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When Vinod Khosla had a skiing accident in 2011 that led to an ACL injury in his knee, doctors gave conflicting opinions over his treatment. Frustrated with the healthcare system, the leading venture capitalist proffered, in a hotly debated article, that AI algorithms could do the job better than doctors. Since then, Khosla’s firm has invested in a number of robotics and medtech companies, including Rad AI, a radiology tech company. The self-professed techno-optimist still stands by his assertions a decade later. “Almost all expertise will be free in an AI model, and we’ll have plenty of these for the benefit of humanity,” he told TIME in an interview in August.

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One of Silicon Valley’s most prominent figures, Khosla, 69, co-founded the influential computing company Sun Microsystems in the 1980s, which he eventually sold to Oracle in 2010. His venture capital firm Khosla Ventures has subsequently placed big bets on green tech, healthcare, and AI startups around the world—including an early investment of $50 million in 2019 in OpenAI. When OpenAI’s CEO, Sam Altman, was briefly fired last year, Khosla was one of the investors who spoke out about wanting Altman back in the top job. “I was very vocal that we needed to get rid of those, frankly, EA [Effective Altruism] nuts, who were really just religious bigots,” he said, referring to the company’s board members who orchestrated the ousting. He contends with their concerns: “Humanity faces risks and we have to manage them,” he said, “but that doesn’t mean we completely forgo the benefits of especially powerful technologies like AI.”

Khosla, one of the TIME100 Most Influential People in AI in 2024, is a firm believer that AI can replace jobs, including those performed by teachers and doctors, and enable a future where humans are free from servitude. “Because of AI, we will have enough abundance to choose what to do and what not to do,” he said.

This interview has been condensed and edited for clarity.

Khosla Ventures has been at the forefront of investing in AI and tech. How do you decide what to put your bets on, and what’s your approach to innovation?

I first mentioned AI publicly in 2000, when I said that AI would redefine what it means to be human. Ten years later, I wrote a blog post called “Do we need doctors?” In that post, I focused on almost all expertise that will be free through AI for the benefit of humanity. In 2014, we made our first deep learning investment around AI for images, and soon after, we invested in AI radiology. In late 2018, we decided to commit to investing in OpenAI. That was a big, big bet for us, and I normally don’t make bets that large. But we want to invest in high-risk technical breakthroughs and science experiments. Our focus here is on what’s bold, early, and impactful. OpenAI was very bold, very early. Nobody was talking about investing in AI and it was obviously very impactful.

You were one of the early investors in OpenAI. What role did you play in bringing Sam Altman back into his role as CEO last year?

I don’t want to go into too much detail as I don’t think I was the pivotal person doing that, but I was definitely very supportive [of Altman]. I wrote a public blog post that Thanksgiving weekend, and I was very vocal that we needed to get rid of those, frankly, EA [Effective Altruism] nuts, who were really just religious bigots. Humanity faces risks and we have to manage them, but that doesn’t mean we completely forgo the benefits of especially powerful technologies like AI.

What risks do you think AI poses now and in 10 years? And how do you propose to manage those risks?

There was a paper from Anthropic that looked at the issue of explainability of these models. We’re nowhere near where we need to be, but that is still making progress. Some researchers are dedicated full-time to this issue of ‘how do you characterize models and how do you get them to behave in the way we want them to behave?’ It’s a complex question, but we will have the technical tools if we put the effort in to ensure safety. In fact, I believe the principal area where national funding in universities should go is researchers doing safety research. I do think explainability will get better and better progressively over the next decade. But to demand it be fully developed before it is deployed would be going too far. For example, KV [Khosla Ventures] is one of the few not assuming that only large language models will work for AI, or that you don’t need other types of AI models. And we are doing that by investing in a U.K. startup called Symbolica AI that’s using a completely different approach to AI. They’ll work in conjunction with language models, but fundamentally, explainability comes for free with those models. Because these will be explainable models, they’ll also be computationally much more efficient if they work. Now there’s a big ‘if’ in if they work, but that doesn’t mean we shouldn’t try. I’d rather try and fail than fail. To try is my general philosophy.

You’re saying that explainability can help mitigate the risk. But what onus does it put on the makers of this technology—the Sam Altmans of the world—to ensure that they are listening to this research and integrating that thinking into the technology itself?

I don’t believe any of the major model makers are ignoring it. Obviously, they don’t want to share all the proprietary work they’re doing, and each one has a slightly different approach. And so sharing everything they’re doing after spending billions of dollars is just not a good capitalistic approach, but that does not mean they’re not paying attention. I believe everybody is. And frankly, safety becomes more of an issue when you get to things like robotics. 

