The latest leap in artificial intelligence is forcing businesses to reconsider a question that goes far beyond which chatbot they should use: How much of the work itself still needs to be performed by people?

OpenAI’s newly released GPT-6 Astra is being positioned as a major advance in AI capabilities, particularly in computer use, coding and completing complex multi-step tasks. NVIDIA CEO Jensen Huang has even declared that artificial general intelligence, or AGI, has arrived with Astra — although that claim remains controversial and there is no universally accepted definition of AGI.

For businesses, however, the AGI label may be less important than what these systems can actually do.

The biggest change is the growing ability of AI to complete work rather than simply generate information. Instead of asking an AI to write an email, summarize a report or produce an idea, companies can increasingly give AI a larger objective and allow it to work through multiple steps toward completion.

That changes the economics of automation.

A company could eventually have AI handling portions of customer service, research, administrative operations, sales follow-up, software development and internal analysis with considerably less human intervention. The human role shifts from performing every step to setting objectives, reviewing results and handling the situations AI cannot reliably resolve.

That does not mean businesses should immediately replace employees with AI. It means companies should start examining their workflows differently.

The companies that gain the most from increasingly capable AI may not be the ones that simply purchase the newest model. They will be the ones that redesign their operations around what AI can now accomplish.

This is also why the arrival of more autonomous AI creates a new management challenge. OpenAI’s chief scientist has warned that increasingly capable agents could create consequences that organizations and society are not yet prepared to manage.

For executives, the message is straightforward: AI is moving from a productivity tool toward a potential digital workforce.

Businesses should be asking now which tasks can be automated, where humans must remain in control, and how employees can move toward higher-value responsibilities.

The competitive advantage may no longer come from simply having AI.

It may come from knowing how to reorganize the business around it.

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.

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.

This morning my telephone began ringing. It didn’t ring once. It rang approximately 11 times. Eventually I answered. Then came two text messages, supposedly from Chase Bank Fraud Prevention. What made them disturbing wasn’t simply that someone was pretending to be my bank. The sender had information about me.

The message addressed me by name and not the one I normally use. They asked whether I had used my debit card ending in the exact last four numbers at Smiles Dentistry for $1,286.87. If I recognized the transaction, I was instructed to reply YES. If I didn’t, I was to reply NO.

Now think about that for a moment.

I am currently undergoing extensive dental work. The sender knew the last four digits of my debit card. Dentistry is not some random subject pulled out of the air in my life right now. Put those things together with the name Chase Bank, a specific dollar amount and a supposed fraud warning, and you have something considerably more sophisticated than the badly spelled emails from foreign princes we learned to laugh at years ago.

This is what modern fraud looks like and it is frighteningly easy to understand how somebody falls for it. I didn’t.

Instead, I contacted Chase and reported what had happened. I also forwarded the message to 7726 (SPAM) and reported the incident to the Federal Trade Commission. The FTC specifically recommends forwarding unwanted scam texts to 7726 and reporting suspected fraud to the agency.

Then I did something scammers probably don’t expect everyone to do. I started investigating. The telephone number displayed on my phone was 585-404-6029, a 585 area-code number. My search led me to the name Yeishaly Santiago, age 26. But there is an important distinction here: finding a name associated with a telephone number on the internet does not prove that the person owns the number, made the call or committed a crime. Telephone numbers can be spoofed and identities can be impersonated.

So I tested what I had found. I texted the sender that I had reported the attempted scam to 7726, Chase and the Federal Trade Commission. I cursed him out, called him a scammer and told him that I now knew his name. I gave him the name I had found and his purported age.

His response was a smiley face. Then came: “Yeah, that’s my name, silly lady.”

That does not constitute an independent verification of the real-world identity of the person holding the phone. It does establish something I can report firsthand: the person communicating with me claimed that the name I had discovered was his.

