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The AI boom is reshaping tech portfolios. Discover which semiconductor stocks are buys today and which you should ignore to protect your capital.

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Semiconductor stocks made some of the easiest money on Wall Street this year. Now they’re quickly giving back their gains.The iShares Semiconductor ETF (SOXX), which many investors use to bet on chips, has crossed into bear market territory after falling from its June high.And one closely followed strategist thinks the damage is not finished.Ed Yardeni, the veteran market forecaster behind Yardeni Research, told clients on July 20 that chip stocks have room to fall further, even as the rest of the market holds steady.If you own a chip fund, a memory maker, or a broad tech position, that is a call worth paying attention to.Why Ed Yardeni thinks semiconductor stocks have further to fallYardeni’s warning is clear. He expects the S&P 500 Semiconductors index to drop about 12% more to reach its 200-day moving average, according to Yardeni’s QuickTakes analysis.The 200-day moving average is simply the average closing price over the past roughly 200 trading days. More Chip Stocks:Cathie Wood sells $11.7 million of tumbling semiconductor stockMicron stock jumps as investors look beyond GPUs in AI chip tradeGoldman Sachs turns its back on major semiconductor stockChart watchers treat it as a long-term floor, the line a stock tends to fall back to after a big run.That is the level Yardeni thinks chips are heading toward.The sell-off is already sharp. The SOXX has plunged 20.3% from its June 2 peak, Benzinga reported, the drop that officially defines a bear market.Riskier corners fell even harder. The Roundhill Memory ETF (DRAM) is down roughly 35% from its June 22 high.

Semiconductor stocks have slipped into a bear market after leading the 2026 rally.sinology / Getty Images

What broke while the broader market stayed calmHere is the part that makes this sell-off unusual.The S&P 500 is still sitting near its record, hovering close to the 7,500 level. So most investors checking their broad index funds saw almost nothing wrong.The surface stayed calm while the engine broke.The losses were concentrated in one crowded trade, while everything else held together.Three pressures hit chips at once, according to Yardeni’s note.1. Forced selling out of AsiaMargin calls landed on South Korean chip giants Samsung and SK Hynix (SKHY). When leveraged traders get a margin call, they are forced to sell to cover their positions, and selling affects U.S. memory and chip stocks.2. A cheaper AI model out of ChinaChinese AI lab Moonshot launched Kimi K3, a 2.8-trillion-parameter open-weight model it claims rivals the best from OpenAI and Anthropic.Because it delivers high-end performance at a lower cost, it revived the same fear that hit the market during the DeepSeek scare. The fact that AI hardware may not stay as valuable as investors assumed.3. No buyers left to catch the fallMomentum, or buying stocks simply because they were already rising, was the strategy that most investors crowded into in 2026. Once the selling started, there weren’t enough buyers left on the sidelines to slow it down, and the drop kept feeding on itself.How the chip sell-off unfoldedThe reversal took shape over several weeks:Dec. 7, 2025: Yardeni Research downgrades the S&P 500 Information Technology sector to market weight, an early defensive move.June 2, 2026: The broader market and the SOXX both peak before turning lower.Mid-July 2026: Leveraged liquidation and Moonshot’s Kimi K3 launch hit the sector; South Korea moves to curb high-leverage tech ETFs.July 19-20, 2026: The SOXX confirms a bear market, down 20.3% from its June peak.July 20, 2026: Yardeni issues his call for a further 12% drop.This did not come out of nowhere. Signs of strain had been building for weeks, and it was flagged ahead of TSMC’s July earnings report, when the whole AI trade looked stretched.What Yardeni tells investors to do insteadYardeni’s advice comes down to one idea: Do not try to guess the exact bottom.He warns against “catching falling knives,” which refers to the trap of buying a declining stock, only to watch it fall further.Instead, he favors a sector rotation, moving money out of the hardest-hit group and into areas holding up better.Yardeni’s firm keeps an overweight rating on two sectors:Financials, helped by a strong investment banking environmentHealth care, especially biotechnology, which has weathered the tech dropThe logic is practical. If chips still have room to fall, parking money in steadier value sectors lets you stay invested without absorbing the worst of the fall.What this means for your portfolioThis does not mean chip businesses are broken. Demand for AI hardware is still real, and analysts remain split on where the sector goes next.Even during the slide, Goldman Sachs reset its AMD target higher, and Micron’s (MU) memory pricing story kept some buyers interested.So the question is not about whether chips recover, but when.A few things worth watching before calling a bottom:Whether the SOXX actually reaches and holds its 200-day moving average.Whether forced selling in Korea fully clears out.Whether AI spending from big cloud buyers stays strong into the next round of earnings.If you hold chip stocks for the long term, Yardeni’s call is a reason for caution, not panic. If you were tempted to buy this dip, his message is simple: The floor may not be in yet.Keep in mind that moving averages can break, and rotations can reverse quickly if AI demand exceeds expectations.Yardeni himself remains positive about the wider economy, still predicting a “roaring 2020s” run backed by broad corporate earnings. For now, the takeaway is clear. The chip trade that carried 2026 has cracked, and the strategist who called in the danger thinks there is still more room to fall.Related: Citi sends warning on semiconductor and hyperscaler stocks

