But these image-generating programs — which look like toys today — could be the start of a big wave in technology. Technologists call them generative models, or generative AI.
“In the last three months, the words ‘generative AI’ went from, ‘no one even discussed this’ to the buzzword du jour,” said David Beisel, a venture capitalist at NextView Ventures.
In the past year, generative AI has gotten so much better that it’s inspired people to leave their jobs, start new companies and dream about a future where artificial intelligence could power a new generation of tech giants.
The field of artificial intelligence has been having a boom phase for the past half-decade or so, but most of those advancements have been related to making sense of existing data. AI models have quickly grown efficient enough to recognize whether there’s a cat in a photo you just took on your phone and reliable enough to power results from a Google search engine billions of times per day.
But generative AI models can produce something entirely new that wasn’t there before — in other words, they’re creating, not just analyzing.
“The impressive part, even for me, is that it’s able to compose new stuff,” said Boris Dayma, creator of the Craiyon generative AI. “It’s not just creating old images, it’s new things that can be completely different to what it’s seen before.”
Sequoia Capital — historically the most successful venture capital firm in the history of the industry, with early bets on companies like Apple and Google — says in a blog post on its website that “Generative AI has the potential to generate trillions of dollars of economic value.” The VC firm predicts that generative AI could change every industry that requires humans to create original work, from gaming to advertising to law.
In a twist, Sequoia also notes in the post that the message was partially written by GPT-3, a generative AI that produces text.
How generative AI works
Kif Leswing/Craiyon
Image generation uses techniques from a subset of machine learning called deep learning, which has driven most of the advancements in the field of artificial intelligence since a landmark 2012 paper about image classification ignited renewed interest in the technology.
Deep learning uses models trained on large sets of data until the program understands relationships in that data. Then the model can be used for applications, like identifying if a picture has a dog in it, or translating text.
Image generators work by turning this process on its head. Instead of translating from English to French, for example, they translate an English phrase into an image. They usually have two main parts, one that processes the initial phrase, and the second that turns that data into an image.
The first wave of generative AIs was based on an approach called GAN, which stands for generative adversarial networks. GANs were famously used in a tool that generates photos of people who don’t exist. Essentially, they work by having two AI models compete against each other to better create an image that fits with a goal.
Newer approaches generally use transformers, which were first described in a 2017 Google paper. It’s an emerging technique that can take advantage of bigger datasets that can cost millions of dollars to train.
The first image generator to gain a lot of attention was DALL-E, a program announced in 2021 by OpenAI, a well-funded startup in Silicon Valley. OpenAI released a more powerful version this year.
“With DALL-E 2, that’s really the moment when when sort of we crossed the uncanny valley,” said Christian Cantrell, a developer focusing on generative AI.
Another commonly used AI-based image generator is Craiyon, formerly known as Dall-E Mini, which is available on the web. Users can type in a phrase and see it illustrated in minutes in their browser.
Since launching in July 2021, it’s now generating about 10 million images a day, adding up to 1 billion images that have never existed before, according to Dayma. He’s made Craiyon his full-time job after usage skyrocketed earlier this year. He says he’s focused on using advertising to keep the website free to users because the site’s server costs are high.
For example, Stable Diffusion was integrated into Adobe Photoshop through a plug-in, allowing users to generate backgrounds and other parts of images that they can then directly manipulate inside the application using layers and other Photoshop tools, turning generative AI from something that produces finished images into a tool that can be used by professionals.
“I wanted to meet creative professionals where they were and I wanted to empower them to bring AI into their workflows, not blow up their workflows,” said Cantrell, developer of the plug-in.
Cantrell, who was a 20-year Adobe veteran before leaving his job this year to focus on generative AI, says the plug-in has been downloaded tens of thousands of times. Artists tell him they use it in myriad ways that he couldn’t have anticipated, such as animating Godzilla or creating pictures of Spider-Man in any pose the artist could imagine.
“Usually, you start from inspiration, right? You’re looking at mood boards, those kinds of things,” Cantrell said. “So my initial plan with the first version, let’s get past the blank canvas problem, you type in what you’re thinking, just describe what you’re thinking and then I’ll show you some stuff, right?”
An emerging art to working with generative AIs is how to frame the “prompt,” or string of words that lead to the image. A search engine called Lexica catalogs Stable Diffusion images and the exact string of words that can be used to generate them.
