Overview: A New Phase Takes Shape
The AI rally’s next chapter is arriving sooner than many traders expected, according to investor chris camillo predicts. He argues that the third wave of the AI cycle—focused on turning automation into real, measurable savings—could deliver the last clear, fast-moving set of profits for the market. In a period of mixed macro signals, he says the setup is unusually clean for companies that run large, labor-intensive operations.
As of late May 2026, market chatter is centering on whether AI-driven productivity can finally translate into broader earnings upside rather than just hype. Camillo’s thesis hinges on a simple premise: when software starts cutting costs in big, repetitive back-office tasks, the gains don’t vanish after a few quarters. They compound as deployments scale and margins expand. He points to a subset of firms with heavy admin, customer-service, and logistics footprints as the likely early beneficiaries.
Wave Theory And The Last Easy Trade
Camillo’s framework splits the AI cycle into three waves, a model he has used to explain why the market’s biggest winners may shift as technology matures. In his view, Wave 1 sparked the consumer-facing AI boom, when people first saw AI write, reason, and chat with surprising fluency. Wave 2 built the infrastructure, with hyperscalers expanding capacity and hardware demand surging. Wave 3, now taking hold, centers on productivity—companies using AI to trim costs and rewrite margin dynamics.
“If you’re hunting for the last easy trade, look at the places where AI efficiency unlocks genuine operating leverage,” Camillo said on a recent episode of a market podcast. He cautions that the easy gains won’t come from novelty alone; they require scalable AI programs that replace high-cost tasks with automated systems and smarter workflows.
Camillo emphasizes that the next 12–24 months could reveal a clearer line between “AI as hype” and “AI as actual profit.” He notes that investors have already priced in a lot of hype around AI’s potential; the remaining delta will come from companies that demonstrate durable cost reductions rather than one-off improvements.
What Investors Should Watch
- Winners are likely to be firms with large, cost-heavy workforces—especially in customer support, administration, and logistics—where AI-driven automation can yield meaningful margin expansion.
- Look for companies that have launched scalable AI programs with measurable savings, not just pilot projects or pilot ROIs.
- Watch the cadence of earnings: the first signs of margin uplift with credible cost savings should appear within the next three reporting cycles.
- Valuation discipline remains essential. The last easy trade tends to fade if price multiple expansion outruns fundamentals.
In the marketplace, Camillo argues the best opportunities will sit at the intersection of measurable cost relief and scalable AI deployment. He suggests investors tilt toward businesses that already have large administrative or service-heavy legacies and can push through efficiency gains without sacrificing service quality.
Market Reactions And Data Points
The AI trade has shifted in 2026 as rate expectations, inflation readings, and corporate earnings narratives color the picture. A segment of AI-focused equities has cooled from last year’s rapid ascent, while broadly diversified tech indices have wavered on a mix of macro data and company-specific updates. In this context, Camillo’s thesis seeks to identify a concrete, near-term driver of earnings growth—namely, productivity gains that translate into front-line margin expansion.
Key indicators shaping the debate include:
- Operating margins in cost-heavy industries could rise by an estimated 150–250 basis points if AI-enabled automation scales across back-office functions and field service networks.
- Annual savings from AI-enabled processes in logistics and customer support are projected to run in the low to mid-single digits as a percentage of revenue for large adopters, according to industry cadence studies.
- The timing window for visible margin improvement is typically 12–24 months after deployment reaches scale, assuming continued AI integration and talent efficiency.
- Investors should monitor capex cycles tied to AI hardware refreshes, software platforms, and data-management improvements, as these inputs influence the pace of productivity gains.
Camillo notes that the market’s patience will be tested by volatility in short-term results, but the payoff could materialize as companies cross milestones on automation adoption. “The last easy trade isn’t about a single quarter’s number; it’s about a sustained arc of cost relief that compounds,” he said.
Stocks To Watch In The Wave 3 Era
Analysts and portfolio managers are scanning for names that meet the Wave 3 criteria: strong balance sheets, scalable AI programs, and significant exposure to labor-intensive processes. The list is still evolving, but several characteristics are already clear:
- Companies with large customer-service footprints and automated support channels stand out as potential early runners.
- Logistics and distribution firms that can consolidate operations through AI-enabled routing and inventory management may show margin uplift before peers.
- Industrials with governance and compliance workflows that benefit from automated data entry and reporting could deliver incremental profits alongside top-line strength.
Camillo cautions that this isn’t a blanket win for all AI stocks. The selective approach matters: firms must prove scale, reliability, and disciplined capital allocation to sustain earnings trajectories.
Risks, Skepticism, And The Road Ahead
As with any major market theme, there are caveats. The AI productivity story can stall if talent shortages persist, if regulatory frictions slow deployment, or if AI investments fail to translate into real-World customer outcomes. Some skeptics argue that cost-cutting gains may face diminishing returns as companies reach a plateau in automation adoption or encounter service-quality tradeoffs.
Camillo acknowledges these risks but believes the balance favors the productivity cycle’s third wave for investors who stick to evidence-based deployments. He notes that the market’s current hurdle—valuations that already imply aggressive growth assumptions—requires a disciplined, data-driven approach to avoid chasing hype.
Bottom Line: A Timely Call For The Last Easy Trade
For readers wondering how to position in a market that keeps bending toward AI headlines, the core takeaway from investor chris camillo predicts is straightforward: the next phase of AI’s earnings story will hinge on concrete, scalable cost savings that lift margins in labor-heavy sectors. If proved, the Wave 3 efficiency gains could offer a clearer, faster path to outsized returns than the prior waves—provided investors remain selective and patient.
As markets navigate a volatile spring and early summer of 2026, Camillo’s framework adds a practical lens: watch for companies turning AI into real profits, not just promises. For traders and money managers tracking the AI cycle, the “last easy trade” label signals a shift from speculative bets to disciplined bets on productivity and margin leverage.
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