulting firm McKinsey's State of AI survey, released in March. From these efforts, companies claimed to typically find cost savings of less than 10% and revenue increases of less than 5%. While the measurable financial return is limited, business is nonetheless all-in on AI, according to the 2025 AI Index report released in April by the Stanford Institute for Human-Centered Artificial Intelligence. Last year, private generative AI investment alone hit $33.9 billion globally, up 18.7% from 2023. The numbers reflect a 'productivity paradox," in which massive improvements in AI capabilities haven't led to a corresponding surge in national-level productivity, according to Stanford University economist and professor Erik Brynjolfsson, who worked on the AI Index. While some specific projects have been enormously productive, 'many companies are disappointed with their AI projects." For companies to get the most out of their AI efforts, Brynjolfsson advocates for a task-based analysis, in which a company is broken down into fine-grained tasks or 'atomic units of work" that are evaluated for potential AI assistance. As AI is applied, the results are measured against key performance indicators, or KPIs. He co-founded a startup, Workhelix, that applies those principles. Companies should take care to target an outcome first, and then find the model that helps them achieve it, says Scott Hallworth, chief data and analytics officer and head of digital solutions at HP. A separate report from McKinsey issued in January helps explain why AI adoption is racing ahead of associated productivity gains, according to Lareina Yee, senior partner and director at the McKinsey Global Institute. Only 1% of U.S. companies that have invested in AI report that they have scaled their investment, while 43% report that they are still in the pilot stage. 'One cannot expect significant productivity gains at the pilot level or even at the company unit level. Significant productivity improvements require achieving scale," she said. The critical question then, is how companies can best scale their AI efforts. Ryan Teeples, chief technology officer of 1-800Accountant, agrees that 'breaking work into AI-enabled tasks and aligning them to KPIs not only drives measurable ROI, it also creates a better customer experience by surfacing critical information faster than a human ever could." The privately held company based in New York provides tax, booking and payroll services to 50,000 active clients, with a focus on small businesses. The company isn't a Workhelix customer. Additionally, he says, companies should look beyond individualized AI usage, in which employees use GenAI chatbots or AI-equipped productivity tools to enhance their work. 'True enterprise adoption…involves orchestration and scaling across the organization. Very few organizations have truly reached this level, and even those are only scratching the surface," he said. The use of AI at 1-800Accountant begins with an assessment of whether the technology improves the client experience. If the AI provides customers with answers that are as good, better or faster than a human, it's a good use case, according to Teeples. In the past, the company scheduled hourlong appointments with advisers who answered simple client questions, such as the status of their tax return. Now, the company uses an AI agent connected to curated data sources to address 65% of customer inquiries, with 30% arranging a call with a human. (The remaining 5% drop out of the inquiry process for various reasons.) The company uses Salesforce's Agentforce to handle customer inquiries and its Einstein platform for orchestration across 1-800Accountant's back end. Teeples said the company is saving money on the cost of human advisers. 'The ROI in this case was abundantly clear," he said. Orchestrating AI across the enterprise requires the right infrastructure, especially when it comes to data, according to Gabrielle Tao, senior vice president for data cloud at Salesforce. It is important, she said, to harmonize data, for example, by creating a consistent way to refer to business concepts such as 'orders" and 'transactions," regardless of the underlying data source. AI deployments should target tasks that are both frequent and generalizable, according to Walter Sun, global head of artificial intelligence at SAP. Infrequent, highly specific tasks such as a marketing campaign for a single event might benefit from AI, but applying AI to regularly occurring tasks will achieve a more consistent ROI, he said. Historically, it has taken years for the world to figure out what to do with revolutionary general-purpose technologies including the steam engine and electricity, according to Brynjolfsson. It isn't unusual for general-purpose models to follow a 'J-curve," in which there's a dip in initial productivity, as businesses figure things out, followed by a ramp-up in productivity. He says companies are beginning to turn the corner of the AI J-curve. The transformation may occur faster than in the past, because businesses—under no small amount of pressure from investors—are working to quickly justify the massive amount of capital pouring into AI. Write to Steven Rosenbush at
