Nvidia has been the biggest star of the AI show so far, but another semiconductor stock could carry even more upside in the long run.
When investors think about artificial intelligence (AI) and the chips powering this technology, one company tends to dominate the conversation: Nvidia (NVDA). It has become an undisputed barometer for AI adoption, riding the wave with its industry-leading GPUs and the sticky ecosystem of its CUDA software that keep developers in its orbit. Since the launch of ChatGPT about three years ago, Nvidia stock has surged nearly tenfold.
Here’s the twist: While Nvidia commands the spotlight today, it may be Taiwan Semiconductor Manufacturing(TSM -0.68%) that holds the real keys to growth as we look toward the next decade. Below, I’ll unpack why Taiwan Semi — or TSMC, as it’s often called — isn’t just riding the AI wave, but rather is building the foundation that brings the industry to life.
Taiwan Semi is the most influential foundry business on the planet
What makes Taiwan Semi so critical is its role as the backbone of the semiconductor ecosystem. Its foundry operations serve as the lifeblood of the industry, transforming complex chip designs into the physical processors that power myriad generative AI applications.
TSMC manufactures GPUs designed by Nvidia, CPUs for Advanced Micro Devices, and a widening range of custom silicon that cloud hyperscalers are using to optimize AI workloads more efficiently. Today, Taiwan Semi dominates the global foundry market with roughly 68% share of industry revenue — leaving rivals like Samsung Electronics in a distant second place with just 8%.
Image source: Getty Images.
Why might TSMC stock outperform Nvidia or AMD?
One of the louder bear cases against Nvidia and AMD is the growing adoption of application-specific integrated circuits (ASICs). Hyperscalers are becoming highly motivated to design their own silicon — not only to fine-tune training performance for AI models, but also to reduce reliance on incumbents and push back against their pricing power.
The trend is already visible: Alphabet‘s Google is rolling out its tensor processing units (TPU), Amazon is deploying its Trainium and Inferentia chips, while Microsoft is experimenting with its own AI accelerators.
For Nvidia and AMD, this shift could translate into slower growth as spending that once flowed directly toward their GPUs is instead redirected to internally developed hardware. For these enterprises, vertical integration isn’t just a budgeting exercise; it’s a strategic hedge against dominating third-party suppliers.
For TSMC, however, these dynamics look quite different. Custom ASICs still need a manufacturer, and Taiwan Semi’s existing footprint in advanced fabrication services makes it a logical partner. In essence, TSMC is less vulnerable to which specific chip design gains momentum. Rather, the company is positioned as a neutral beneficiary riding the secular tailwinds fueling trillions of dollars being poured into AI infrastructure.
Is Taiwan Semi stock a good buy right now?
For investors, the central question boils down to durability in an increasingly competitive AI landscape. With its forward price-to-earnings (P/E) multiple peaking near 50 during the height of the AI frenzy, Nvidia is perhaps the most defining symbol of AI euphoria. Even after cooling off, the stock still trades at 38 times its forward earnings — meaningfully elevated over its three-year average.
While this premium underscores the market’s confidence, it also leaves little margin for error. Any slowdown in demand across compute and networking — or mounting competition from custom silicon — could put downward pressure on Nvidia’s lofty valuation multiple.
By contrast, TSMC’s valuation tells a different story. Despite being the underlying enabler of Nvidia, AMD, and hyperscalers alike, Taiwan Semi has not enjoyed the same degree of valuation expansion. To me, this suggests that the market has yet to fully price in TSMC’s critical role at the intersection of AI development, infrastructure, and manufacturing.
As AI infrastructure spending accelerates, Taiwan Semi is uniquely positioned as an agnostic winner, as the company stands to benefit regardless of which chip designer is featured most prominently in the spotlight. By 2030, TSMC won’t just be part of the AI story — it likely will be seen as a critical chapter supporting the entire ecosystem.
For long-term investors, this makes TSMC stock a no-brainer opportunity to buy and hold — one poised to outperform even today’s most hyped semiconductor names.
Adam Spatacco has positions in Alphabet, Amazon, Microsoft, and Nvidia. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Amazon, Microsoft, Nvidia, and Taiwan Semiconductor Manufacturing. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.
TransHumanity Ltd., a spinout from Loughborough University, has secured approximately £400,000 in pre-seed investment. The round was led by SFC Capital, the UK’s most active seed-stage investor, with additional investment from Silicon Valley-based Plug and Play.
