The crowd at that night’s GPT-4 hackathon was so large as to render the Wi-Fi basically nonfunctional. Every room overflowed with hackers crowding around whiteboards. In the kitchen, Chinese takeout was laid out on a table. A smattering of investors were present to check out the demos, which started at 8 p.m. with short speeches from the organizers. The speeches were all variations on a theme: We are living in a momentous time. Maybe in a few decades from now, we’ll look back at all these seminal A.I. achievements and see that they all came from this house in Hillsborough.
As at HF0, the demos here alternated between business uses and personal applications — a chatbot that impersonates business gurus like Mark Cuban, the owner of the Dallas Mavericks basketball team and a judge on “Shark Tank,” the business-reality TV show, and that allows you to ask for business advice; or an A.I. sommelier that will take your dinner menu and suggest an appropriate wine pairing. Six months ago, any one of these projects might have seemed remarkable, but the arrival of ChatGPT has remade expectations. “The one pattern I’m starting to see is that ChatGPT is the killer app,” the technologist Diego Basch has written on Twitter. “None of the tools built on top of the A.P.I. have been as useful to me.” Indeed, if you are building something on top of OpenAI’s A.P.I., it does seem as though your app’s marginal value has to be extremely high if it is to avoid being bulldozed by either OpenAI itself or one of the big tech companies like Google and Microsoft (or even later-stage start-ups that are rapidly rolling out A.I.-enabled features in their products).
As two analysts at N.E.A., an investment firm, put it in a recent report, generative A.I. may not be as disruptive to established businesses, and beneficial to start-ups, as previous big shifts in tech platforms. “Unlike with the prior shifts, incumbents do not need to re-architect their entire products to adopt this new platform shift,” the analysts wrote. “In addition, this shift favors companies with bigger, proprietary data sets which can give an edge to more established companies.”
During previous tech eras, start-ups could introduce a superior technology or interface and then race to build market share before entrenched competitors could match them. But with large language models, incumbents like Google and Microsoft have had a huge head start in both developing the technology and acquiring market share among consumers. The situation risks becoming like that of the pharmaceutical industry, in which research and development is outsourced to start-ups and many of the benefits ultimately accrue to the parent company. Moreover, the capital-intensive nature of training large language models means that smaller companies like OpenAI and Anthropic creating their own large language models have few alternatives beyond making Faustian “partnerships” with tech giants.
This doesn’t mean that generative A.I. isn’t going to transform industries or eliminate jobs. Beyond the incumbents, one beneficiary might well be the indie hacker, the kind of coder for hire who does niche A.I. projects to fill specific needs in specific industries. Problems that have been too esoteric to solve or work flows that have been too complicated to improve might become easily automated with the help of ChatGPT. As the Gumroad founder Sahil Lavingia recently put it on a podcast, “If I had a friend who’s like, ‘I want to make 200K a year, building something in S.A.S.’” — software as a service — “ ‘building some A.I. tool,’ I would basically tell them to walk around their neighborhood, go to as many businesses as possible and see what manual thing, what piece of paper you sign, and figure out how to automate that.”
Source: | This article originally belongs to Nytimes.com