TheStreet aims to feature only the best products and services. If you buy something via one of our links, we may earn a commission.Why we love this dealKeeping your house clean can feel like something that consumes you on a daily basis. Whether you’re in a sprawling single family home or a small apartment, keeping dust and dirt at bay isn’t always easy. That’s why it’s imperative that you have the right tools for the job. While a wet dry vac is great for big spills, every day maintenance of your space requires something a bit more versatile. That’s where a good lightweight stick vacuum can help. Thankfully, Walmart has some of the best deals on stick vacuums, and we found one with a discount that simply cannot be ignored. If you ever wanted to get a great price on a high-quality stick vacuum, today is the day you can make that a reality.The  Prettycare Self-Standing Cordless Stick Vacuum is available at the moment for only $70. That’s 59% off the original price of $170. Finding a stick vacuum of this quality for a shocking $100 doesn’t happen every day. That’s why we recommend you buy one now rather than waiting. Flash deals like this one often disappear without warning, so take advantage while you still can.Prettycare Self-Standing Cordless Stick Vacuum, $70 (was $170) at Walmart

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GPU memory is the most expensive resource in production AI, and it’s also the one running out fastest. Long context windows and multi-turn conversations force AI models to repeatedly recompute information they’ve already processed, consuming GPU memory and compute that could otherwise serve additional users or generate new responses.Instead of treating GPU memory as the limiting resource, why not extend it with much cheaper storage technologies? Weka, for one, believes that cheap flash storage can close that gap. The company’s NeuralMesh 6 software platform, launching alongside its first self-designed hardware line, Wekapod 3, extends what Weka calls Augmented Memory Grid, an approach that aggregates NAND flash to behave like GPU memory at a fraction of the cost.This is an active and increasingly crowded category. Dell, NetApp, Pure Storage and VAST have all repositioned toward AI infrastructure over the past two years and Weka is one of several vendors arguing it’s built for this specific moment rather than adapting to it.”What we’re seeing now with customers is they’re chasing availability of compute, and once they get new allocation from anyone, they want to be able to grab it and start running right away,” Weka co-founder and CEO Liran Zvibel, told VentureBeat.The potential payoff is straightforward: better utilization of existing GPU investments, lower inference costs and faster deployment of new AI workloads without waiting months for additional GPU capacity.The technology is most relevant for organizations already operating AI at scale or expecting rapid growth in usage, particularly enterprises building internal copilots, customer service agents, software engineering assistants or retrieval systems with long context windows. Smaller deployments may see less immediate benefit than organizations where GPU utilization has already become a limiting factor.Inside Weka’s NeuralMesh 6NeuralMesh 6 adds four capabilities aimed directly at a functionality gap Zvibel says has been costing Weka deals in competitive evaluations.Composable and virtual multi-tenancy. Composable clusters give anchor tenants full hardware-level isolation, dedicated CPU, memory, and storage. Virtual multi-tenancy runs through Weka’s RDMA fabric, delivering network-level isolation that scales past 1,000 tenants per cluster, with provisioning in under 30 minutes. Combined, a single cluster running 50 composable clusters can support up to 50,000 tenants. Unified file and object storage. Most storage systems keep two separate paths: a file-based path (the standard way servers and applications read and write files, used heavily in training and fine-tuning pipelines) and an object-based path (S3, the format inference and cloud-native tools typically expect). Normally a gateway translates between the two, meaning the data effectively exists twice. Weka’s claim is that the same physical