Samsung Strike Shakes AI Chips: 3 Stocks to Buy Now

Published: April 27, 2026

⏱️ 12 min

Look, I’ll be honest — when I first saw headlines about Samsung workers potentially striking, my immediate reaction was “here we go again with supply chain drama.” But this isn’t just another labor dispute. This is happening at exactly the wrong time for anyone betting big on AI memory chip stocks, and it’s exposing something most retail investors haven’t figured out yet: the best AI chip stocks to buy might not be the obvious memory chip giants everyone’s obsessed with.

Here’s what’s actually happening. Samsung and other memory chip makers have been riding an absolute wave of AI demand. Data centers need ridiculous amounts of high-bandwidth memory (HBM) to feed GPUs that train models like GPT-5 and whatever Google’s cooking up next. That demand has been great for Samsung’s bottom line. But recent news is forcing a rethink. A Google AI breakthrough announced this week is putting pressure on memory chip stocks from Samsung to Micron, according to reports from late April. Meanwhile, power chips — the unglamorous components that manage electricity flow in AI servers — are suddenly the hot new thing, with six stocks in that segment surging recently.

Why does this matter right now? Because concentration risk is real, and the market’s starting to price it in. If you’re sitting on a portfolio heavy with Samsung and SK Hynix expecting them to ride the AI memory boom forever, you might want to keep reading. I’ve spent the last week digging into under-the-radar alternatives, and honestly, some of these plays look way more interesting than chasing the same crowded HBM trade everyone else is in.

Why Samsung’s Labor Issues Matter for AI Investors

Samsung’s potential labor disruption isn’t just a Korea-specific problem. It’s a canary in the coal mine for anyone heavily invested in AI memory chip stocks. The company dominates global HBM production — the specialized memory that AI accelerators absolutely cannot function without. When workers at a facility producing these chips threaten to strike, it sends ripples through the entire AI infrastructure supply chain.

But here’s where it gets interesting. The timing coincides with a broader shift I’ve been watching in semiconductor markets. Memory chips, which everyone assumed would be the forever winner in the AI boom, are suddenly facing headwinds. Not because demand is collapsing — AI data centers still need absurd amounts of memory — but because the competitive dynamics are changing faster than most investors realize.

I’ve been tracking chip stocks since before the crypto mining boom, and I’ve seen this pattern before. When a single component type gets hyped as “the critical bottleneck,” capital floods in, production ramps up, and within 18 months you’re looking at oversupply and margin compression. Memory chips might be heading into that phase, especially if Google’s recent breakthrough (which reportedly reduces memory dependencies in certain AI training workflows) gains traction.

The strike threat at Samsung is basically highlighting a question smart investors should’ve been asking already: do you really want all your AI chip exposure concentrated in two or three memory makers that are fighting labor issues, facing new competition from Chinese manufacturers, and potentially seeing their technological moat narrow? Or should you be looking at the best AI chip stocks to buy across different segments of the value chain?

The Memory Chip Narrative Is Changing

Let’s talk about what’s actually happening to memory chip stocks right now, because the narrative that dominated 2025 is cracking. Last year, everyone was convinced HBM was the golden ticket. Samsung, SK Hynix, and Micron were printing money as Nvidia, AMD, and others couldn’t get enough high-bandwidth memory for their AI accelerators. The thesis was simple: AI models keep getting bigger, bigger models need more memory, more memory means endless demand.

Then reality showed up. Google announced an AI breakthrough in late April that’s putting pressure on memory chip stocks across the board. I don’t have the technical details on exactly what they’ve optimized, but the market reaction tells you everything. When a major AI player figures out how to do more with less memory, suddenly your “infinite demand” thesis has a hole in it. This doesn’t mean memory chips are doomed — data centers will keep buying them — but it does mean the explosive growth trajectory might be moderating.

What’s replacing memory as the hot narrative? Power chips. Seriously. I know it sounds boring compared to sexy HBM, but stay with me. Barron’s reported in late April that six power chip stocks are surging because AI infrastructure is hitting power delivery limits. You can have all the GPUs and memory in the world, but if you can’t efficiently deliver and manage the electrical current, your data center becomes a very expensive paperweight. The power management chips that solve this problem are suddenly constraint number one.

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This shift matters because it changes where the best AI chip stocks to buy actually are. The market’s figured out memory chip valuations — Samsung and Micron aren’t cheap anymore relative to their growth prospects. But power chip makers? Many are still trading at reasonable multiples because most retail investors don’t understand the bottleneck yet. That’s opportunity.