You’ve spoken of a future where labor is free and humans are free of servitude. I’m wondering about the flip side of that. When we’re talking about replacing things like primary healthcare with AI, how does that shift the labor market, and how do we reimagine jobs in the future?

It’s very hard to predict everything, and we like to predict everything before we let it happen. But society evolves in a way that’s evolutionary, and these technologies will be evolutionary. I’m very optimistic that every professional will get an AI intern for the next 10 years. We saw that with self-driving cars. Think of it as every software programmer can have a software intern programmer, every physician can have a physician intern, every structural engineer can have a structural engineer intern, and much more care or use of this expertise will be possible with that human oversight that will happen for the next decade. And in fact, the impact of that on the economy should be deflationary, because expertise starts to become cheaper or hugely multiplied. One teacher can do the job of five teachers because five AI interns help them. 

That’s interesting because you’re suggesting almost a coexistence with AI that complements or optimizes the work. But do you see it eventually replacing those jobs?

I think these will be society’s choices, right? It’s too early to tell what’s there, and we know the next decade will be about this internship of AI expertise idea, in conjunction with humans. The average primary care doctor in America sees the average patient once a year. In Australia, it’s four or five times a year because they have a different doctor-patient ratio. Well, America could become like Australia without producing 5 more doctors. All these effects are hard to predict, but it’s very clear what the next decade will be like. We’ve seen it in self-driving cars. Apply that model to everything, and then you can let them go and do more and more, and society gets to choose. I do think in the long term, in 30, 40, 50 years, the need to work will disappear. The majority of jobs in this country, in most parts of the world, are not desirable jobs, and I think we will have enough abundance because of AI to choose what to do, and what not to do. Maybe there will be many more kids becoming like Simone Biles or striving to be the next basketball star. I do think society will make most of these choices, not technology, of what is permitted and what isn’t.

You’ve publicly disagreed with Lina Khan’s approach to the FTC. What role can regulators play in this need to strike a balance between investing in radical, untested new technologies at scale, and enforcement and regulation to make sure they are safe to use?

I think regulation has a role to play. How much, and when, are critical nuances. We can’t slow down this development and fall behind China. I’ve been very, very clear and hawkish on China because we are in the race for technology dominance with them. This is not in isolation. The Europeans have sort of regulated themselves out of any technology developments, frankly, around all the major areas, including AI. That’s going too far. But I thought the executive order that President Biden issued was a reasonably balanced one. Many, many people had input into that process, and I think that’s the right balanced hand.

Can you expand on where you see dominance within the global AI race? Do you think countries like Japan and India can become global AI leaders?

In the West, it’s pretty clear there will be a couple of dominant models. Places like Google, OpenAI, Meta, and Anthropic will have state-of-the-art models. So there won’t be 50 players in the West, but there will be a few, a handful, as it currently appears. Now, that doesn’t mean the world has to depend on the American models. In Japan, for example, even the Kanji script is very different, as are their national defense needs. They want to be independent. If AI is going to play a role in national defense, they will have to rely on a Japanese model. The same thing in India. If China has its own model, India will have its own model. And so national models will exist. There’s Mistral in the E.U., and that’s a trend we recognized very early, and we were the first to invest in this idea that countries and regions with large populations will want their own models.

In thinking about these nation models, how do you ensure greater equitable distribution of the benefits of AI around the world?

I do think we have to pay attention to ensuring it, but I’m relatively optimistic it will happen automatically. In India, for example, the government’s Aadhaar payment system has essentially eliminated Visa and MasterCard in their [fee] of 3% on all transactions. I’ve argued that if that same system is the key to providing AI services, a primary care doctor and an AI tutor for everybody should be included in the same service. It wouldn’t cost very much to do it. I actually think many of these will become free government services and much more accessible generally. We’ve seen that happen with other technologies, like the internet. It was expensive in 1996, and now the smartphone has become pretty pervasive in the West and is slowly becoming pervasive in the developing world too.

Since Shein became the world’s most popular online shopping destination—with seemingly unbeatable prices, and influencers posting “haul” videos to show off their purchases on social media—the Chinese fast-fashion giant has raised questions over how it produces its plethora of merchandise at dizzying speeds. The answer: AI-powered algorithms that allow the company to pick up changes in customer demand and interest, allowing it to adjust its supply chain in real time. As a result, Shein reportedly lists as many as 600,000 items on its online platform at any given moment, selling to customers in over 220 countries and regions globally.

