Lead Tech Analyst and CEO Beth Kindig recently joined Real Vision’s Nico Brugge to discuss her AI outlook on leading AI stock Nvidia, while sharing which AI stock she believes may outpace Nvidia’s returns through 2030.

This AI stock’s opportunity is in the AI inference market, which will begin to take shape when large language models (LLMs) migrate and operate locally on AI-capable client devices, such as PCs and smartphones. Kindig has boldly stated in Forbes, and on CNBC, and Bloomberg that Nvidia will reach a $10 trillion valuation by 2030. Yet, she believes this AI stock may outpace Nvidia’s stock and provide investors with a larger percentage return.

Click here to watch the full interview on RealVision.

We built a leading AI portfolio beginning with Nvidia’s AI thesis in 2018, with our AI allocation of 45% in 2023 helping push us to a 131% cumulative return since inception. Now, we’re closely tracking what we believe is one of the next explosive growth waves in AI – and it’s not the cloud. Learn more here.

Training Versus Inference

Nvidia had surged to briefly become the world’s most valuable company due to its impenetrable moat in the data center GPU market, which was built upon the CUDA software platform for the purposes of training AI models. Eventually, we will see a shift from AI training to AI inference, which leaves the market open for competitors.

Kindig explained in the interview with Brugge that Nvidia’s H100 transformer engine was the impetus for Chat-GPT’s moment. Chat-GPT, and its competitors, are essentially large R&D departments for training models. We are in the midst of AI training, and what follows will be the AI inference market. As Kindig explains, “when you take the models and you bring them to the edge, and you run those models and have it make predictions based on live data for actionable results, that’s inference.” She pointed out that “for the most part, it’s agreed that inference will be a larger market than training once the ecosystem is mature.”

Currently, there’s one primary headwind to the inference market; devices are not powerful enough to handle the requirements to run AI at the edge. Kindig says that “one of the things holding back inference is our client devices, so our PCs and mobile. Inference runs best close to the data, and we don’t have powerful enough devices for inference, for where AI needs to go.”

AI PCs are currently working on solving this critical bottleneck, with NPU, GPU and CPU equipped devices packing the necessary power and efficiency to operate AI models locally, on-device and without relying on data being sent to and from the cloud.

Kindig told Brugge on Real Vision that she believes one AI stock is well positioned to capitalize on the long-term opportunity arising in AI inference -- that stock is AMD.

Why AMD Can Outpace Nvidia Through 2030

Nvidia will need to rise nearly 250% by 2030 to reach Kindig’s $10 trillion target, yet she thinks AMD has the potential to provide a larger return over that time frame.

She told Brugge that her “time horizon would be that we see really nice movement by 2027, but we really need this 2030 time period to play out, and there’s a few reasons. Number one, Nvidia has the training market cornered right now. Training requires a lot of compute power, and they’ve gone through architectural changes that have defied Moore’s Law. This is things like Tensor Cores, which do matrix computations; floating-point precision, moving from 16 point [FP16] to 8 point [FP8], [the transformer engine switches back and forth which] increases accuracy while also increasing speed [depending on the workload]. So, all of those things, Nvidia has 98% of the GPU market and is crushing it, but a lot of that is training.”

Core to this thesis on AMD is giving time for the budding inference market to take off and mature – Kindig explains that “where AMD is going to compete with Nvidia is a market that is very early, so we need time for that to mature, which is inference. Many people may get that confused, because we are fully in the AI market today because Nvidia is putting up those huge data center numbers. We are in the data center training market today; one day, we will be an AI market led by inference.”

Kindig told Brugge that there are a “few reasons” that AMD could do better than Nvidia in inference and etch a niche, with the primary reason being that inference is “one way to circumvent CUDA.” CUDA is Nvidia’s proprietary software stack that has essentially locked developers into its GPU ecosystem, and what has driven its ~98% market share in AI GPUs.

For a deep dive on CUDA and how it’s Nvidia’s moat and first line of defense in the AI accelerator market, read more here and here.

How AMD Can Fend Off Nvidia

AMD is equal to Nvidia on hardware in many regards, but CUDA has locked in Nvidia’s monopoly; however, it’s likely that Big Tech and developers will seek alternatives to CUDA to limit reliance on Nvidia for the entirety of the hardware stack for AI development.  