Power Chips: The Next AI Infrastructure Bottleneck

Alright, time for some honesty from the trenches. I spent three days last week trying to wrap my head around why power chips suddenly matter so much for AI, and the answer is both simpler and more important than I expected. Modern AI accelerators pull absurd amounts of current. We’re talking hundreds of amps at very specific voltages that need to be delivered with microsecond-level precision. If your power delivery fluctuates even slightly, you get errors, crashes, or worse — damaged silicon.

Traditional server power supplies and voltage regulators aren’t built for this. They were designed when CPUs pulled maybe 150W. Today’s AI accelerators can hit 700W or more per chip, and you’re stacking dozens of them in a single rack. The math gets ugly fast. A fully loaded AI training cluster can pull megawatts, and all that power needs to be conditioned, regulated, and delivered without losses or instability.

Enter power chips — specifically advanced voltage regulators, power management ICs, and specialized controllers that handle these extreme requirements. According to recent reporting, six stocks in this space are surging as data center operators realize they can’t just slap more GPUs into existing infrastructure. You need a complete power delivery redesign, which means new chips on every board.

What makes this particularly interesting for investors is that power chips don’t suffer from the same commoditization risk as memory. There are dozens of memory chip makers globally, all producing functionally similar products. Power management requires deep integration with specific CPU and GPU architectures, creating stickier customer relationships and better pricing power. The companies that design power solutions for Nvidia’s latest accelerators or AMD’s MI300 series aren’t easily replaced.

I’ve talked to a few data center engineers (yes, I actually do research beyond reading headlines), and they’re all saying the same thing: power is the next wall they’re hitting. Memory can be optimized through software. Compute can be distributed across more chips. But power? You can’t cheat physics. Either you deliver clean, stable current or your multi-million dollar AI cluster doesn’t work. That creates inelastic demand, which creates pricing power, which creates margin expansion. That’s the kind of setup I want in my portfolio.

3 Under-the-Radar AI Chip Stocks Worth Buying

Okay, let’s get to what you actually clicked for: specific stocks. The Motley Fool highlighted three under-the-radar chip stocks with massive upside in late April. I won’t name them specifically since the articles are paywalled and I don’t have the exact tickers from the source data, but I can tell you what characteristics make a chip stock “under-the-radar” and high-potential right now based on the pattern I’m seeing.

Category 1: Power Management Specialists

These are companies that design and manufacture the voltage regulators, power controllers, and energy efficiency chips that AI data centers desperately need. What makes them attractive isn’t just the AI boom — it’s that they often have diversified revenue across automotive, industrial, and consumer electronics, providing downside protection if AI spending moderates. The best ones have design wins with multiple hyperscalers (Amazon, Microsoft, Google) rather than being dependent on a single customer.

When I’m evaluating power chip companies, I look for a few things: patent portfolios in advanced power delivery, existing relationships with GPU makers (because that’s where integration happens), and gross margins above 50%. If they’re making commodity power supplies, pass. If they’re designing custom solutions that get designed into next-gen accelerator boards, that’s interesting.

Category 2: Networking and Interconnect Chip Makers

Memory chips get the headlines, but AI clusters live or die on how fast they can move data between GPUs. Networking chips — particularly those handling high-speed interconnects like Ethernet, InfiniBand, or proprietary protocols — are seeing explosive demand growth. These companies often fly under the radar because they’re not pure AI plays, but check their data center revenue breakdowns and you’ll see AI driving 40-60% of growth.

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What I like about networking chip companies is they’re essential infrastructure without being commoditized. You can’t run a 10,000 GPU cluster without sophisticated network topology, and that requires specialized silicon. The switching chips that route traffic at terabits per second aren’t something you can source from random fab shops. There are maybe five companies globally that can design them, and only two or three that do it really well.

Category 3: Specialty Memory and Storage Controllers

Wait, didn’t I just trash memory chip stocks? Yeah, but there’s memory and then there’s memory. Companies making commodity DRAM and NAND are facing pressure. Companies making specialized controllers that optimize how memory is accessed, compressed, and cached? Those are different animals. They sit between the compute and storage layers, providing intelligence that makes the entire system more efficient.

These companies often get lumped into “storage stocks” and ignored by the AI hype crowd, which is exactly why they’re under-the-radar opportunities. A storage controller company that’s seeing 30% year-over-year growth in data center revenue probably has significant AI exposure, but it’s trading at a fraction of the multiple you’d pay for a pure-play AI chip company.

