Generative AI big year meant profit for Nvidia, experiments elsewhere

Generative AI big year meant profit for Nvidia, experiments elsewhere

Sam Altman, CEO of OpenAI, at the Hope Global Forums annual meeting in Atlanta on Dec. 11, 2023.

Dustin Chambers | Bloomberg | Getty Images

OpenAI CEO Sam Altman has admitted that he was surprised by the popularity of ChatGPT, which was released as a research project a little more than a year ago. His team had spent an entire meeting debating whether it was worth even opening up the chatbot to the public.

As it turns out, OpenAI’s decision to launch ChatGPT in November 2022 became the defining moment for generative artificial intelligence and set the stage for a rush of investments and a mountain of new products and services in 2023.

Some form of generative AI has made its way into virtually every industry, from financial services to biomedical research. Some 95% of utility and energy companies are discussing using generative AI algorithms, according to a July survey

To see the financial effect of the generative AI rush, you need only to look at Nvidia’s bottom line. The chipmaker’s graphics processing units, or GPUs, are at the heart of the large language models created by OpenAI as well as those from Alphabet, Meta and a growing crop of heavily funded startups all battling for a slice of the generative AI pie.

Through the first three quarters of 2023, Nvidia generated $17.5 billion in net income, up more than sixfold from a year earlier. Its stock price jumped 237% this year, far exceeding any other member of the S&P 500.

Generative AI quickly became the buzzy phrase for corporate earnings calls, as every company needed a narrative. Sometimes, the story was painful, such as when digital education company Chegg said in May that it was seeing a “significant spike in student interest in ChatGPT,” which appears to be “having an impact on our new customer growth rate.” The stock plunged 48% in one day following the warning.

Perhaps no company was less prepared for the generative AI boom than OpenAI itself. In November, Altman was suddenly ousted by the board for a dispute that reportedly had to do with his aggressive push to develop new commercial products at the expense of safety. However, Altman quickly returned to the helm after employees threatened to flee and large investors banded together to fight for his reinstatement.

The public spat included a reshuffling of OpenAI’s board and shined a bright light on the debate raging between AI skeptics and evangelists. While advances in generative AI showcase the potential for technology to unlock all sorts of business opportunities and efficiencies, fears of the algorithms’ perceived power gained equal resonance. Some of the real-life harms for minorities and vulnerable populations showed up in FTC proposals, wrongful arrests, contaminated datasets and more.

Here were some of the key areas for generative AI advancements in 2023: 

The Anthropic website on a laptop arranged in New York on Aug. 15, 2023.

Gabby Jones | Bloomberg | Getty Images

Chatbots

ChatGPT opened up the floodgates for investments in chatbots, as it became clear how the input of a few words could produce more thorough and creative responses than ever before.

Nearly two months after launch, ChatGPT broke records as the fastest-growing consumer app in history — until Meta’s Threads dethroned it last summer. ChatGPT now has about 100 million weekly active users, along with more than 92% of Fortune 500 companies using the platform, according to OpenAI. 

Earlier this year, Microsoft plunged an additional $10 billion in the company, making it the biggest AI investment of the year, according to PitchBook, and OpenAI is in talks to sell employee shares at a price that would suggest a valuation of $86 billion. According to Bloomberg, the company is in discussions to potentially raise capital at a valuation of $100 billion or more.

Google was caught off guard by ChatGPT’s success and responded by accelerating the public release of its Bard chatbot, powered by its LLM called LaMDA, which stands for Language Model for Dialogue Applications.

Google has been rolling out new Bard features, including integrations with Google Search and a YouTube extension, and recently released Gemini, the new and buzzy AI model to power Bard. Gemini’s launch this month involved a marketing blitz and controversy over an edited video promoting the model’s capabilities. 

In addition to its internal investments, Google is one of many big names behind Anthropic, an AI startup that’s currently in talks to raise $750 million at a valuation of $18.4 billion. Founded by former OpenAI research executives, Anthropic is the developer of the chatbot Claude.

In July, Anthropic debuted Claude 2, and said it has the ability to summarize up to about 75,000 words, which could be the length of a book. Users can input large datasets and ask for summaries in the form of a memo, letter or story.

As a category, new generative AI chatbots have been used this year to answer questions about business strategy, as well as to design study guides, offer advice on salary negotiation and spark creative writing prompts. They’ve even assisted in writing wedding vows. 

“It’s probably one of the most influential step function changes in technology that we’ve seen,” Jill Chase, investment partner at CapitalG Ventures, told CNBC. Chase said it’s up there with the dawn of the internet and the shift to mobile. “Things like that just open up people’s imaginations,” she said.

Academics and ethicists have voiced significant concerns about the technology’s tendency to fabricate information and to propagate bias. Still, it has quickly made its way into schools, online travel, the medical industry, digital advertising and beyond. Microsoft and IBM have invested increasing amounts in enterprise AI offerings, including development studios for companies to personalize the use of LLMs. 

There are plenty of detractors.