Kindig notes that CUDA will be the “biggest hurdle for sure” for AMD to compete against, “but after that, it’s probably product roadmap versus product roadmap, meaning that for everything AMD does, can Nvidia do better, by 6 months.”  Put differently, Nvidia took the industry by storm with its transformer engine-equipped H100s, which saw extreme demand outstrip supply for multiple quarters. No company could compete at the time with a similarly spec’d GPU that could provide the same level of AI computing performance.

Now, AMD is accelerating its product roadmap cycle to align with Nvidia’s, after being a generation behind. AMD is aiming to launch its MI400 lineup in 2026 alongside Nvidia’s Rubin platform, catching up in the release cycle after being behind the GB200 with its MI350x accelerators.

AMD has an edge over Nvidia in that it is undercutting them quite heavily on price, though this is detrimental to margins and thus bottom line growth. Kindig explains that this “incentive of saving $20,000 or more [per GPU] is big enough for these companies that are building these huge data centers, that they’re likely to try their very best to make this work with their in-house engineering departments. This is Big Tech only. This will not apply to enterprises or small businesses, which won’t have the time or resources to do anything other than CUDA.”

At scale, that $20,000 savings for a GPU with similar compute performance capabilities and similar memory bandwidth, albeit with AMD’s software instead of CUDA, can entice companies to shift towards allocating some of the tens of billions flowing to Nvidia’s chips to AMD in the long-run.

For example, Microsoft is reportedly aiming to triple its GPU supply this year, from 600,000 GPUs to 1.8 million GPUs, and is a customer of both Nvidia and AMD. As AI accelerator purchases increase in size and scale, with upgrades to the latest generation for performance improvements and decreasing TCOs, Big Tech can save billions by allocating a fraction to AMD – hypothetically speaking, allocating one-third of a 1.2 million GPU purchase could save $8 billion with AMD’s pricing. That $8 billion could then be deployed to purchase more GPUs, train the next generation of AI models, and otherwise remain ahead of stiff competition.

Kindig explains that this is both an “opportunity and a risk that AMD undercut so much on price, because their margins will not look as good as Nvidia’s. Nvidia has been an amazing stock not only because of these revenue beats, but because the margins and the pricing power that CUDA has created” has driven 600% growth on the bottom line that AMD won’t be able to replicate.

Analysts foresee strong growth for AMD on both the top and bottom lines over the next few years, though it pales in comparison to Nvidia’s streak of blazing triple-digit growth rates. AMD’s revenue growth is forecast to accelerate from under 13% in 2024 to 28% in 2025, before moderating to 18% in 2026. Adjusted EPS growth is expected to accelerate from 32% to 59% in 2025.

Source: Seeking Alpha

While it is by no means the triple digit growth that Nvidia has been putting up, these top and bottom line accelerations are what has been rewarded by the market, especially for AI stocks. Because of the differing growth rates, AMD trades at a cheaper valuation than Nvidia: currently, AMD is valued at 7.8x 2025 revenue and 28.3x adjusted EPS, versus 19.2x revenue and 34.7x adjusted EPS for Nvidia for the same period. However, both companies are currently trading above long-term historical averages for these valuation multiples, with AMD trading above its 10-year average 4.3x revenue multiple and Nvidia above its 10-year average of 14.0x revenue.

Conclusion

Nvidia has greatly rewarded investors as it quickly ascended to be the pinnacle of the generative AI revolution of 2023 and 2024, with revenue consistently exceeding expectations so far on robust demand. Beth Kindig and the I/O Fund have projected Nvidia to potentially rise to a $10 trillion valuation by 2030 on strong data center growth from its rapid GPU roadmap and upcoming software and automotive opportunities, but Kindig believes that AMD and its opportunity in AI inference may help the stock outpace Nvidia’s projected 250% return through 2030.

Click here to watch the full interview on RealVision.

For more insights on AMD, consistent deep dive research on AI stocks and mega-trends, weekly webinars with AI stock and broad market outlooks, real-time trade alerts on AI stock buys and sells, consider taking a look at the I/O Fund’s premium services here.

Disclaimer: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in NVDA and AMD at the time of writing and may own stocks pictured in the charts.

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