Startups, cloud providers, and chip makers could thrive
Image generated by DALL-E with prompt: A cat on sitting on the moon, in the style of Pablo Picasso, detailed, stars
Screenshot/OpenAI
Some investors are looking at generative AI as a potentially transformative platform shift, like the smartphone or the early days of the web. These kinds of shifts greatly expand the total addressable market of people who might be able to use the technology, moving from a few dedicated nerds to business professionals — and eventually everyone else.
“It’s not as though AI hadn’t been around before this — and it wasn’t like we hadn’t had mobile before 2007,” said Beisel, the seed investor. “But it’s like this moment where it just kind of all comes together. That real people, like end-user consumers, can experiment and see something that’s different than it was before.”
Cantrell sees generative machine learning as akin to an even more foundational technology: the database. Originally pioneered by companies like Oracle in the 1970s as a way to store and organize discrete bits of information in clearly delineated rows and columns — think of an enormous Excel spreadsheet, databases have been re-envisioned to store every type of data for every conceivable type of computing application from the web to mobile.
“Machine learning is kind of like databases, where databases were a huge unlock for web apps. Almost every app you or I have ever used in our lives is on top of a database,” Cantrell said. “Nobody cares how the database works, they just know how to use it.”
Michael Dempsey, managing partner at Compound VC, says moments where technologies previously limited to labs break into the mainstream are “very rare” and attract a lot of attention from venture investors, who like to make bets on fields that could be huge. Still, he warns that this moment in generative AI might end up being a “curiosity phase” closer to the peak of a hype cycle. And companies founded during this era could fail because they don’t focus on specific uses that businesses or consumers would pay for.
Others in the field believe that startups pioneering these technologies today could eventually challenge the software giants that currently dominate the artificial intelligence space, including Google, Facebook parent Meta and Microsoft, paving the way for the next generation of tech giants.
“There’s going to be a bunch of trillion-dollar companies — a whole generation of startups who are going to build on this new way of doing technologies,” said Clement Delangue, the CEO of Hugging Face, a developer platform like GitHub that hosts pre-trained models, including those for Craiyon and Stable Diffusion. Its goal is to make AI technology easier for programmers to build on.
Some of these firms are already sporting significant investment.
Hugging Face was valued at $2 billion after raising money earlier this year from investors including Lux Capital and Sequoia; and OpenAI, the most prominent startup in the field, has received over $1 billion in funding from Microsoft and Khosla Ventures.
Meanwhile, Stability AI, the maker of Stable Diffusion, is in talks to raise venture funding at a valuation of as much as $1 billion, according to Forbes. A representative for Stability AI declined to comment.
Cloud providers like Amazon, Microsoft and Google could also benefit because generative AI can be very computationally intensive.
Meta and Google have hired some of the most prominent talent in the field in hopes that advances might be able to be integrated into company products. In September, Meta announced an AI program called “Make-A-Video” that takes the technology one step farther by generating videos, not just images.
“This is pretty amazing progress,” Meta CEO Mark Zuckerberg said in a post on his Facebook page. “It’s much harder to generate video than photos because beyond correctly generating each pixel, the system also has to predict how they’ll change over time.”
On Wednesday, Google matched Meta and announced and released code for a program called Phenaki that also does text to video, and can generate minutes of footage.
The boom could also bolster chipmakers like Nvidia, AMD and Intel, which make the kind of advanced graphics processors that are ideal for training and deploying AI models.
At a conference last week, Nvidia CEO Jensen Huang highlighted generative AI as a key use for the company’s newest chips, saying these kind of programs could soon “revolutionize communications.”
Profitable end uses for Generative AI are currently rare. A lot of today’s excitement revolves around free or low-cost experimentation. For example, some writers have been experimented with using image generators to make images for articles.
Prompt: “A cat sitting on the moon, in the style of picasso, detailed”
Screenshot/Craiyon
Ultimately, everyone developing generative AI will have to grapple with some of the ethical issues that come up from image generators.
First, there’s the jobs question. Even though many programs require a powerful graphics processor, computer-generated content is still going to be far less expensive than the work of a professional illustrator, which can cost hundreds of dollars per hour.
That could spell trouble for artists, video producers and other people whose job it is to generate creative work. For example, a person whose job is choosing images for a pitch deck or creating marketing materials could be replaced by a computer program very shortly.
“It turns out, machine-learning models are probably going to start being orders of magnitude better and faster and cheaper than that person,” said Compound VC’s Dempsey.
There are also complicated questions around originality and ownership.