TransHumanity’s vision is to empower faster, smarter human decisions by transforming data into accessible intelligence using large language model based agentic AI.
Agentic AI refers to artificial intelligence systems that collaborate with people to reach specific goals, understanding and responding in plain English. These systems use AI “agents” — models that can gather information, make suggestions, and carry out tasks in real time — helping people solve problems more quickly and effectively.
TransHumanity’s first product, AptIq, is designed to help transport authorities quickly analyse transport data and models, turning days of analysis into seconds.
By simply asking questions in plain English, users can gain instant insights to support key initiatives like congestion reduction, road safety, creation of business cases and net-zero targets.
Dr Haitao He, Co-founder and Director of TransHumanity, said: “I am proud to see my rigorous research translated into trusted real-world AI innovation for the transport sector. With this investment, we can now realise my Future Leaders Fellowship vision, scaling a technology that empowers authorities across the UK to deliver integrated, net-zero transport.”
Developed from rigorous research by Dr Haitao He, a UKRI Future Leaders Fellow in Transport AI at Loughborough University, AptIq, previously known as TraffEase, has already garnered significant recognition.
The technology was named a Top 10 finalist for the 2024 Manchester Prize for AI innovation and was recently highlighted as one of the Top 40 UK tech start-ups at London Tech Week by the UK Department for Business and Trade.
Adam Beveridge, Investment Principal at SFC Capital, said: “We are excited to back TransHumanity. The combination of cutting-edge research, a proven founding team, clear market demand, and positive societal impact makes this exactly the kind of high-growth venture we are committed to supporting.”
AptIq is currently in a test deployment with Nottingham City Council and Transport for Greater Manchester, with plans to expand to other city, regional, and national authorities across the UK within the next 12 months.
With a product roadmap that includes diverse data sources, advanced analytics and giving the user full control over the AI tool when required, interest from the transport sector is already high. Professor Nick Jennings, Vice-Chancellor and President of Loughborough University, noted: “I am delighted to see TransHumanity fast-tracked from lab to investment-ready spinout.
This journey was accelerated by TransHumanity’s selection as a finalist in the prestigious Manchester Prize and shows what’s possible when the University’s ambition aligns with national innovation policy.”
An oversimplified approach I have taken in the past to explain wisdom is to share that “We don’t know what we don’t know until we know it.” This absolutely applies to the fast-moving AI space, where unknowingly introducing legal and compliance risk through an organization’s use of AI is a top concern among IT leaders.
We’re now building systems that learn and evolve on their own, and that raises new questions along with new kinds of risk affecting contracts, compliance, and brand trust.
At Broadcom, we’ve adopted what I’d call a thoughtful ‘move smart and then fast’’ approach. Every AI use case requires sign-off from both our legal and information security teams. Some folks may complain, saying it slows them down. But if you’re moving fast with AI and putting sensitive data at risk, you’re also inviting trouble if you don’t also move smart.
Here are seven things I’ve learned about collaborating with legal teams on AI projects.
1. Partner with Legal Early On
Don’t wait until the AI service is built to bring legal in. There’s always the risk that choices you make about data, architecture, and system behavior can create regulatory headaches or break contracts later on.
Besides, legal doesn’t need every answer on day one. What they do need is visibility into the gray areas. What data are you using and producing? How does the model make decisions? Could those decisions shift over time? Walk them through what you’re building and flag the parts that still need figuring out.
2. Document Your Decisions as You Go
AI projects move fast with teams needing to make dozens of early decisions on everything from data sources to training logic. So, it’s only natural that a few months later, chances are no one remembers why those choices were made. Then someone from compliance shows up with questions about those choices, and you’ve got nothing to point to.
To avoid that situation, keep a simple log as you work. Then, should a subsequent audit or inquiry occur, you’ll have something solid to help answer any questions.
3. Build Systems You Can Explain
Legal teams need to understand your system so they can explain it to regulators, procurement officers, or internal risk reviewers. If they can’t, there’s the risk that your project could stall or even fail after it ships.
I’ve seen teams consume SaaS-based AI services without realizing the provider could swap out a backend AI model without their knowledge. If that leads to changes in the system’s behavior behind the scenes, it could redirect your data in ways you didn’t intend. That’s one reason why you’ve got to know your AI supply chain, top to bottom. Ensure that services you build or consume have end-to-end auditability of the AI software supply chain. Legal can’t defend a system if they don’t understand how it works.
