Home Business The AI ​​revolution is coming. But not as fast as some people think.

The AI ​​revolution is coming. But not as fast as some people think.

The AI ​​revolution is coming.  But not as fast as some people think.

Lori Beer, global chief information officer at JPMorgan Chase, talks about the latest artificial intelligence with the enthusiasm of a convert. She refers to AI chatbots like ChatGPT, with their ability to produce everything from poetry to computer programs, as “transformative” and a “paradigm shifter.”

But it won’t be coming to the country’s largest bank any time soon. JPMorgan blocked access to ChatGPT from its computers and told its 300,000 workers not to enter any banking information into the chatbot or other generative artificial intelligence tools.

For now, Beer said, there are too many risks of sensitive data leaks, questions about how the data is used, and about the accuracy of AI-generated responses. The bank has created a private, walled-off network to allow a few hundred data scientists and engineers to experiment with the technology. They are exploring uses such as automating and improving technical support and software development.

Throughout corporate America, the outlook is much the same. Generative AI, the software engine behind ChatGPT, is seen as an exciting new wave of technology. But companies across all industries are primarily testing technology and thinking about economics. Its widespread use in many companies could take years.

According to projections, generative AI could significantly boost productivity and add trillions of dollars to the global economy. However, the lesson of history, from steam power to the Internet, is that there is a long lag between the arrival of important new technologies and their widespread adoption, which is what transforms industries and helps drive the economy.

Take the Internet. In the 1990s, there were sure predictions that the Internet and the web would disrupt the retail, advertising, and media industries. Those predictions turned out to be true, but that was more than a decade later, long after the dot-com bubble had burst.

During that time, technology improved and costs fell, so bottlenecks disappeared. Broadband Internet connections eventually became commonplace. User-friendly payment systems were developed. Audio and video transmission technology has improved a lot.

What prompted the development was a flood of money and a flurry of entrepreneurial trial and error.

“This time we’re going to see a similar gold rush,” said Vijay Sankaran, chief technology officer at Johnson Controls, a large provider of construction equipment, software and services. “We will see a lot of learning.”

The investment frenzy is already underway. In the first half of 2023, funding for generative AI startups hit $15.3 billion, nearly three times the total for all of last year, according to PitchBook, which tracks seed investments.

Corporate technology managers are testing generative AI software from a host of vendors and watching the industry move.

In November, when ChatGPT became available to the public, it was a “Netscape moment” for generative AI, said Rob Thomas, IBM’s chief business officer, referring to Netscape’s introduction of the browser in 1994. “That brought ChatGPT to life. Internet”. Mr Thomas said. But it was only the beginning, since it opened a door to new business opportunities that took years to explode.

In a recent report, the McKinsey Global Institute, the consultancy’s research arm, included a timeline for the widespread adoption of generative AI applications. It was a constant improvement on currently known technology, but no future advances. Its forecast for widespread adoption was neither short nor accurate: a range of eight to 27 years.

The wide range is explained by connecting different assumptions about business cycles, government regulation, corporate cultures, and management decisions.

“Here we are not modeling the laws of physics; we are shaping economies and societies, people and companies,” said Michael Chui, a fellow at the McKinsey Global Institute. “What happens is largely the result of human decisions.”

Technology spreads throughout the economy through people, who contribute their skills to new industries. A few months ago, Davis Liang left an AI group in Meta to join Abridge, a healthcare startup that records and summarizes patient visits to doctors. Its generative AI software can save doctors hours of writing patient notes and billing reports.

Mr. Liang, a 29-year-old computer scientist, has authored scientific papers and helped build so-called large language models that animate generative AI.

Your skills are in demand these days. Liang declined to say, but people with his background and experience in generative AI startups typically receive a base salary of more than $200,000, and stock grants can potentially push total compensation much higher.

Abridge’s main appeal, Liang said, was to apply the “super-powerful tool” of AI to healthcare and “improve doctors’ working lives.” He was recruited by Zachary Lipton, a former research scientist in Amazon’s artificial intelligence group who is an assistant professor at Carnegie Mellon University. Lipton joined Abridge earlier this year as chief scientific officer.

“We’re not working on ads or anything like that,” Lipton said. “There’s a level of satisfaction when you get thank you letters from doctors every day.”

Important new technologies are engines of continuous innovation, spawning start-ups that create applications to make the underlying technology useful and accessible. In its early years, the personal computer was viewed as a hobbyist’s toy. But the creation of the spreadsheet program, the “star application” of its day, made the PC an essential tool in business.

Sarah Nagy led a data science team at Citadel, a giant investment firm, in 2020 when she first played with GPT-3. It took more than two years before OpenAI released ChatGPT. But the power of fundamental technology became apparent in 2020.

Ms. Nagy was particularly impressed by the software’s ability to generate computer code from text commands. She thought that could help democratize data analytics within companies, making it widely accessible to entrepreneurs rather than an elite group.

In 2021, Nagy founded Seek AI to pursue that goal. The New York start-up now has about two dozen clients in the technology, retail and financial industries, most working on pilot projects.

Using Seek AI’s software, a retail manager, for example, could write questions about product sales, ad campaigns, and online versus in-store performance to guide marketing strategy and spending. The software then transforms the words into a computer-encoded query, searches the company’s data warehouse, and returns responses in text or retrieves the relevant data.

Entrepreneurs, Nagy said, can get answers almost instantly, or in a day instead of a couple of weeks, if they have to make a request for something that requires the attention of a member of a data science team.

“At the end of the day, we’re trying to reduce the time it takes to get a response or useful data,” Ms. Nagy said.

Saving time and optimizing work within companies are the first major goals of generative AI in most companies. New products and services will come later.

Earlier this year, JPMorgan registered IndexGPT as a possible name for a generative AI-powered investment advice product.

“That’s something we’ll see and continue to evaluate over time,” said Beer, the bank’s technology lead. “But it’s not close to launch yet.”


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