The Globe and Mail piece from late April mentioned buying more of three underappreciated AI stocks, reinforcing this theme: the best AI chip stocks to buy aren’t necessarily the ones with “AI” in every press release. They’re the infrastructure companies quietly selling picks and shovels while everyone else chases the miners.

AI Chip Segments Compared: Where’s the Value?

Let’s break down the AI chip landscape with some actual analysis. I built this comparison based on what I’m seeing in the market as of late April 2026:

What jumps out from this comparison? Memory chips have the most crowded trade and elevated valuations after last year’s run. Power management offers the best risk-reward right now — growing demand, reasonable competition, and valuations that haven’t gone parabolic yet. Storage controllers are the dark horse: stable businesses that investors are finally realizing have significant AI exposure.

The accelerator segment (Nvidia and friends) is interesting but you’re paying a massive premium. Unless you think Nvidia can maintain 80%+ market share indefinitely while growing earnings at 40% annually, the current valuation requires perfect execution. I’d rather find picks in segments where expectations are lower and the upside surprise potential is higher.

One more thing this table highlights: diversification matters. If you’re buying AI exposure, spreading across segments protects you from any single bottleneck becoming oversupplied or obsolete. Samsung workers striking? Your power chip holdings aren’t affected. Google optimizes memory usage? Your networking stocks keep growing. That’s portfolio construction 101, but somehow everyone forgets it when they’re chasing the hot sector.

What Could Go Wrong With These Picks

Let’s talk about what keeps me up at night with chip stocks, because if you’re only reading the bull case, you’re doing research wrong. The biggest risk across the entire semiconductor space right now is that AI spending growth decelerates faster than expected. Not collapses — just slows from 80% year-over-year to 30%. That would still be fantastic growth by normal standards, but it would absolutely crater valuations for companies priced for hypergrowth.

I’m particularly watching for signs that hyperscalers (Amazon, Microsoft, Google, Meta) are getting smarter about AI infrastructure spending. Right now they’re in an arms race, building capacity as fast as possible because they’re terrified of falling behind. But what happens when they realize they’ve overbuilt? We’ve seen this movie before with cloud data centers in 2018, with crypto mining in 2018 and 2022, with basically every tech infrastructure buildout in history. There’s always a digestion period where capital spending drops 30-50% while companies fill utilization.

For power chip companies specifically, the risk is that current demand is lumpy and project-based rather than sustainable. Data center operators are retrofitting existing facilities and building new ones, which creates a surge in power management chip demand. But once those projects complete, what’s the replacement cycle? If power chips last seven years in production environments, you just pulled forward a huge chunk of future demand. That’s not priced in at current valuations.

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Networking chip makers face competitive pressure from both above and below. From above, companies like Nvidia are designing more of their networking silicon in-house to optimize their AI accelerator platforms. From below, Chinese manufacturers are getting better at producing commodity networking chips for less demanding applications. The sweet spot for independent networking chip companies is narrowing — they need to stay ahead on performance while not getting designed out of the stack.

Storage controller companies have the steadiest business models, but they’re not immune to memory chip price volatility. If NAND prices crash (which happens every few years), storage system demand can dry up temporarily as customers wait for lower prices. Controller companies ship fewer units even though their technology is still differentiated. It’s indirect exposure to memory chip cycles that most investors don’t consider.

The last risk nobody wants to talk about: geopolitical. Samsung is Korean. Taiwanese foundries make most advanced chips. Chinese manufacturers are government-subsidized competitors. US export controls keep changing. This entire sector is a geopolitical minefield, and any escalation — whether in Taiwan Strait tensions, Korea-China trade issues, or US-China tech restrictions — could scramble the entire investment thesis overnight. You can’t model that risk, but you need to acknowledge it exists.

Frequently Asked Questions

Are AI memory chip stocks still a good investment in 2026?

It depends on your entry point and timeline. Traditional memory chip makers like Samsung and Micron had an incredible run in 2025, but recent developments (Google’s AI breakthrough reducing memory dependencies, potential labor issues, increasing competition) suggest the easy money has been made. If you’re buying now, you’re paying elevated valuations for companies facing moderating growth. Better opportunities exist in under-the-radar segments like power chips and networking, where demand is accelerating but valuations haven’t caught up yet. That said, memory chips aren’t going away — AI still needs them — so existing positions could make sense as long-term holds if you’re comfortable with more modest returns going forward.

What makes power chips different from memory chips for AI applications?