4. Watch Out for Shadow AI
Any engineer can subscribe to an AI service and accept the provider’s terms without knowing they don’t have the authority to do that on behalf of the company.
That exposes the organization to major risk. An engineer might accidentally agree to data-sharing terms that violate regulatory restrictions or expose sensitive customer data to a third party.
And it’s not just deliberate use anymore. Run a search in Google and you’re already getting AI output. It’s everywhere. The best way to avoid this is by building a culture where employees are aware of the legal boundaries. You can give teams a safe place to experiment, but at the same time, make sure you know what tools they’re using and what data they’re touching.
5. Help Legal Navigate Contract Language
AI systems get tangled in contract language; there are ownership rights, retraining rules, model drift, and more. Most engineers aren’t trained to spot those issues, but we’re the ones who understand how the systems behave.
That’s another reason why you’ve got to know your AI supply chain, top to bottom. In this case, when legal needs our help in reviewing vendor or customer agreements to put the contractual language into the appropriate technical context. What happens when the model changes? How are sensitive data sets safeguarded from being indexed or accessed via AI agents such as those that use Model Context Protocol (MCP)? We can translate the technical behavior into simple English—and that goes a long way toward helping the lawyers write better contracts.
6. Design with Auditability in Mind
AI is developing rapidly, with legal frameworks, regulatory requirements, and customer expectations evolving to keep pace. You need to be prepared for what might come next.
Can you explain where your training data came from? Can you show how the model was tested for bias? Can you justify how it works? If someone from a regulatory body walked in tomorrow, would you be ready?
Design with auditability in mind. Especially when AI agents are chained together, you need to be able to prove that identity and access controls are enforced end-to-end.
7. Handle Customer Data with Care
We don’t get to make decisions on behalf of our customers about how their data gets used. It’s their data. And when it’s private, it shouldn’t be fed to a model. Period.
You’ve got to be disciplined about what data gets ingested. If your AI tool indexes everything by default, that can get messy fast. Are you touching private logs or passing anything to a hosted model without realizing it? Support teams might need access to diagnostic logs but that doesn’t mean third-party models should touch them. Tools are rapidly evolving that can generate comparable synthetic data devoid of any customer private data that could help with support use cases for example, but these tools and techniques should be fully vetted with your legal and CISO organizations prior to using them.
The Reality
The engineering ethos is to move fast. But since safety and trust are on the line, you need to move smart, which means it’s okay if things take a little longer. The extra steps are worth it when they help protect your customers and your company.
Nobody has this all figured out. So ask questions by talking to people who’ve handled this kind of work before. The goal isn’t perfection—it’s to make smart, careful progress. For enterprises, the AI race isn’t a question of “Who’s best?” but rather “Who’s leveraging AI safely to drive the best business outcomes.”
Progress Software, a company offering artificial intelligence-powered digital experience and infrastructure software, has launched Progress Federal Solutions, a wholly owned subsidiary that aims to deliver AI-powered technologies to the federal, defense and public sectors.
Progress Federal Solutions to Boost Digital Transformation
The company said Monday the new subsidiary, announced during the Progress Data Platform Summit at the International Spy Museum in Washington, D.C., is intended to fast-track federal agencies’ digital modernization efforts, meet compliance requirements, and advance AI and data initiatives. The subsidiary leverages MarkLogic’s data management and integration expertise, a platform that Progress Software acquired in 2023.
Progress Federal Solutions functions independently but will offer the company’s full technology portfolio, including Progress Data Platform, Progress Sitefinity, Progress Chef, Progress LoadMaster and Progress MOVEit. These will be available to the public sector through Carahsoft Technology‘s reseller partners and contract vehicles.
Remarks From Progress Federal Solutions, Carahsoft Executives
“Federal and defense agencies are embracing data-centric strategies and modernizing legacy systems at a faster pace than ever. That’s why we focus on enabling data-driven decision-making, faster time to value and measurable ROI,” said Cori Moore, president of Progress Federal Solutions.
“Progress is a trusted provider of AI-enabled solutions that address complex data, infrastructure and digital experience needs. Their technologies empower government agencies to build high-impact applications, automate operations and scale securely to meet program goals,” said Michael Shrader, vice president of intelligence and innovative solutions at Carahsoft.