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But the company has also long been under scrutiny for its poor record on environmental sustainability, becoming fashion’s biggest polluter in 2023. Investigations into Shein’s supply chains have found severe labor rights violations, with factory workers in Southern Chinese manufacturing plants reporting grueling 75-hour work weeks to keep up with demand. 

Shein claims AI is the answer to solving these problems, too. During a retail conference in Berlin in January, Peter Pernot-Day, Shein’s head of global strategy and corporate affairs, explained that more than 5,000 Shein suppliers recently gained access to an AI software platform to analyze customer preferences—information that the company then uses to produce small batches of merchandise to match supply in real-time. “We are using machine-learning technologies to accurately predict demand in a way we think is cutting-edge,” Peter Pernot-Day said. “The net effect of this is reducing inventory waste.”

Shein isn’t the only fast-fashion company to tout the benefits of AI in transforming the fast fashion business. Many of its competitors, including H&M and Zara, have also turned to machine-learning technology to analyze sales data and understand customer demand by predicting trends, tracking inventory levels, and cutting down on operational costs. Retail experts are equally optimistic about the power of generative AI: a recent report by McKinsey suggests that AI could add up to $275 billion to the operating profits of apparel, fashion, and luxury sectors in the next three to five years.

“We are already seeing significant shifts in fast fashion with the use of GenAI,” says Holger Harreis, a senior partner at McKinsey who co-authored the report. Harries adds that in the long term, this could result in more personalized processes in fashion, “even to levels of quasi-bespoke tailoring with colors, styles, and sizes—all delivered by a heavily genAI-led process with human interventions focused on where humans add the most value.”

As Shein uses AI to optimize its supply chain, however, environmental experts question whether these claimed efficiencies are truly improving outcomes. “Without strong ethical, social, and environmental standards in place, AI could just as easily be driving faster production and overconsumption,” says Lewis Perkins, the president of the Apparel Impact Institute, a global nonprofit that measures the fashion industry’s climate impact. 

Companies promise waste reduction as consumption soars

As the world’s second-largest industrial polluter, fast fashion releases 1.2 billion tonnes of carbon emissions every year, accounting for 10% of global emissions, according to research from the European Environment Agency. But no company has been as prolific in generating emissions in recent years as Shein. The company’s 2023 sustainability report recorded a carbon footprint of 16.7 million tonnes last year—nearly triple the number of emissions it produced in the previous three years. Shein’s record has also soared past Zara, previously fashion’s biggest emitter, and is roughly double that of companies like Nike, H&M, and LVMH. 

Founded by Chinese billion Sky Xu in 2008, Shein became a go-to destination for online shopping during the pandemic after listing nearly 600,000 items on its marketplace. By November 2022, it was accounting for 50% of fast-fashion sales in the U.S. One in four Gen Z consumers now shop at Shein, while 44% make at least one Shein purchase monthly, according to research from EMARKETER. Shein reports that 61% of its carbon footprint came from its supply chain, while 38% came from transporting goods from its facilities to customers. In July alone, Shein sent about 900,000 packages to customers by air. 

The sustainability report also highlighted how the company plans to reduce emissions. That includes moving production hubs closer to the customers, launching a $222 million circularity fund to promote textile-to-textile recycling, and setting a 25% reduction target for emissions by 2030. While Shein did not respond to TIME’s request for comment, a spokesperson for the company recently told Grist that the company is increasing inventory in U.S. warehouses and using cargo ships to deliver to customers. The company also reiterated that AI would further help to reduce waste, asserting that “we do not see growth as antithetical to sustainability.”

Read More: Shein Is the World’s Most Popular Fashion Brand—at a Huge Cost to Us All

There is new research that could back these claims. A study by the UNSW Institute for Climate Risk & Response found that companies can harness AI-driven technologies for climate action to analyze their carbon footprint, as well as devise strategies to make these improvements. 

“In short, AI will improve the firms’ entire value chain in ways that help them avoid, mitigate, or offset the environmental impacts of their products, services, or processes,” says David Grant, who co-authored this study with colleague Shahriar Akter. Grant adds that much of this work can be done far more quickly and more accurately with AI, as opposed to humans. “The benefits to the environment, specifically in respect of climate change, are thus far greater than would otherwise be achieved,” he says. 