There was another rather spectacular moment in our exchange. When I initially told the supposed Chase representative that he had been reported, his professional banking demeanor disappeared remarkably quickly. His response? “Suck a dick.”

I have dealt with Chase for years. I don’t remember that particular phrase appearing in its customer-service manual. The humor, however, shouldn’t obscure what happened. Someone contacted me pretending to represent my bank while apparently possessing enough accurate personal financial information to make the contact initially plausible.

Chase itself warns that suspicious texts should not be answered. The bank says its legitimate text messages use five- or six-digit short codes, and lists 28107, 36640 and 72166 among the codes used for fraud and account-security messages. Chase advises customers who receive suspicious messages to contact the bank independently using the telephone number on the back of their card or account statement.

The message I received came from an ordinary ten-digit telephone number. That is something I now know to look for immediately.

The Scam Has Changed

For years we were taught to recognize scams by their stupidity. Look for misspellings. Look for ridiculous promises. Don’t send money to somebody claiming you’ve won a lottery you never entered. That advice is no longer enough.

Today’s scammer doesn’t necessarily need you to believe an outrageous story. They need to know just enough truth about you to make the lie believable.

Your bank. Four digits from your card. A business you actually use—or, in my case, a type of business very much connected to something currently happening in my life.  Then comes urgency. Your card has supposedly been charged $1,286.87. Did you authorize it? YES or NO?

The instinct is to respond immediately because you think somebody is stealing your money. The moment you respond, the scammer knows several things. The telephone number is active. You are reading the messages. You are concerned and, most importantly, you are now engaged in the conversation.

That is why verification has to happen outside the conversation the stranger initiated.

The scale of the problem is enormous. According to new FTC figures released this summer, consumers reported losing $3.5 billion to imposter scams in 2025, nearly triple the reported losses in 2020. Nearly one in three fraud reports involved an imposter scam, and among people reporting losses to business impersonators, bank impersonators accounted for the highest reported losses.

Even more disturbing, Americans reported nearly $16 billion in total fraud losses during 2025, a record and roughly 25 percent higher than the previous year.

On the very day I am writing this, an Associated Press and FRONTLINE investigation reports that 98 percent of Americans surveyed suspect they have been targeted by scam messages, while reported U.S. scam losses reached $15.9 billion last year.

This isn’t happening to a handful of gullible people. It is happening to virtually everybody.

One of the reasons scams continue to flourish is embarrassment. People don’t want to admit that they were fooled. That gives the criminal another advantage. We need to stop treating scam victims as stupid.

A convincing fraud is specifically engineered not to look like fraud. Its entire purpose is to take fragments of reality and arrange them into a lie convincing enough to make an intelligent person act before questioning it.

If a bank supposedly contacts you about suspicious activity, stop. Don’t use the telephone number supplied by the person contacting you. Don’t click the link in the text. Don’t give them another piece of information merely because they already possess one. Open your banking app independently or call the number printed on the back of your card.

And report the attempt.

What happened to me this morning did not cost me, ut it taught me something considerably more valuable.

The fact that someone knows something about you does not mean they are who they say they are. A stranger knowing your bank doesn’t prove he works for your bank. Knowing four digits of your card doesn’t entitle him to the other twelve. Knowing something happening in your life doesn’t make him trustworthy. And a telephone number appearing on your screen doesn’t necessarily tell you who is actually behind it.

I got proactive. I reported them. I crank-called them. Then I went looking. The sender told me the name I found was his. He messed with the wrong person. But here is the part I don’t want to leave out: about two years ago, I was almost taken in by a scam myself.

This time I reported what happened to the institutions whose job it is to investigate further first instead of feeling helpless.

Now I’m reporting something else—to you.

The next scam that arrives on your phone may know far more about you than you expect. Don’t let what they already know convince you to give them what they don’t.

What the sender apparently didn’t know was that the “silly lady” he had chosen to scam happens to be a journalist—and owns the publication you’re reading right now.

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.