Generative AIs are trained on huge amounts of images, and it’s still being debated in the field and in courts whether the creators of the original images have any copyright claims on images generated to be in the original creator’s style.
One artist won an art competition in Colorado using an image largely created by a generative AI called MidJourney, although he said in interviews after he won that he processed the image after choosing it from one of hundreds he generated and then tweaking it in Photoshop.
Some images generated by Stable Diffusion seem to have watermarks, suggesting that a part of the original datasets were copyrighted. Some prompt guides recommend using specific living artists’ names in prompts in order to get better results that mimic the style of that artist.
Image generators can also be used to create new images of trademarked characters or objects, such as the Minions, Marvel characters or the throne from Game of Thrones.
As image-generating software gets better, it also has the potential to be able to fool users into believing false information or to display images or videos of events that never happened.
Developers also have to grapple with the possibility that models trained on large amounts of data may have biases related to gender, race or culture included in the data, which can lead to the model displaying that bias in its output. For its part, Hugging Face, the model-sharing website, publishes materials such as an ethics newsletter and holds talks about responsible development in the AI field.
“What we’re seeing with these models is one of the short-term and existing challenges is that because they’re probabilistic models, trained on large datasets, they tend to encode a lot of biases,” Delangue said, offering an example of a generative AI drawing a picture of a “software engineer” as a white man.
CEO of Supermicro Charles Liang speaks during the Reuters NEXT conference in New York City, U.S., December 10, 2024.
Mike Segar | Reuters
PARIS — Super Micro plans to increase its investment in Europe, including ramping up manufacturing of its AI servers in the region, CEO Charles Liang told CNBC in an interview that aired on Wednesday.
The company sells servers which are packed with Nvidia chips and are key for training and implementing huge AI models. It has manufacturing facilities in the Netherlands, but could expand to other places.
“But because the demand in Europe is growing very fast, so I already decided, indeed, [there’s] already a plan to invest more in Europe, including manufacturing,” Liang told CNBC at the Raise Summit in Paris, France.
“The demand is global, and the demand will continue to improve in [the] next many years,” Liang added.
Liang’s comments come less than a month after Nvidia CEO Jensen Huang visited various parts of Europe, signing infrastructure deals and urging the region to ramp up its computing capacity.
Growth to be ‘strong’
Super Micro rode the growth wave after OpenAI’s ChatGPT boom boosted demand for Nvidia’s chips, which underpin big AI models. The server maker’s stock hit a record high in March 2024. However, the stock is around 60% off that all-time high over concerns about its accounting and financial reporting. But the company in February filed its delayed financial report for its 2024 fiscal year, assuaging those fears.
In May, the company reported weaker-than-expected guidance for the current quarter, raising concerns about demand for its product.
However, Liang dismissed those fears. “Our growth rate continues to be strong, because we continue to grow our fundamental technology, and we [are] also expanding our business scope,” Liang said.
“So the room … to grow will be still very tremendous, very big.”
Jeff Williams, chief operating officer of Apple Inc., during the Apple Worldwide Developers Conference (WWDC) at Apple Park campus in Cupertino, California, US, on Monday, June 9, 2025.
David Paul Morris | Bloomberg | Getty Images
Apple said on Tuesday that Chief Operating Officer Jeff Williams, a 27-year company veteran, will be retiring later this year.
Current operations leader Sabih Khan will take over much of the COO role later this month, Apple said in a press release. For his remaining time with the comapny, Williams will continue to head up Apple’s design team, Apple Watch, and health initiatives, reporting to CEO Tim Cook.
Williams becomes the latestlongtime Apple executive to step down as key employees, who were active in the company’s hyper-growth years, reach retirement age. Williams, 62, previously headed Apple’s formidable operations division, which is in charge of manufacturing millions of complicated devices like iPhones, while keeping costs down.
He also led important teams inside Apple, including the company’s fabled industrial design team, after longtime leader Jony Ive retired in 2019. When Williams retires, Apple’s design team will report to CEO Tim Cook, Apple said.
“He’s helped to create one of the most respected global supply chains in the world; launched Apple Watch and overseen its development; architected Apple’s health strategy; and led our world class team of designers with great wisdom, heart, and dedication,” Cook said in the statement.
Williams said he plans to spend more time with friends and family.
“June marked my 27th anniversary with Apple, and my 40th in the industry,” Williams said in the release.
Williams is leaving Apple at a time when its famous supply chain is under significant pressure, as the U.S. imposes tariffs on many of the countries where Apple sources its devices, and White House officials publicly pressure Apple to move more production to the U.S.