But still, the authors of the study warn against the risks posed by AI in the fast fashion supply chain, specifically through a “vicious circle of overconsumption, pollution, and exploitation,” says Akter, pointing to Shein’s ability to predict demand and manufacture garments at “lightning-fast speed,” which puts added strain on factory workers to churn out garments even faster.

Algorithms feed on copyrighted work

Generative AI’s risks don’t stop at the supply chain. Akter from UNSW adds that the technology is also susceptible to breaching copyrights and compromising the artistic quality of human creativity. 

In April, Connecticut-based artist and designer Alan Giana filed a lawsuit in New York’s Southern District against Shein, alleging that the company’s use of AI, machine learning and algorithms were systematically infringing on his copyrighted work. Citing “Coastal Escape,” artwork that appeared on Shein’s website without permission or attribution, the complaint alleges that “widespread copyright infringement is baked into the business” by using sophisticated electronic systems that “algorithmically scour the internet for popular works by artists.” It went further by stating that the infringement likely extends to “thousands or tens of thousands of other persons” in the U.S. 

Shein has been faced with dozens of similar lawsuits alleging design theft in the past. In July 2023, three graphic designers in China sued Shein for using “secretive algorithms” to identify trends and copy their designs. The complaint went so far as to say that the company’s copyright infringement was so aggressive that it amounted to “racketeering.” In response, Shein told NBC that it took all claims of infringement “seriously:” “We take swift action when complaints are raised by valid IP rights holders,” it stated. 

Akter from UNSW says that generative AI-based designs “might result in breaching copyrights and put a company in a questionable situation,” adding that it could also result in “algorithmic monoculture,” pushing fashion companies to rely on similar algorithms and causing them to lose the necessary creativity in fashion retailing. Moreover, he says that AI-based marketing models could also result in algorithmic bias extending to race, gender, sexual orientation, social class, religion, and ethnicity. 

But despite these risks, more brands are investing significant amounts of their budget in AI. McKinsey’s Harreis is optimistic about its ability to optimize production and reduce waste, but he adds that companies still face a big challenge. “In order for tech to add value, companies need to realize that it is never just about tech, it takes rewiring the entire organization,” he says. 

AI can help bring a systemic shift in design, production, and consumption, says Perkins at the Apparel Impact Institute, but only if it is “paired with responsible business practices, transparent supply chains, and a commitment to reducing overall impact.” It’s not impossible to imagine what this might look like. Perkins points to innovators like Made2Flow, which uses AI-driven data analytics to measure and optimize environmental impact across the fashion supply chain. Similarly, Smartex.Ai leverages AI to detect and reduce fabric defects, leading to lower material waste.

But if AI is used solely to speed up production and push more products to market, it could “fuel overconsumption,” Perkins warns. “Until there’s clear evidence that AI is being used to genuinely reduce the fashion industry’s environmental footprint, I remain cautious about how much positive impact this model is actually having,” he says.

As Asia’s richest man, Mukesh Ambani, addressed his shareholders during a much-anticipated yearly address last Thursday, he also unveiled “JioBrain,” a suite of artificial intelligence (AI) tools and applications that he says will transform a spate of businesses in energy, textiles, telecommunications and more that form his multinational conglomerate, Reliance Industries. “By perfecting JioBrain within Reliance, we will create a powerful AI service platform that we can offer to other enterprises as well,” Ambani said during his speech.

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The Reliance Chairman’s latest offering comes as India emerges as a crucial player in the global AI ecosystem, boasting a high-powered IT industry worth $250 billion, which serves many of the world’s banks, manufacturers and firms. As the world’s most populous country, India also has a robust workforce population with nearly 5 million programmers at a time when AI talent is in short supply globally, with analysts predicting that India’s AI services could be worth $17 billion by 2027, according to a recent report by Nasscom and BCG.

Puneet Chandok, the President of Microsoft India & South Asia, points to research that finds India has one of the highest AI adoption rates among knowledge workers, with 92% using generative AI at work—significantly higher than the global average of 75%. “These insights highlight the significant impact of AI on the Indian workforce and the proactive steps being taken by both employees and leaders to integrate AI into their daily routines,” Chandok says, adding that the company is also powering initiatives that aim to equip 2 million people with AI skills by 2025.

The spotlight on India comes at a time when many countries around the globe are keen to foster their own competing AI systems rather than turning to the U.S. or China. In the last few years, the Indian government has nurtured an ecosystem where global players like Google and Meta, Indian businesses like Reliance Jio and Tata Consulting Services, and homegrown startups can take advantage of its cost-efficient technological landscape.

