Commerce Secretary Howard Lutnick said Tuesday that Intel must give the U.S. government an equity stake in the company in return for CHIPS Act funds.

“We should get an equity stake for our money,” Lutnick said on CNBC’s Squawk on the Street. “So we’ll deliver the money, which was already committed under the Biden administration. We’ll get equity in return for it.”

Shares of the struggling chipmaker climbed nearly 7% on Tuesday, continuing to rally on recent reports that the Trump administration is weighing different ways to get involved with the company. Bloomberg reported Monday that the White House was discussing a 10% stake in Intel, in a deal that could see the U.S. government become the chipmaker’s largest shareholder.

Intel and SoftBank announced Monday that the Japanese conglomerate will make a $2 billion investment in the chipmaker. The investment, equal to about 2% of Intel, makes SoftBank the fifth-biggest shareholder, according to FactSet. The move is part of SoftBank’s broader pledge to expand investment in the U.S. under President Donald Trump’s domestic manufacturing agenda, which has emphasized “reshoring” critical industries such as semiconductors.

Lutnick said any potential arrangement wouldn’t provide the government with voting or governance rights in Intel.

“It’s not governance, we’re just converting what was a grant under Biden into equity for the Trump administration, for the American people,” Lutnick said. “Nonvoting.”

Lutnick also suggested that President Trump could seek out similar deals with other CHIPS recipients. Intel said last fall that it had finalized a nearly $8 billion grant from the law to build its factories. Taiwan Semiconductor Manufacturing Co. (TSMC) was awarded $6.6 billion under the legislation to boost chip fabrication at its Arizona facilities.

Trump’s “Silicon Heartland” Vision

Trump has repeatedly called for more reshoring of U.S. manufacturing to reduce the country’s reliance on foreign foundries such as Samsung and TSMC. Intel has been spending billions near Columbus, Ohio, to build a series of chip factories that the company previously dubbed the “Silicon Heartland.” Intel has said the factory complex would eventually produce the most advanced chips, including AI semiconductors critical to national security and next-generation computing.

However, in July, Intel CEO Lip-Bu Tan warned employees in a memo that there would be “no more blank checks,” signaling the company was slowing down construction of the Ohio factory complex depending on market conditions. The first factory is now scheduled to begin operations in 2030 — a significant delay from earlier timelines.

The Ohio project remains one of the most high-profile initiatives backed by the CHIPS and Science Act, a $53 billion program passed in 2022 to support semiconductor research, development, and manufacturing in the U.S.

“The Biden administration literally was giving Intel for free, and giving TSMC money for free, and all these companies just giving them money for free,” Lutnick said. “Donald Trump turns that into saying, ‘Hey, we want equity for the money. If we’re going to give you the money, we want a piece of the action.'”

Intel’s Struggles in AI and Leadership Shake-Up

Intel has lagged behind rivals Nvidia and AMD in capitalizing on the artificial intelligence boom. The company has poured billions into standing up a manufacturing business, but has yet to secure a significant long-term customer. Intel tapped Lip-Bu Tan as CEO in March after ousting Pat Gelsinger in December.

Tan’s leadership has already been controversial. He met with Trump at the White House last week after the president publicly called for his resignation, citing concerns over his alleged ties to China.

SoftBank’s Strategic Bet

SoftBank’s $2 billion bet on Intel marks one of the most significant U.S. investments by the Japanese group since Trump urged global firms to channel more capital into American advanced manufacturing. SoftBank founder Masayoshi Son has previously pledged tens of billions toward U.S. technology ventures, aligning with Trump’s broader effort to restore the country’s leadership in critical sectors.

With Trump’s administration pushing to secure domestic control of chipmaking, Intel finds itself at the center of both government oversight and foreign strategic investment. The looming equity arrangement, if finalized, would represent a historic precedent: one that gives the U.S. government a sizable stake in one of its most important technology companies.

A new report from MIT’s NANDA initiative, The GenAI Divide: State of AI in Business 2025, has revealed a striking reality: while generative AI promises to reshape business operations, most corporate attempts to harness it are falling short.

According to the study published by Fortune, which draws on 150 leadership interviews, a survey of 350 employees, and an analysis of 300 public deployments, only about 5% of generative AI projects are achieving rapid revenue acceleration. The overwhelming majority either stall or deliver minimal measurable impact on profitability.

Speaking on the findings, Aditya Challapally, the lead author and research contributor to MIT’s NANDA project, said a small fraction of organizations—often startups—are excelling because of their sharp focus and strategic partnerships.

“Startups led by 19- or 20-year-olds have seen revenues jump from zero to $20 million in a year,” Challapally explained. “They pick one pain point, execute well, and partner smartly with companies who use their tools.”

For the remaining 95%, the gap is less about the AI models themselves and more about how businesses integrate them. MIT describes this as a “learning gap”—both on the side of the tools, which often fail to adapt to enterprise workflows, and the organizations, which struggle to embed AI meaningfully into processes.

While executives often cite regulatory barriers or imperfect model performance, MIT’s findings suggest the real issue lies in enterprise adoption strategies. Off-the-shelf AI models such as ChatGPT work well for individuals because of their flexibility, but inside businesses, they often underperform without workflow-specific adaptation.

Misaligned Investments

One of the most striking insights from the report is that more than half of corporate AI budgets in 2025 are flowing into sales and marketing tools. Yet, MIT’s data shows the highest returns are being realized in back-office automation, where AI can cut outsourcing costs, reduce reliance on agencies, and streamline repetitive operations.

This mismatch between investment and impact underscores why so many initiatives fail to deliver revenue growth.

“Almost everywhere we went, enterprises were trying to build their own tool,” Challapally noted.

But the report found that companies that purchased AI tools from specialized vendors succeeded 67% of the time, while internal builds had only a one-in-three success rate.

Sectoral Implications

The divide is particularly visible in financial services and other regulated sectors, where companies are racing to build proprietary AI models. MIT’s findings suggest this strategy is backfiring: instead of establishing a competitive advantage, many firms are wasting resources on systems that struggle to scale or adapt.

Workforce Shifts

The study also points to workforce disruption already underway. While fears of mass layoffs have not materialized, businesses are quietly reshaping their labor forces by not replacing workers in administrative and customer support roles as they leave. Much of this work was already outsourced, making AI’s entry a direct replacement for offshore contractors rather than core employees.

Shadow AI

Another concern is the rise of “shadow AI”—unsanctioned employee use of tools like ChatGPT to bypass internal restrictions. This poses compliance and security risks, particularly for industries handling sensitive data. Meanwhile, companies still struggle to measure AI’s real impact on productivity, making it difficult to justify investments internally.

What Works: Lessons from Success Stories

MIT’s research highlights several common features of successful deployments:

• Empowering line managers—not just central AI labs—to lead adoption.
• Choosing tools that can integrate deeply and adapt over time.
• Partnering with vendors instead of attempting costly solo builds.

Looking ahead, the most advanced firms are already experimenting with agentic AI systems—models that can remember, learn, and act autonomously within defined boundaries. These tools could mark the beginning of AI as a proactive decision-making partner in business, rather than just a passive assistant.

Challapally concluded that the next frontier will depend less on raw model power and more on organizational agility: “Companies that adapt their workflows and decision-making structures to AI will pull ahead. Those that don’t risk being stuck in endless pilots that never deliver.”