NVIDIA is making a massive bet that the future of artificial intelligence will not be controlled solely by a handful of companies selling access to closed AI models.

The chip giant has agreed to acquire Hugging Face for $12.93 billion, one of NVIDIA’s largest acquisitions. Hugging Face has become a central platform for developers building, sharing and deploying open-source and open-weight AI models. More than 18 million developers, researchers and creators use the platform, while more than 200,000 companies rely on it for AI development.

For businesses, the deal matters because it points toward a future in which companies have far more choices about how they build AI.

Rather than depending entirely on expensive proprietary models, businesses can increasingly customize open models for specific tasks, run them across different cloud providers and potentially deploy them using their own infrastructure. NVIDIA says Hugging Face will remain open and will continue supporting different models, clouds and computing platforms rather than requiring NVIDIA hardware.

That could eventually make enterprise AI more flexible and less expensive.

But there is another message behind the acquisition: AI infrastructure is becoming the real battleground.

NVIDIA already dominates the chips powering modern AI. By moving deeper into the software and developer ecosystem, the company is positioning itself across more of the AI stack—from the computing hardware to the models and tools businesses use to build applications.

For business owners, this means the AI decision is becoming less about asking, “Which chatbot should we use?” and more about asking, “What AI infrastructure gives our company the greatest control, flexibility and return on investment?”

The companies that begin experimenting with customized models, AI agents and internal AI systems now may have an advantage as these technologies become cheaper and more capable.

The NVIDIA-Hugging Face deal is therefore more than a $13 billion acquisition. It is a signal that the next phase of business AI may be defined by open models, customized systems and control over the underlying AI infrastructure.

And for businesses, that could ultimately mean more powerful AI without being locked into a single vendor.

Conducting proper market research when investing in cryptocurrencies is essential to managing risks and seizing opportunities. Starting with reading the whitepaper and analyzing the coin’s profile, you gain a basic understanding of how it could perform in the future. Usually, you can also check social media for opinions or developers’ insights, but this guideline is tricky when making crypto predictions.

That’s because users on social media, from regular investors to influencers, can only offer their limited insight into the future of a cryptocurrency, each considering their own risks and goals. When it comes to crypto influencer advice, you should practice caution before you buy Bitcoin or other coins, as a person with the right experience and knowledge can truly have a positive impact on your journey as an investor, but the wrong one can cause more harm to your portfolio.

On a broader note, these popular users are also known as “finfluencers” who offer financial advice for others to follow. However, they are far from being what accredited advisors are, and can pose serious risks for investors. Let’s learn more about them.

What makes finfluencers appealing?

Influencers in the financial domain have become famous content creators on social media platforms like Instagram or TikTok, where GenZ is the majority of viewers. Finfluencers create engaging video posts that leverage storytelling and conversational language to make the content interesting and relatable. Interestingly, the type of content appealing to younger investors has been successful because Gen Z has a greater appetite for risky investments as opposed to older generations, which is why they rely on influencers to hit the right spot.

Unfortunately, finfluencers expose their followers to risks, such as misinformation, which can be particularly dangerous for beginners. They might portray crypto investments as straightforward and without risk, when the truth is that people must thoroughly research the market and make investment decisions with safety in mind.

Moreover, influencers’ content can also lead to scams and risky investments, as they leverage their positions in the online media ecosystem to sell risky products, promote unregulated exchanges, or make pitches for trading platforms that risk bankruptcy at any time.

How do influencers impact companies?

Besides confusing users about the right information, financial influencers can also spread misleading information about a firm to promote personal gain. This is possible by oversimplifying financial topics or misinterpreting a company’s latest announcement, affecting customers’ perception of the company’s image.

Luckily, there are efforts to minimize such impacts, as regulators like the SEC are charging finfluencers for their involvement in stock manipulation schemes or for participating in “pump and dump” activities with new coins. But companies must also practice due diligence when collaborating with influencers and try to promote their products and services in ways that educate retail investors and strengthen investor relationships.

That’s why designing effective communication strategies can help identify the right collaborators who are willing to respect key features such as transparency and consistent messaging for a campaign. Otherwise, working with fake influencers can detrimentally affect a company’s brand image.

However, some investment advisors can be present on social media

While it’s generally unwise to follow every influencer’s approach to cryptocurrency investment, it is not uncommon to find accredited financial advisors making content on social media to expand the range of people who can access genuine, free information.

These advisors work only after achieving specific qualifications that allow them to offer advice, and they must respect their duties to seek the best execution and to offer advice that works in the best interest of the customer. They also know their charging fees and can earn commissions for financial transactions, which allows them to be registered employees, like any of us.

Checking whether a public figure has the right qualifications to serve as an advisor and seeking their collaboration on content they create can be helpful for crypto investors.

Social media has helped bring people together from around the world, but this is becoming a problem for modern cryptocurrency investors due to the risk of fake influencers spreading misinformation. These users are also known as finfluencers, and they can influence investors’ decisions by offering information that lacks proper research, as well as by coercing them into scams. While some financial institutions are starting deals with them, their growing presence on social media is overwhelming, making it people’s responsibility to protect themselves.

Nvidia is making one of its biggest moves beyond chips, agreeing to acquire AI platform Hugging Face for roughly $13 billion. The deal is significant because Hugging Face has become a major home for open-source AI models, datasets and applications, with more than 18 million developers and 200,000 companies using the platform. Nvidia says Hugging Face will remain open and support multiple cloud and computing platforms.

What It Means for Businesses

The acquisition points to an important shift in the AI market: businesses are increasingly looking beyond simply subscribing to a chatbot.