Khan was added to Apple’s executive team in 2019, taking an executive vice president title. Apple said on Tuesday that he will lead supply chain, product quality, planning, procurement, and fulfillment at Apple.
The operations leader joined Apple’s procurement group in 1995, and before that worked as an engineer and technical leader at GE Plastics. He has a bachelor’s degree from Tufts University and a master’s degree in mechanical engineering from Rensselaer Polytechnic Institute in upstate New York.
Khan has worked closely with Cook. Once, during a meeting when Cook said that a manufacturing problem was “really bad,” Khan stood up and drove to the airport, and immediately booked a flight to China to fix it, according to an anecdote published in Fortune.
Elon Musk, chief executive officer of SpaceX and Tesla, attends the Viva Technology conference at the Porte de Versailles exhibition center in Paris, June 16, 2023.
Gonzalo Fuentes | Reuters
Tesla CEO Elon Musk told Wedbush Securities’ Dan Ives to “Shut up” on Tuesday after the analyst offered three recommendations to the electric vehicle company’s board in a post on X.
Ives has been one of the most bullish Tesla observers on Wall Street. With a $500 price target on the stock, he has the highest projection of any analyst tracked by FactSet.
But on Tuesday, Ives took to X with critical remarks about Musk’s political activity after the world’s richest person said over the weekend that he was creating a new political party called the America Party to challenge Republican candidates who voted for the spending bill that was backed by President Donald Trump.
Ives’ post followed a nearly 7% slide in Tesla’s stock Monday, which wiped out $68 billion in market cap. Ives called for Tesla’s board to create a new pay package for Musk that would get him 25% voting control and clear a path to merge with xAI, establish “guardrails” for how much time Musk has to spend at Tesla, and provide “oversight on political endeavors.”
Ives published a lengthier note with other analysts from his firm headlined, “The Tesla board MUST Act and Create Ground Rules For Musk; Soap Opera Must End.” The analysts said that Musk’s launching of a new political party created a “tipping point in the Tesla story,” necessitating action by the company’s board to rein in the CEO.
Still, Wedbush maintained its price target and its buy recommendation on the stock.
“Shut up, Dan,” Musk wrote in response on X, even though the first suggestion would hand the CEO the voting control he has long sought at Tesla.
In an email to CNBC, Ives wrote, “Elon has his opinion and I get it, but we stand by what the right course of action is for the Board.”
Musk’s historic 2018 CEO pay package, which had been worth around $56 billion and has since gone up in value, was voided last year by the Delaware Court of Chancery. Judge Kathaleen McCormick ruled that Tesla’s board members had lacked independence from Musk and failed to properly negotiate at arm’s length with the CEO.
Tesla has appealed that case to the Delaware state Supreme Court and is trying to determine what Musk’s next pay package should entail.
Ives isn’t the only Tesla bull to criticize Musk’s continued political activism.
Analysts at William Blair downgraded the stock to the equivalent of a hold from a buy on Monday, because of Musk’s political plans and rhetoric as well as the negative impacts that the spending bill passed by Congress could have on Tesla’s margins and EV sales.
“We expect that investors are growing tired of the distraction at a point when the business needs Musk’s attention the most and only see downside from his dip back into politics,” the analysts wrote. “We would prefer this effort to be channeled towards the robotaxi rollout at this critical juncture.”
Trump supporter James Fishback, CEO of hedge fund Azoria Partners, said Saturday that his firm postponed the listing of an exchange-traded fund, the Azoria Tesla Convexity ETF, that would invest in the EV company’s shares and options. He began his post on X saying, “Elon has gone too far.”
“I encourage the Board to meet immediately and ask Elon to clarify his political ambitions and evaluate whether they are compatible with his full-time obligations to Tesla as CEO,” Fishback wrote.
Musk said Saturday that he has formed the America Party, which he claimed will give Americans “back your freedom.” He hasn’t shared formal details, including where the party may be registered, how much funding he will provide for it and which candidates he will back.
Tesla’s stock is now down about 25% this year, badly underperforming U.S. indexes and by far the worst performance among tech’s megacaps.
Musk spent much of the first half of the year working with the Trump administration and leading an effort to massively downsize the federal government. His official work with the administration wrapped up at the end of May, and his exit preceded a public spat between Musk and Trump over the spending bill and other matters.
Musk, Tesla’s board chair Robyn Denholm and investor relations representative Travis Axelrod didn’t immediately respond to requests for comment.