MarketBeat hosts Keith Kaplan of TradeSmith on a bullish edge-AI thesis: the next major AI investment wave is moving from cloud/data-center infrastructure into AI deployed inside physical devices, factories, vehicles, hospitals, and defense systems. He argues the opportunity is still early, likely to play out over 5-10 years, and names Honeywell, Vertiv, and One Stop Systems as different ways to express the theme.
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Keith Kaplan’s core argument is that investors should stop thinking of AI as only ChatGPT, cloud software, and data centers, because the next large investment wave is edge AI: models embedded directly into machines that must make decisions with very low latency, often without reliable internet. He frames AI adoption as a sequence of waves—first semiconductors and data centers, then software, and now physical deployment into the real world. In his view, that third wave is only beginning, and it is the one most investors still underestimate. He supports this thesis with practical examples. He says cloud AI can tolerate delay, but edge AI cannot: a Tesla at highway speed, a John Deere combine in a field, or a BMW assembly line all require local processing. …
Tactically, the trade is a theme rotation into edge-AI enablers, but it’s early and still sensitive to valuation, so pullbacks in the names are a real risk. Honeywell is the cleaner near-term expression, while Vertiv and especially One Stop Systems carry more momentum/speculation risk.
Over the next few months, the setup depends on whether edge-AI deployment becomes visible in industrial, defense, and medical use cases rather than staying a concept trade. If that rollout accelerates, the market could keep re-rating hardware and infrastructure names tied to the physical layer of AI.
The structural thesis is that AI value creation expands from cloud software into the physical world, where latency, ruggedization, and local compute become enduring moats. If that regime shift holds, the winners will include industrial incumbents and niche hardware providers, not just the obvious AI software and chip leaders.
The edge AI market is projected to grow from $11.8 billion in 2025 to nearly $60 billion by 2030, representing roughly 5x growth.
Speaker cites market projections for edge AI growth from 2025 to 2030.
Edge AI accounts for approximately 35% of Honeywell's total revenue.
Speaker makes a specific revenue attribution claim about Honeywell's edge AI exposure.
Vertiv's addressable market will scale directly with edge node proliferation from thousands to millions of locations, causing a hockey-stick growth inflection.
Speaker argues that as edge computing nodes multiply, Vertiv's power and thermal solutions become essential at each location, driving exponential growth.
Given the volatility we're seeing in the market, how can any industry boom right now?
Keith acknowledges the volatility but explains that the AI boom moves in waves — semiconductors/datacenters were wave one, memory/optics/cooling wave two, and the next wave is edge computing. He argues this trend 'is not going anywhere' and is where investors should focus.
What is the real difference between cloud AI and edge AI?
Keith explains that cloud AI is like a genius locked in a room miles away — always a delay, sometimes no response when internet is down. Edge AI is like a capable person who rides with you always — super fast, no delays, always present, never dependent on a signal. He gives the example of Tesla needing to process braking decisions in 100 milliseconds, which is too fast for a cloud round-trip.
How big could the edge AI market be and will it change the entire AI story?
Keith describes three waves: infrastructure (mostly closed), software (largely priced in), and now the deployment wave — edge AI. He says the edge AI market was $11.8B in 2025, projected at nearly $60B by 2030 (37% annually), and he thinks it will 'hockey stick' before then. Demand will spread across every autonomous vehicle, smart factory, hospital, satellite, power grid, and eventually every home.
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