One of the biggest beneficiaries of the artificial-intelligence boom may turn out to be an industry that existed decades before the internet. Holtec Nuclear is seeking a valuation of as much as $10.2 billion in its upcoming U.S. IPO, offering 50 million shares at between $15 and $18 and potentially raising about $900 million. Holtec builds nuclear equipment, handles spent fuel and decommissioning, develops small modular reactors and is now attempting something the United States has never done before: restart a commercial nuclear plant after it had already been permanently shut down. The renewed investor interest comes as AI data centers create enormous new demand for electricity and force technology companies, utilities and governments to rethink where that power will come from.

At the center of Holtec’s story is the 805-megawatt Palisades nuclear plant in Michigan. The plant shut down in 2022 and entered the decommissioning process, which normally would have meant the end of its electricity-producing life. Instead, Holtec began a multiyear effort to restore it. On August 30, the company started loading nuclear fuel back into the reactor vessel, a major step toward bringing the facility back into service. If Palisades successfully returns to commercial operation, it would become the first U.S. commercial nuclear power plant to restart after being decommissioned. Holtec also plans to deploy its first two SMR-300 small modular reactors at the Palisades site, potentially turning an old nuclear facility into a showcase for a newer generation of reactor technology.

The timing is not accidental. Artificial intelligence requires enormous amounts of computing power, and computing power requires enormous amounts of electricity. AI data centers run thousands of specialized chips around the clock, along with cooling equipment, networking systems and backup infrastructure. As companies race to build more of them, electricity is becoming one of the biggest constraints on AI expansion. The pressure is already appearing internationally. South Korea estimates that new semiconductor factories and AI data centers could require an additional 25 to 30 gigawatts of electricity, roughly equivalent to the output of about 20 modern nuclear reactors if nuclear alone supplied the increase.

That changes the conversation around nuclear power. For years, many nuclear plants looked economically unattractive because they were expensive to build, slow to permit and politically controversial. Natural gas was cheaper and faster to deploy, while wind and solar costs fell rapidly. But AI companies are creating demand for something slightly different: enormous quantities of electricity that can be available reliably around the clock. Nuclear plants can provide that kind of steady output without the carbon emissions associated with fossil-fuel generation. Suddenly, an asset that once looked outdated can become strategically valuable because a completely new industry desperately needs what it produces.

The shift shows how technological revolutions can revive industries rather than simply destroy them. The internet created enormous demand for fiber-optic cable and data centers. Smartphones boosted demand for semiconductors, rare-earth minerals and wireless infrastructure. Electric vehicles created new opportunities for lithium miners and battery manufacturers. AI may do the same for uranium miners, reactor builders, electrical-equipment manufacturers, cooling companies and utilities. The businesses that benefit most from AI will not necessarily have anything resembling a chatbot. Some may operate mines, turbines, substations and power plants.

Holtec’s IPO is essentially giving public investors a chance to bet on that connection. The company is not purely an AI business and its future does not depend solely on data centers, but rising electricity demand is improving the economic backdrop for nuclear projects throughout the industry. Uranium investment is already increasing as miners prepare for stronger demand. Reuters reported recently that expectations for AI-driven power needs are helping fuel investment in new uranium production, with some industry analysts expecting uranium demand to increase dramatically over the next decade.

There are still significant obstacles. Nuclear plants can require billions of dollars in upfront capital, years of regulatory review and complicated construction. Restarting Palisades is an unusually ambitious engineering and regulatory project, while small modular reactors still need to prove that they can be deployed economically at scale. Nuclear safety, radioactive-waste management and local opposition remain major issues as well. AI demand may strengthen the business case for nuclear power, but it does not remove the challenges that caused many nuclear projects to struggle in the first place.

That is what makes Holtec’s attempted $10 billion valuation interesting. It represents a larger shift in how investors are thinking about the AI economy. The opportunity is no longer limited to whoever builds the best model or manufactures the fastest chip. Every layer beneath AI, land, power, cooling, transmission, construction and fuel is becoming part of the opportunity. Nuclear power was once treated by many investors as a legacy industry whose best years were behind it. AI may be forcing the market to reconsider. Sometimes the newest technology does not replace an old industry. It gives that old industry an entirely new reason to exist.

The biggest shift happening in business AI right now is not simply that models are becoming smarter. It is that AI is increasingly being given the ability to do the work itself.

OpenAI reported this week that leading AI-using companies are moving beyond basic assistance and connecting AI agents directly to company context, software and business processes. Its examples include agents handling employee onboarding, maintaining sales-account intelligence and carrying opportunities through research and execution.

That represents an important change for businesses.

