February 2026 has produced a notably uneven tape for technology: broad market strength has been punctuated by repeated “safe haven” rotations into energy and staples, while higher-beta AI-linked equities have absorbed sharp drawdowns. In that context, 20–30% pullbacks can look less like a verdict on AI demand and more like a repricing of duration, volatility, and expectations.
For disciplined investors, this kind of “white noise” is often where entry points appear—particularly when underlying company fundamentals (revenue, earnings, and forward guidance) are improving quarter over quarter even as the stock price retraces.
Key takeaways
- Market leadership has broadened unevenly: defensive rotations can coexist with steep pullbacks in high-beta AI names.
- AI adoption has shifted from experimentation to operational efficiency (software development, data-center management, and healthcare workflows), supporting demand for hardware and specialized software.
- In the current dataset, top AI quant picks show materially higher projected growth than both the “Magnificent Seven” and the broader S&P 500.
- Short-term sentiment can obscure signal; revision trends and the “staircase” pattern in revenue/EPS are often more informative than headline-driven price action.
A tale of two markets: rallies and rotations
Recent price action has been characterized by an on/off risk regime. On “risk-on” days, broad indices can rally; conversely, those rallies have frequently been interrupted by capital rotating into perceived defensives such as energy and consumer staples. This push-pull dynamic matters because it compresses holding periods and increases the odds that high-volatility segments—especially AI-linked growth equities—overshoot to the downside.
High-beta AI stocks falling 20–30% in a month is attention-grabbing, but it is not automatically diagnostic of weakening adoption. In many cases, these moves reflect a combination of valuation reset, risk-parity deleveraging, and crowded positioning unwinds. The practical implication is straightforward: when price declines are driven more by macro/positioning than by deteriorating fundamentals, fundamentals-based screening becomes more useful, not less.
From experimental to “broad” AI: where demand is showing up
AI deployment has increasingly moved beyond pilots and proofs of concept. The dominant use case in 2026 is operational efficiency: writing and maintaining code, managing and optimizing data centers, and improving clinical and diagnostic workflows in healthcare. Moreover, these workflows are not “AI-only” projects; they are blended into existing software stacks, infrastructure procurement, and enterprise budgeting cycles.
In this regard, demand can be expressed through two complementary channels:
- Hardware and infrastructure: compute, connectivity, and manufacturing capacity needed to deploy and run AI at scale.
- Specialized software: tooling that makes AI systems usable, measurable, and economically productive in real business processes.
Within the current comparison set, “top AI picks” show a projected revenue growth rate of 38%, versus 6% for the broader S&P 500. That spread is large enough that it can dominate the investment outcome if it persists, even after accounting for volatility and valuation compression.
Growth comparison: AI picks vs. mega-cap tech vs. the index
| Group | Revenue growth | EPS growth |
|---|---|---|
| Top AI quant picks | 38% | 99% |
| Magnificent Seven | 17% | 20% |
| S&P 500 | 6% | 10.6% |
The key point is not that mega-cap tech is “bad” or that the index is “irrelevant.” Rather, it is that the growth differential can justify looking beyond the largest names—particularly when price drawdowns have improved prospective entry points for smaller or mid-cap companies tied to AI infrastructure and applied AI.
Five AI-linked names to watch during the 2026 dip (Group B)
The following tickers are presented as a focused watchlist rather than a blanket recommendation. They span software, infrastructure manufacturing, semiconductors/connectivity, and healthcare equipment—areas where AI spending tends to show up as measurable demand for products and services.
Hut 8 Corp (HUT)
Category: application software. One notable datapoint in the current profile is the improvement in profitability, moving from D- to A+. If that trajectory is durable, it can change how the market values the business (profitability and cash flow tend to matter more when volatility is elevated).
Celestica (CLS)
Category: electronic manufacturing. With a stated 43% long-term growth rate and a role in AI infrastructure, this name can be viewed as an “enabler” rather than a pure software narrative. Manufacturing and integration capacity are frequently bottlenecks when adoption accelerates.
Credo Technology (CRDO)
Category: connectivity microchips. The stock has experienced a 28% monthly pullback alongside reported 105% revenue growth. This is a good illustration of the current regime: strong growth metrics do not immunize a name from a valuation reset. Conversely, a sharp pullback can improve forward returns if the growth profile persists.
Revision activity can also matter here: the current snapshot notes 12 upward revisions in 90 days and 0 downward. While revisions are not a guarantee, they often reflect improving expectations that may not yet be fully reflected in the price.
AppLovin (APP)
Profile: A+ growth and profitability. In a market that intermittently rewards defensiveness, the combination of growth and profitability can be a differentiator. The analytical task is to confirm that profitability is not a one-off (e.g., driven by temporary margin factors) and that growth is not overly dependent on a single channel or customer concentration.
Globus Medical (GMED)
Theme: AI applied to medical equipment and surgical optimization. The profile includes 68% EBIT growth. Healthcare is often a slower adoption domain; however, when AI is embedded in workflow and instrumentation, progress can be incremental and measurable rather than purely conceptual.
Filtering signal from noise: sentiment, insider selling, and revisions
When volatility is high, narrative tends to expand. It is tempting to anchor on daily commentary, viral takes, and short-term price moves. Conversely, a more stable approach is to prioritize what changes slowly and compounds: revenue, earnings power, and the forward expectation set.
Insider selling is a common source of headline anxiety, but it is not always a clean signal of negative conviction; compensation structure and diversification can drive sales even when the underlying business is healthy. Meanwhile, analyst estimate revisions—especially when they trend consistently in one direction—can serve as a practical summary of how the expectation set is moving.
A useful mental model is the “staircase” pattern: when revenue and EPS increase quarter over quarter, the fundamentals are climbing even if price is temporarily falling. In that setup, a dip can be a gift rather than a warning—provided the next few quarters continue to validate the trend.
Caveats and limits
- High growth does not eliminate risk. 20–30% drawdowns can repeat, and liquidity can vanish quickly in risk-off rotations.
- Revisions can reverse. Upward estimate changes are helpful context, but they can lag real-time business conditions and can shift rapidly after guidance updates.
- Mix matters. Revenue growth and EPS growth are summary metrics; margin sustainability, customer concentration, and capex intensity can materially change the investment profile.
- Watch valuation and duration. Even strong businesses can underperform if multiples compress due to rates, risk premia, or changing market regimes.
Practical checklist for the 2026 pullback
For investors considering AI exposure beyond the largest index constituents, a disciplined process can reduce the odds of confusing volatility with deterioration:
- Confirm that revenue and EPS are improving quarter over quarter (the “staircase,” not a single spike).
- Track estimate revisions around earnings and guidance updates; treat dispersion as a risk indicator.
- Separate macro-driven drawdowns from company-specific breaks (product demand, competitive losses, or margin impairment).
- Size positions for volatility; assume that pullbacks can deepen before fundamentals reassert themselves.