Publishers, artists, writers and technologists have been pushed to pursue legal action against companies behind popular generative AI tools, out of concern that their creative content is being used as free training data. John Grisham, George R.R. Martin and other prominent authors sued OpenAI in September over alleged copyright infringement.

This photo taken on Jan. 31, 2023, shows an artificial intelligence manga artist, who goes by the name Rootport, wearing gloves to protect his identity, demonstrating how he produces AI manga during an interview with AFP in Tokyo.

Richard A. Brooks | Afp | Getty Images

Image and video generation 

Generative AI for images and video emerged in 2022, due to powerful image generators such as OpenAI’s DALL-E 2, Stable Diffusion and Midjourney, and video-generation AI tools from Meta, Google and Amazon

While interest in those technologies continues, progress has waned compared to chatbots, according to Brendan Burke, an analyst at PitchBook. 

“Multimedia content generation has fallen behind language in the pace of progress,” Burke told CNBC. “The initial excitement with Stable Diffusion in 2022 exposed both the general interest but also the drawbacks of AI content generation. Progress has been incremental this year, yet still disappointing for the most sophisticated content creators.”

Meta’s Instagram recently debuted a feature that allows users to change the background of Stories posts using AI. Google and Amazon have incorporated generative AI tools into advertising technology to create more appealing marketing images.

Some industry leaders say the future of generative AI is “multimodal,” bringing the various mediums together.

“The world is multimodal,” Brad Lightcap, OpenAI’s operating chief, told CNBC in a recent interview. “If you think about the way we as humans process the world and engage with the world, we see things, we hear things, we say things. The world is much bigger than text. So to us, it always felt incomplete for text and code to be the single modalities, the single interfaces that we could have to how powerful these models are and what they can do.”

Agents and assistants

After the chatbot comes the agent.

It’s not just about getting sophisticated answers, but it’s also about using generative AI to be productive in completing tasks. That could be scheduling a group hangout by scanning everyone’s calendar to make sure there are no conflicts, booking travel and activities, buying presents for loved ones or doing a specific job function such as outbound sales.

Last month, OpenAI announced custom GPTs, or customized, niche versions of ChatGPT that users can personalize for getting travel recommendations, recipe help or startup advice. However, the company chose to delay the release of the platform that would popularize different use cases — the “GPT store” — until next year. 

One type of AI assistant that has gained popularity is for coding. Take, for example, Microsoft’s GitHub coding repository. GitHub CEO Thomas Dohmke wrote in a blog post earlier this year that an average of 46% of all code on GitHub, “across all programming languages,” was AI generated.

Last month, GitHub introduced a more expensive version of its Copilot assistant that can explain and provide recommendations about internal source code.

“Copilot, when it started at the very beginning, was thought to be a tool that could help developers write docs,” Kyle Daigle, GitHub’s chief operating officer, told CNBC in an interview. In the past year, he said, the company has expanded the technology, looking for more places “to help developers collaborate and work together and solve problems outside of just the code.” 

But PitchBook’s Burke said coding assistants are in their very early days and currently can only do “a small part” of a developer’s work. That’s true in the broader world, he said. 

“Users have found how little AI can do for them this year,” he said. “AI knows a lot, but it can’t do a lot yet. We’re still far away from AI truly being able to do the complex tasks that people are used to doing in their personal lives and at work. That has been shown by the struggles of AI agents this year.” 

Nvidia CEO Jensen Huang speaks at the Supermicro keynote presentation during the Computex conference in Taipei on June 1, 2023.

Walid Berrazeg | Sopa Images | Lightrocket | Getty Images

Looking ahead 

Overall, 2023 was a big year for consumer excitement surrounding generative AI and for adoption of a few popular products. But business success stories have been few and far between.

“It was an especially transformative year from a consumer perspective where AI became much more tangible than before,” Grace Isford, a partner at Lux Capital, said in an interview. “AI is nothing new, but the awareness — and in turn, the adoption — has skyrocketed. Many more hackers and builders are leveraging the technology and the really exciting advancements into products.” 

CapitalG’s Chase said the consumer fascination with the space has allowed people to “see what was possible” in AI, allowing for a “cake tasting” of sorts and a teasing of the imagination.

Early in the year, people “extrapolated out early exploration of that technology into lasting and enduring use cases,” Chase said. She added that there hasn’t been a straight line from early adoption and widespread use of one or two products to mainstream popularity. Companies and developers are now going back to doing research and development to “build the right infrastructure and tooling” that can hopefully lead to mass adoption.

“I think that will happen over the next year,” she said. “I think some people thought it would happen this year.”

In 2023, it’s clear that the overwhelming beneficiary from all the hype was Nvidia. The challenge for the coming year and beyond is for businesses to show that their hefty spending on those advanced GPUs and the models they power can lead to the development of products that allow more companies to share in the wealth.

“I thought that the excitement at the end of last year would quickly translate into enterprise adoption, but the reality is that very few companies have launched generative AI applications into production and experiments aren’t quickly translating into reliable applications,” Burke said. “We’re still looking at an outlook where companies may not widely deploy products until later next year or even the following year.”

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