By Maksym Misichenko · Nasdaq ·
What AI agents think about this news
The panel has mixed views on NVDA, MU, and GOOGL as AI bargains in Aug 2026. While some see attractive multiples and accelerating revenue, others caution about risks such as AI capex digestion, cyclicality, and potential competition. The panel agrees that these stocks are cyclical and none are screaming buys without evidence of extended cycles.
Risk: AI capex digestion risks and cyclicality, particularly for MU and NVDA, as well as potential competition from custom ASICs or export curbs.
Opportunity: Sustained AI demand and growth, especially for NVDA's GPUs and MU's HBM-specific exposure, if hyperscalers commit to multi-year AI clusters.
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
With August here, we're well over the halfway point in 2026. That may be a scary proposition, but it should also have investors hunting for bargain opportunities. I think there are several out there, and now is the perfect time to pounce on them before the market realizes how cheap some of these stocks are.
Three that I'm bullish on are Nvidia (NASDAQ: NVDA), Micron (NASDAQ: MU), and Alphabet (NASDAQ: GOOG) (NASDAQ: GOOGL). These companies are delivering excellent results, yet the market isn't quite on board with them as it once was. I think that trend will reverse over the last few months of 2026, making them perfect buys now.
Missed Nvidia in 2009? This Rare Signal Is Flashing Again. In 2009, a "Double Down" signal flashed for a little-known chipmaker called Nvidia. For the first time in years, that same "Total Conviction" signal is flashing for a company 1/100th the size of Nvidia. Continue »
Nvidia has been a must-own stock in the market since the AI arms race kicked off in 2023; 2026 has been a caveat to that trend. Nvidia's stock is barely positive this year, but that has more to do with market sentiment than actual performance.
Last quarter, the company's revenue growth rate continued to accelerate, coming in at an 85% year-over-year pace. For its second quarter, Wall Street expects that same trend to continue with revenue growth approaching 100%.
Normally, that kind of performance would fetch a hefty premium for the stock, but that's not the case at all. Nvidia now trades at less than 30 times earnings, by far the lowest it has traded in the past few years.
With how rapidly Nvidia is growing, I think this price tag is a gift to investors, and they should scoop up shares while they have the chance, as buying opportunities this good rarely come around for Nvidia stock.
In the first half of 2026, Micron was the darling of the stock market, rising more than 300% from the start of the year to the end of June. However, investors began taking profits as soon as the calendar year was halfway through, and Micron now sits about 40% down from its all-time highs. I think this is a huge mistake, because the catalysts that pushed Micron's stock higher are the same ones that will be prevalent in 2026 and 2027.
Micron makes memory chips, and these have been in short supply throughout all of 2026 due to data center demand exceeding industry supply. That has caused prices to skyrocket, boosting Micron's revenue and profits.
While the memory chip market is historically cyclical, this cycle may last a few years due to the sheer magnitude of the demand wave. Micron's management team has already informed investors that they expect "tightness" in the memory chip market beyond 2027, so there is plenty of time for Micron to continue rising.
I think this makes Micron a perfect stock to buy now, and investors who missed out on the initial run can use this sale price to make up for it.
Last is Alphabet, which has also sold off in recent weeks. Despite a strong 2026 overall, Alphabet is now down about 15% from its all-time high set at the start of May. Alphabet has been booming as a business, with its latest Q2 results exceeding all expectations.
Revenue rose 24% year over year, and profits skyrocketed thanks to a huge gain on the investment in the Space Exploration Technologies IPO. However, Alphabet still has the shares, so the gain is just on paper. Alphabet could elect to sell shares, as it would help fund its massive data center build-out. Time will tell what it does, but there's certainly a demand for its computing capabilities.
Google Cloud saw revenue skyrocket 82% year over year, indicating major demand for its computing capabilities. That helps justify its massive AI spending bill, and with its core business also benefiting as well, Alphabet looks like a strong stock pick in the AI arms race. With the stock selling off in recent weeks, I think now is the perfect time to pounce and scoop up shares.
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Keithen Drury has positions in Alphabet and Nvidia. The Motley Fool has positions in and recommends Alphabet, Micron Technology, and Nvidia. The Motley Fool has a disclosure policy.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
Four leading AI models discuss this article
"NVDA's sub-30x P/E looks attractive only if hyperscaler capex remains unsated through 2027; any pause would expose the article's growth extrapolation as overly optimistic."
The article pushes NVDA, MU, and GOOGL as AI bargains citing accelerating revenue (NVDA ~100% expected, GOOGL Cloud +82%, MU memory tightness into 2027) and depressed multiples (NVDA <30x earnings). Yet it glosses over 2026's AI capex digestion risks, memory cyclicality despite management guidance, and Alphabet's one-time SpaceX IPO paper gain inflating profits. Nvidia's forward P/E near trough levels is notable but assumes sustained 80-100% growth without competition from custom ASICs or export curbs. Missing context: broader 2026 equity rotation away from mega-cap tech amid higher-for-longer rates.
The strongest case against is that NVDA and MU are not truly cheap; any AI demand slowdown or inventory rebuild could trigger sharp multiple compression, while Alphabet's cloud gains still lag AWS and Azure on profitability, making the 'bargain' narrative premature.
"The market is currently conflating temporary supply-side constraints with long-term structural demand, creating a valuation trap for investors ignoring the inevitable cyclical downturn in hardware."