Open-source AI gives companies more opportunities to customize models, run AI within their own infrastructure and reduce dependence on a single AI provider. Nvidia’s investment could accelerate that trend by combining its computing infrastructure with one of the world’s largest open AI communities.

For smaller businesses, the bigger takeaway is that AI is becoming infrastructure rather than an experimental tool. Companies that build AI into sales, customer service, marketing, operations and internal workflows are likely to have more choices about which models power those systems.

But there is also a warning. Hugging Face has recently faced AI-related security concerns, while businesses are giving autonomous AI agents increasing access to company systems. Security researchers and lawmakers are now pushing for stronger controls around what AI agents can access and execute.

The business opportunity is no longer simply “use AI.” It is building an AI stack that is flexible, secure and capable of changing as better models arrive.

For business owners, that means the companies that avoid locking themselves into one AI model today may have a significant advantage tomorrow.

Tesla is showcasing its two-seat Cybercab in Austin as it pushes deeper into autonomous ride-hailing, and the vehicle’s most striking feature may be what it does not have: a traditional steering wheel. That design decision matters because it signals that Tesla is not simply introducing another car. It is trying to build a vehicle meant to function primarily as part of a transportation network rather than as a product someone buys, parks in a driveway and drives personally. If that strategy works, the economics of the car business could start to look very different.

For most of automotive history, the business model has been simple. A car company designs a vehicle, manufactures it and sells it once. Revenue is tied largely to unit sales. The company may earn additional money from financing, servicing or software, but the main transaction still happens when ownership changes hands. A robotaxi model changes that completely. Instead of generating revenue one time at the point of sale, the same vehicle could potentially generate revenue over and over again by selling rides throughout the day.

That is why autonomous ride-hailing is such an important idea for Tesla. A privately owned vehicle often spends most of its life parked. A robotaxi, in theory, becomes a productive asset. If it can operate for many hours a day, carrying passenger after passenger, the same car begins looking less like a consumer product and more like infrastructure. The financial value of the vehicle no longer comes only from what someone is willing to pay to own it. It comes from how much transportation revenue the vehicle can produce over time.

This is a very different business model from traditional car manufacturing, and it pushes Tesla closer to something that resembles a hybrid of automaker, software company and transportation platform. The company is no longer just asking how many vehicles it can sell. It is asking how many rides each vehicle can complete, how efficiently the fleet can operate and how much demand exists for driverless transportation. That moves the conversation from hardware margins to utilization, network density and recurring revenue.

The appeal of that model is obvious. A company that successfully operates autonomous vehicles at scale could capture much more lifetime value from each car than a one-time sale would provide. It could also potentially reduce reliance on the normal replacement cycle in which customers buy a new vehicle only every several years. In that sense, the most valuable transformation may not be making a better car. It may be turning the car into a machine that continuously earns money.

But that vision also explains why the path is difficult. Building a robotaxi business involves much more than manufacturing the vehicle itself. The company must prove the safety of the technology, satisfy regulators, manage public trust, secure operating permits, build the ride-hailing system and maintain the vehicles as part of an active fleet. A traditional carmaker mainly needs to persuade a customer to buy the car. A robotaxi operator must persuade cities, regulators and the public to accept an entirely different way of moving through everyday life.

There is also a broader lesson here for other industries. Some of the most powerful business transformations happen when a company stops earning money only when the product is sold and starts earning money from what the product does after it is deployed. Software shifted from one-time licenses to subscriptions. Industrial equipment increasingly includes ongoing monitoring and service contracts. Media moved from individual purchases to recurring access. Tesla’s robotaxi push reflects the same logic in physical form: the biggest opportunity may be turning an owned product into a recurring service.

The Cybercab therefore represents more than an unusual vehicle design. It is a visible example of a much larger economic shift. If autonomous transportation becomes mainstream, the winning company may not simply be the one that builds the most cars. It may be the one that best turns those cars into revenue-generating assets inside a functioning network. At that point, the question is no longer just whether a customer wants to buy the vehicle. It is whether the vehicle itself has become the business.

The next major shift in business AI may not be another smarter chatbot. It may be AI that actually helps customers shop.

Anthropic announced Wednesday that it is giving retailers blueprints for building AI shopping and merchant agents using Claude. The systems can make personalized product recommendations and add items to a customer’s shopping cart, while merchant-facing agents can help with inventory, pricing and marketing decisions.

The timing is significant. Shoppers are increasingly turning to AI to compare products, check availability and decide what to buy. According to Adobe Analytics, visits to retail websites originating from AI are converting at a rate about 60% higher than traffic from other sources. Anthropic also reported that one partner saw shopping carts grow roughly 30% to 35%, while customers were about 60% more likely to complete a purchase.

What This Means for Businesses

This changes the role AI can play in sales.

For years, businesses primarily used AI to answer questions, generate content or automate individual tasks. Commerce agents move AI closer to the actual revenue process: understanding what a customer wants, recommending products and helping move that customer toward a purchase.

That means businesses may soon compete not only for Google rankings and social-media attention, but also for visibility inside AI-driven shopping experiences.

For small and midsize businesses, the message is particularly important. Companies do not necessarily need to build their own frontier AI model. They need to make their products, services, inventory and customer information usable by AI systems—and begin thinking about how an AI agent could participate in their sales process.

The businesses that adapt early could gain an advantage as customers increasingly ask AI what they should buy rather than searching through dozens of websites themselves.

The bigger shift is already underway: AI is moving from helping employees do the work to helping businesses generate the sale.