For years, companies adopted AI primarily as a productivity tool: write an email, summarize a document, generate marketing copy or answer an employee’s question. The emerging model is different. Businesses are beginning to design workflows where an AI agent receives a trigger, gathers the necessary information, uses business software, completes defined steps and escalates exceptions to a human.

OpenAI says its own researchers are already using coding agents throughout the day, with agents handling increasingly complex tasks and helping accelerate research work.

The opportunity for businesses is enormous, but the lesson is not to automate everything at once.

The companies most likely to benefit will start with one measurable workflow: lead follow-up, customer onboarding, appointment scheduling, reporting, research or another repetitive process. They can then measure whether the agent actually saves time, reduces costs, improves response times or generates revenue.

The competitive advantage may ultimately come less from having AI and more from knowing which business processes to give AI responsibility for.

AI is no longer just becoming a better assistant. It is becoming part of the workforce.

For business leaders, the question is increasingly not, “How can we use AI?”

It is: “What work should AI own?”

Half of Google’s search results now come wrapped in an AI-generated summary. A growing share of buyers never click through to your website at all; they get their answer from a chatbot and move on. If your brand isn’t the one being cited, quoted, or recommended inside that answer, you are invisible to a customer who never even opened a browser tab.

This is the new battlefield, and most agencies aren’t built for it. They know keywords. They know backlinks. They don’t yet know how to get a brand mentioned inside a large language model’s answer, which is a completely different discipline.

1. They Think in Systems, Not Keywords

A traditional SEO consultant optimizes a page. An AI visibility specialist optimizes an entire information ecosystem, because that’s how models like ChatGPT, Gemini, and Perplexity actually pull their answers.

They understand that visibility now depends on structured data, third-party citations, review platforms, forums like Reddit, and your own site content working together. No single lever moves the needle anymore.

2. They Can Explain the Difference Between Ranking and Being Cited

This one trips up a lot of “experts.” Ranking on page one of Google and being cited as a source inside an AI answer are not the same game, even though they overlap.

A reliable specialist will walk you through:

• How large language models select and weight sources when generating an answer
• Why Wikipedia, review sites, and structured comparison content punch above their weight in AI citations
• How brand mentions across the open web (not just your own domain) feed model training and retrieval
• Why answer engines reward clarity and directness over keyword density

If someone can’t articulate this distinction in plain English, they’re likely reselling traditional SEO with a new label.

3. They Treat Data Like Evidence, Not Decoration

Good specialists don’t guess. They test prompts across multiple AI platforms, track how often your brand appears, and measure sentiment when it does.

This matters because behavior is shifting fast. A 2026 survey from Orbit Media found that 55% of respondents now use AI chat as their primary or frequent research tool, with usage patterns varying widely by task, from quick factual lookups to vacation planning to medical questions. A specialist who understands these nuances can tell you exactly where your brand needs to show up, and where it doesn’t matter yet.

4. They Understand Content Structure at a Technical Level

AI models don’t read your website the way a human does. They parse, extract, and reassemble. That means content structure isn’t a nice-to-have anymore; it’s the whole game.

What Structural Fluency Looks Like

A specialist worth hiring will insist on:

1. Clear, scannable headers that map to actual user questions
2. Schema markup that tells machines exactly what your content is
3. FAQ-style sections that mirror how people phrase queries to a chatbot

5. They’re Honest About What They Can’t Control

This is where trust gets tested. Nobody, no matter how skilled, can guarantee a specific placement inside a ChatGPT response the way an old-school SEO consultant might promise a page-one ranking.

A reliable specialist will tell you this upfront. They’ll frame their work as improving your probability of being cited across a range of AI systems, not promising a fixed outcome. Overconfidence here is a massive red flag, because it signals someone selling certainty in a genuinely uncertain field.

6. They Stay Current Because the Ground Keeps Moving

The AI landscape changes monthly, not yearly. Model updates, new retrieval methods, and shifting user behavior mean a strategy that worked in January might be stale by June.

The best AI visibility company specialists treat ongoing education as part of the job description, not an afterthought. They are testing new platforms, reading model release notes, and adjusting client strategy in near real time. If someone’s approach hasn’t shifted in the last six months, they’ve fallen behind.

The Bottom Line for Leadership Teams

Choosing the wrong partner here isn’t just wasted budget. It’s a missed window during a period when the rules of discovery are still being written, and early movers are quietly locking in advantages that latecomers won’t be able to buy back.

Author Bio:
Derek Iwasiuk has more than 20 years of experience as a strategist and SEO for organizations in highly competitive industries. He helps businesses improve their visibility and optimize their presence on search engines and AI systems at Searchtides.com.

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.