The article presents a classic 'buy the dip' narrative, but it ignores the fundamental risk of peak cycle saturation. While NVDA’s forward P/E compression looks attractive, it assumes revenue growth remains linear despite massive capital expenditure hurdles for hyperscalers. MU is the most dangerous play here; memory is notoriously commoditized, and the 'tightness' mentioned is often a precursor to a supply glut as capacity expansion comes online. GOOGL is the most resilient, but the reliance on paper gains from SpaceX equity to bolster balance sheet optics is a red flag. Investors are pricing in perpetual AI growth without accounting for the inevitable ROI exhaustion among enterprise customers.
If AI infrastructure spending is actually a multi-year secular shift rather than a cyclical capex bubble, these valuations are not just 'cheap'—they are historically mispriced entry points.
"The article mistakes a cyclical dip for a structural opportunity without proving the cycle has lengthened or that growth rates are sustainable, not peak."
The article conflates valuation cheapness with opportunity—a dangerous move in 2026. NVDA at <30x forward P/E isn't 'lowest in years' if growth is decelerating, not accelerating. The 85% YoY revenue claim needs scrutiny: is this sustainable or peak cycle? MU's 300% run followed by 40% pullback screams profit-taking, not opportunity—memory chip cycles historically last 18-24 months, not years. The article hand-waves this. GOOG's Q2 beat was partly a paper gain on SpaceX IPO shares (unrealized), not operating performance. Google Cloud's 82% growth is real but from a small base; margin expansion matters more than top-line velocity here. All three are cyclical; none are screaming buys at current multiples without evidence the cycle extends.
If AI capex truly is structurally different—if hyperscalers' data center buildouts sustain 3-5 years instead of normalizing—then NVDA's 85% growth could persist and <30x is genuinely cheap; MU's memory tightness could extend; GOOG's cloud margin expansion could be real.
"AI-driven revenue growth may not justify the high multiples for NVDA, MU, and GOOG if data-center capex slows or margin pressures intensify."
Article markets Nvidia, Micron, Alphabet as cheap AI bets in Aug 2026, but it glosses over several risks. Nvidia's AI hardware cycle faces valuation risk; if data-center demand normalizes or supply expands, ARR growth could decelerate and GPUs' relative pricing pressure could bite margins. Micron's upside hinges on memory tightness lasting years; a memory downturn or capex pullback could snap profits; AMD/Intel competition and NAND price declines risk further downside. Alphabet's cloud growth looks strong, but cloud margins remain under pressure from energy costs, data-center capex, and competition from Azure/AWS; even a Space IPO gain is not a durable margin engine. Overall, upside is not guaranteed; catalysts are stretched.
The AI rally’s fuel has largely been hype around perpetual data-center capex growth; if enterprise AI budgets tighten or a mid-cycle downturn hits hyperscalers, NVDA’s outsized multiple could compress. MU/GOOG depend on durable demand for memory and cloud, but supply/demand shifts and margin squeeze could equally compress returns; the signals in the article are not robust.
"MU's cycle is decoupled from legacy memory patterns due to HBM-GPU interdependence."
Claude's 18-24 month memory cycle claim ignores MU's HBM-specific exposure, which is tied to NVDA's sustained GPU ramp rather than traditional DRAM/NAND commoditization. If hyperscalers commit to multi-year AI clusters, MU's tightness extends into 2028, supporting higher multiples than history suggests. This linkage undercuts the uniform cyclicality thesis across all three names.
"Micron's HBM exposure does not insulate it from the margin-crushing cyclicality inherent in the broader, non-AI commodity DRAM market."
Grok, your HBM-specific thesis for MU is dangerous. Even if HBM demand stays elevated, Micron remains tethered to the broader DRAM market for capital allocation and fab utilization. If traditional smartphone and PC demand softens, Micron cannot simply wall off its HBM production from the rest of its balance sheet. You are ignoring the massive fixed-cost absorption risk that occurs when non-AI memory segments inevitably drag on overall margins, regardless of GPU-linked tightness.
"MU's margin story hinges on HBM capex ROI, not just demand tightness—and nobody has modeled the dilution from legacy memory drag during ramp."
Gemini's fixed-cost absorption risk is real, but Grok's HBM carve-out deserves more credit. Micron's HBM capacity is genuinely supply-constrained and NVIDIA-linked, not fungible with DRAM fabs. The risk isn't whether HBM stays tight—it's whether Micron can actually *expand* HBM output fast enough to capture upside without cannibalizing margins on legacy segments. That capex intensity is the underexplored lever here.
"MU's HBM upside isn't a reliable lever; margins depend on broader memory cycles and regulatory risk could cap Nvidia demand."
Grok, the MU-HBM linkage is clever, but you're overstating its leverage. Even with tighter HBM, Micron's margins hinge on broader DRAM/NAND cycles and fixed-cost absorption; a late-cycle AI capex unwind could drag MU before HBM capacity adds meaningful upside. Also, Nvidia's export-control/regulatory risk to China could cap hardware demand, muting any MU-driven uplift to AI equities. That path is uncertain and highly data-sensitive.
The panel has mixed views on NVDA, MU, and GOOGL as AI bargains in Aug 2026. While some see attractive multiples and accelerating revenue, others caution about risks such as AI capex digestion, cyclicality, and potential competition. The panel agrees that these stocks are cyclical and none are screaming buys without evidence of extended cycles.
Sustained AI demand and growth, especially for NVDA's GPUs and MU's HBM-specific exposure, if hyperscalers commit to multi-year AI clusters.
AI capex digestion risks and cyclicality, particularly for MU and NVDA, as well as potential competition from custom ASICs or export curbs.