India’s “bottom-up” approach to AI

India also aspires to have what Rajeev Chandrasekhar, the former Indian Minister for Electronics and Information Technology, calls “sovereign AI,” by integrating large-scale models across sectors like healthcare, agriculture, and governance to drive economic growth. In March, the government ramped up investment worth $1.25 billion towards an ambitious “IndiaAI Mission,” which will aid the development of computing infrastructure, startups and the use of AI applications in the public sector.

“Interestingly, the government itself is the main driver behind India’s AI transformation,” says Jibu Elias, a leading AI researcher and ethicist who helped create IndiaAI. Elias says the push has accelerated since 2020. “We want India to be like a global garage for AI tools, especially for the Global South.”

“The idea is that if you can build tools that address some of the decade-long socio-economic challenges in India, they can be adopted across the globe,” he continues.

It’s a method that Arvind Gupta, who heads the Digital India Foundation in New Delhi, calls a “bottom-up” approach: “Unlike the Googles and Microsofts of the world, India took it to the next level by building trust in technology with digital public infrastructure,” he says. Digital public infrastructure, also known as DPI, is a public-private partnership that was introduced by the government nearly a decade ago by combining technology, governance and civil society. It extends to a biometric identification system, a fast payments system, and consent-based data sharing that now gives India’s 1.4 billion citizens access to public services. 

Gupta says DPI is instrumental in giving India an advantage in the global AI race. With 900 million Indians connected to the internet, he points to India being “the data capital of the world,” which has “leapfrogged into the whole culture of artificial intelligence.” That’s because much of this data exists in public data sets that companies can use to write their own AI algorithm. “You won’t see that anywhere else in the world,” Gupta says.

The race to build LLMs as chipmakers eye Indian market

With so much data publicly available, a swath of Indian startups are now racing to build their own large language models or LLMs, which harness generative AI by learning from vast quantities of data. And in a country where people speak more than a dozen languages, “India’s diverse and multilingual environment makes it an ideal test bed for developing and refining global AI solutions,” says Chandok from Microsoft. 

In January, Krutrim, an AI startup founded by entrepreneur Bhavish Aggarwal whose name translators to “artificial” in Sanskrit, became India’s first unicorn when it secured $50 million in funding from prominent Silicon Valley investors like Lightspeed Venture Partners and billionaire Vinod Khosla. Similarly, Bengaluru-based startup Sarvam recently launched a voice-enabled AI bot that supports more than 10 Indian languages using open-source software after raising $41 million. The government is also supplementing this innovation by building “targeted LLMs” that can do real-time language translation for citizens accessing public services, Gupta adds.

Still, India’s AI push can’t accelerate without computing power and shared resources. To address this gap, last month, the Indian government finalized the procurement of a thousand graphics processing units, or GPUs, to offer computing capacity to AI makers. Last September, the CEO of chipmaker Nvidia, Jensen Huang, visited India to sit down with Modi and tech executives, setting the company’s sights on the country as a potential location for chip production as the U.S. increasingly clamps down on the export of high-end chips from China. “You have the data, you have the talent,” Huang told Modi at the time. “This is going to be one of the largest AI markets in the world.” This March, the first consignment of Nvidia chips arrived in Indian data centers after the company forged a partnership with Indian cloud services company Yotta, powering its Shakti Cloud as India’s fastest AI supercomputing infrastructure. 

Against this backdrop, billionaire-owned Indian companies are eager not to be left behind. In July, India’s largest software company, Tata Consultancy Services (TCS), heavily invested in a generative AI project pipeline exceeding $1.5 billion. Gautam Adani, Asia’s second-richest person, announced a joint venture with UAE in December to explore AI and diversify into digital services. 

And as for Ambani, who has urged his employees to accelerate AI transformation across all businesses this year, the goal is clear: “We need to be at the forefront of using data, with AI as an enabler for achieving a quantum jump in productivity and efficiency,” the billionaire told Reliance employees. 

Since then, Jio, Reliance’s telecommunications business, has worked with the Indian Institute of Technology to launch “Bharat GPT,” a ChatGPT-style service for Indian users. A video played during a Reliance event demonstrated how the speech-to-text tool would work if successful: a motorcycle mechanic speaks to the AI bot in his native Tamil, a banker uses the tool in Hindi, and a developer in Hyderabad writes computer code in Telegu.

“It’s like the Indian joint family,” said Ganesh Ramakrishnan, the chair of IIT Bombay’s computer science and engineering department. “We are interdependent, and we do better together.”