data on disk is directly readable through either path at once, no translation layer, no second copy. Zvibel is targeting non-AWS GPU clouds specifically, naming Lambda, Nebius, G42, and CoreWeave, with what he described as roughly two orders of magnitude higher performance than conventional S3 and a capacity-based pricing model instead of per-API charges. Metadata-first replication. Destination environments become browsable before a full data copy arrives, with data hydrating only when accessed. “They had to wait for all of that to make it to the other side, and this takes days or weeks, in extreme cases a month,” Zvibel said. “We now allow our customers to grab some allocation of new GPUs and get up and running within an hour.”AlloyFlash and Always-On data reduction. TLC and QLC are two types of NAND flash memory. TLC is faster and more durable but costs more per terabyte, while QLC is cheaper and holds more data per chip but is slower. AlloyFlash mixes both within a single cluster, automatically routing latency-sensitive work to TLC while running bulk-capacity workloads on QLC, cutting cost per terabyte without a performance penalty on the work that needs speed. Data reduction now runs by default rather than as an option.Solving AI’s context problemMulti-tenancy and object storage solve how enterprises and neo clouds operate the platform day to day. A harder problem sits underneath: as context windows and multi-turn interactions grow, so does the GPU compute wasted recalculating work a model has already done. Augmented Memory Grid, a NeuralMesh 6 feature built specifically for this, is Weka’s answer.Every prompt triggers two stages. Prefill calculates attention, the core mechanism behind how large language models process input, and it’s computationally expensive. Decode converts that calculation into output and is comparatively lightweight. The cost shows up hardest in multi-turn sessions like chat or coding, where each new turn re-triggers prefill for everything that came before it, unless that work has been cached.”If you have 10 turns, you may overcalculate 100 times because you’re redoing all of them. If you have 20, you’ll overcalculate 400 times,” Zvibel said. “You can put two orders of magnitude more NAND than you could afford in shared memory, and we can cache 100% of the pre-calculated tokens, so you never need to redo it.”Where Weka sits competitivelyStorage vendors have spent the past year and a half repositioning around AI, and separating genuine capability from repositioned messaging is now a real evaluation problem for buyers. “The storage world is shifting its focus from serving bits to enterprise workloads to managing data at the speed of AI. We’ve seen that most clearly over the past 18 months from Dell, NetApp, and Pure,” Steve McDowell, chief analyst at NAND Research, told VentureBeat. “The interesting thing is that companies like Weka, and VAST, are the true AI-native data companies, solving these problems since day one.”McDowell singled out Augmented Memory Grid as Weka’s clearest technical lead. “Weka continues to have the most technically capable KV cache implementation on the market with its Augmented Memory Grid,” he said. ” They were early with this technology, and continue to innovate. This is critical for AI inference, as it enables a level of GPU efficiency that, without question, saves money on GPUs and memory. That’s key for today’s memory and GPU constrained market.” He also flagged Weka’s contractual guarantee on its data reduction claims as underappreciated. “One flying a little under the radar: Weka is putting its money where its mouth is with its contractual guarantees for its data reduction promises,” he said.McDowell’s advice to buyers evaluating competing claims from Weka, VAST, Pure and NetApp alike was pointed suggesting that enterprise buyers should look hard at what vendors are promising versus what they’re actually delivering.”A smart buyer will look at how competing vendors are solving real-world problems today,” McDowell said. ” They do this by talking to organizations running similar workloads at similar scale. If a vendor can’t point to that, then it should be a warning sign.”

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