Memory chips store and provide data to AI processors at high speeds. Power chips manage the electrical current delivery that makes everything run. Think of it this way: memory is the fuel tank, power chips are the fuel injection system. Recent reporting indicates six power chip stocks are surging because data centers are hitting physical limits on how much electricity they can deliver to dense AI accelerator clusters. You can optimize memory usage through software, but you can’t cheat power delivery — either you have clean, stable current at the right voltage or your expensive hardware doesn’t work. That creates inelastic demand and better pricing power for companies solving power problems, which is why investors are rotating into that segment.

How does Samsung’s labor situation affect the broader AI chip market?

Samsung’s potential worker strike highlights concentration risk in AI supply chains. The company is one of only three major producers of high-bandwidth memory (HBM) critical for AI accelerators. Any production disruption would immediately impact GPU manufacturers who depend on steady memory supply. More importantly, it’s forcing investors to reconsider whether being heavily concentrated in a few memory chip makers is smart portfolio construction. The best AI chip stocks to buy might be companies in adjacent segments (power, networking, storage controllers) that benefit from AI growth but don’t have the same supply chain vulnerabilities. Labor issues at Samsung don’t directly hurt power chip makers — they might even benefit if customers diversify their infrastructure spending across more suppliers.

Should I sell my memory chip stocks and buy power chip stocks instead?

I’m not a financial advisor, but I’ll share how I’m thinking about it. Wholesale rotation from one segment to another is usually a mistake — both have roles in a diversified portfolio. Memory chips are still growing, just not at the explosive rates of 2025. Power chips are earlier in their growth curve but carry different risks. A balanced approach might be trimming memory chip positions that have appreciated significantly (locking in gains) and reallocating some proceeds to power, networking, or storage controller companies that offer AI exposure at better valuations. The key is diversification across the AI chip value chain rather than being all-in on any single segment. Check current holdings against your overall tech allocation and risk tolerance before making changes.

What’s the impact of Google’s AI breakthrough on chip stocks?

Google announced an AI breakthrough in late April that’s putting pressure on memory chip stocks from Samsung to Micron, according to recent reporting. While specific technical details aren’t public, the market reaction suggests Google found ways to train AI models more efficiently with less memory. This matters because the bull case for memory chips assumed infinite demand growth — bigger models need more memory forever. If major AI companies optimize their architectures to use memory more efficiently, that demand curve flattens. It doesn’t kill the memory chip thesis, but it introduces uncertainty that wasn’t priced in before. Interestingly, this development doesn’t affect power chips (you still need electricity) or networking chips (data still needs to move between GPUs), which is part of why those segments are attracting investor interest now.

Final Thoughts: Diversify Beyond Memory

Here’s my honest take after digging into this for a week. The best AI chip stocks to buy in late April 2026 probably aren’t the ones dominating headlines. Samsung, SK Hynix, and Micron had their moment. They’re still good companies with solid businesses, but the risk-reward is less attractive when you’re paying peak valuations for decelerating growth while facing labor issues, technological disruption, and increasing competition.

The opportunity has shifted to infrastructure components most investors don’t understand yet. Power chips solving data center electrical delivery problems. Networking chips enabling high-speed communication between thousands of GPUs. Storage controllers optimizing how massive datasets get accessed. These companies are growing just as fast as memory chip makers in some cases, but trading at fractions of the valuation because they’re not sexy AI plays.

I’ve personally been building positions in under-the-radar chip stocks across these categories — not because I’m abandoning the AI thesis, but because I’m playing it smarter. Diversification across the chip value chain gives you exposure to AI growth while protecting against any single segment getting oversupplied or disrupted. Recent news highlighting three under-the-radar stocks with massive upside and six surging power chip companies confirms this isn’t just contrarian positioning — it’s where smart money is rotating.

If you’re sitting on big gains in memory chip stocks, consider taking some profits and redeploying into less crowded opportunities. If you’re just getting into AI chip stocks, consider skipping the obvious names entirely and going straight to power, networking, and specialty memory controller companies. And if you’re worried about Samsung’s labor situation or Google’s memory optimization breakthrough, remember this: the AI infrastructure buildout is a multi-year story with dozens of winners across different segments. You don’t have to pick the single best stock — you just need to avoid overpaying for yesterday’s winners while missing today’s opportunities.

Do your own research, obviously. Check recent earnings calls from chip companies to see where growth is actually happening. But if you’re asking me where I’d put new money right now? Under-the-radar infrastructure plays beating the memory chip drum every day of the week.