TMT Market Talk: M&A, Performance, and the Strategic Shift to AI Operationalization

The TMT sector is undergoing a clear pivot in its merger and acquisition activity. After years of speculative deals, the market is now defined by a strategic consolidation phase. This shift is evident in both the buyer profile and the scale of transactions, even as overall valuation multiples compress modestly.

The valuation backdrop shows a market recalibrating. As of the fourth quarter, the long-term median transaction multiple stood at 14.68x EBITDA. This represents a sequential compression from earlier highs, reflecting a buyer environment that now emphasizes profitability, cash flow conversion, and defensibility over pure growth. Yet, activity levels have remained resilient, with 551 transactions closing last quarter. This suggests that while appetite has cooled, the right strategic targets still command deals.

The most striking feature is the dominance of corporate acquirers. They accounted for 89.54% of all LTM transactions, a concentration that underscores a deliberate, platform-driven strategy. Financial sponsors, by contrast, represented just 7.48% of volume, highlighting their selectivity amid higher interest rates. This corporate-led wave is not about opportunistic speculation; it is about building integrated, AI-enabled ecosystems.

Recent large-scale deals exemplify this strategic intent. In Q4, Omnicom Group completed its $18.2 billion acquisition of The Interpublic Group, a transformative move to consolidate the global advertising industry. Simultaneously, Salesforce closed its $10.1 billion purchase of Informatica, a critical step to bolster its AI and data management platform. These are not random acquisitions but calculated expansions of core capabilities. The trend extends beyond software, with SoftBank acquiring $6.5 billion in AI chipmaker Ampere Computing and ServiceNow buying $2.85 billion in workflow automation firm Moveworks.

Sector Performance and the Rotation Narrative

The market's narrative is shifting. After a relentless three-year rally where technology stocks powered gains, investors are now actively rotating capital away from expensive valuations. This is not a minor correction but a structural rotation that challenges the very foundation of the bull market's recent dominance.

The scale of the tech-led rally is undeniable. In 2025, the S&P 500 delivered an 18% total return, its third consecutive year of double-digit gains. Yet that strength was concentrated. Only three sectors-technology, communications, and industrials-managed to outperform the broader index. The technology sector led the pack with a 24.6% return, driven by AI adoption across chipmakers, hyperscalers, and software. This created a powerful feedback loop where a handful of mega-cap names, like NVIDIA and Broadcom, became the primary engine for the entire market's performance.

That dynamic is now under pressure. Since the end of October, a clear rotation has taken hold. Industrial, healthcare, and small-cap shares have outperformed the S&P 500, while the tech sector itself has declined. This shift is a direct response to concerns over valuation and the sustainability of the AI theme. As one strategist noted, investors are becoming "wary of expensive tech valuations amid uncertainty over the AI theme" that propelled the rally. The equal-weight S&P 500, which tracks the average stock rather than the largest, has gained over 5% since late October, significantly outpacing the standard index's 1% rise. This divergence signals a market seeking broader leadership.

The challenge to tech's dominance is twofold. First, its nearly 53% market cap weight in the S&P 500 means any sector-wide rotation has a magnified impact on the index. Second, the rotation is fueled by a tangible shift in investment thesis. The initial euphoria over AI's potential is giving way to a more pragmatic assessment of its near-term financial returns. This creates a vulnerability: the market's performance has become too dependent on a narrow set of narratives and stocks.

The bottom line is a market in transition. The rotation away from tech is not a rejection of AI's long-term promise, but a healthy recalibration. It reflects a search for value and a desire to see earnings growth broaden beyond the megacaps. For the rally to sustain itself, this broadening must be more than a seasonal trade. It requires the AI benefits that strategists expect to "filter through" to a wider array of sectors, allowing the market's leadership to become more resilient.

The Strategic Shift: From AI Hype to Operationalization

The narrative around artificial intelligence is undergoing a fundamental reset. The era of headline-grabbing model releases is yielding to a more pragmatic, infrastructure-driven phase. Enterprises are moving decisively from AI experimentation toward operational deployment, a shift that is now the primary engine for corporate strategy, M&A, and sector leadership.

This operational pivot is creating a clear demand for specific capabilities. Companies are no longer chasing novelty; they are investing in the tools to make AI work at scale. This includes robust data infrastructure buildout, workflow automation, and agentic AI systems that can execute complex tasks. The M&A activity of recent quarters reflects this. Deals like Salesforce's purchase of Informatica and ServiceNow's acquisition of Moveworks are not about flashy new AI models. They are about securing the data management and automation platforms that are essential for integrating AI into core business operations. The strategic acquirer is now focused on building integrated, AI-enabled ecosystems, not just acquiring AI startups for their IP.

The 2026 narrative, as predicted by Deloitte, is one of "quieter and smarter" progress. Gains will come from the often-unglamorous work of integration, scaling, and governance, not from new foundational models. This more practical focus is critical because it determines whether AI's promise translates into tangible business value. The technology, media, and telecom sectors are no longer just providers of chips and code; they are becoming the essential infrastructure for cross-industry transformation. For AI to "eat the world," its operational foundations must be solid.

This operationalization, however, comes with a massive and growing cost: energy. AI operations are an absolute glutton for electrons. As one analysis notes, by 2028, AI alone could consume as much electricity annually as 22% of all U.S. households. This creates a critical infrastructure bottleneck. Meeting this demand sustainably is driving a new wave of investment in power sources. Companies like Microsoft are partnering with nuclear energy producers to secure massive, reliable baseload power for their data centers. The message is clear: the next frontier in AI infrastructure may not be silicon, but the grid. The strategic shift is complete. The focus has moved from what AI can do to how it can be deployed at scale, and that deployment is now the central challenge for the entire economy.

Forward Themes and Key Catalysts

The investment landscape for 2026 is defined by a critical transition. The market is moving from a phase of speculative AI spending to one of operational validation. This shift sets the stage for a new set of themes and catalysts that will determine which companies succeed and which falter.

The first major theme is a deceleration in the growth of AI capital expenditure, even as absolute spending remains sky-high. Analysts now consensus on 2026 capital spending of $527 billion for hyperscalers, up from $465 billion earlier in the year. Yet Goldman Sachs Research expects the growth rate of this spending to slow. This is a pivotal inflection. It signals that the initial, breakneck build-out of AI infrastructure is maturing. The focus will now shift from simply adding capacity to proving that each dollar of capex generates a return. For investors, this means the easy money from pure infrastructure bets may be fading.

This leads directly to the second, more important theme: the search for productivity gains and margin expansion. As investors rotate away from AI infrastructure companies where earnings growth is pressured, the focus will narrow to two groups. First are the AI platform operators that can demonstrate a clear link between their massive spending and top-line revenue. Second are the "productivity beneficiaries" across other sectors that are using AI to cut costs and boost efficiency. The market will reward companies that show AI is moving from a cost center to a profit center. The divergence in stock performance among AI hyperscalers-where average price correlation has collapsed from 80% to just 20%-already reflects this selective scrutiny. Only those showing tangible financial returns will be rewarded.

The key risk to the entire consolidation and operationalization thesis is that the gap between AI promise and operational reality narrows too slowly. Deloitte's 2026 prediction of a "quieter and smarter" progress captures this. The work of scaling AI-data integration, workflow automation, governance-is often unglamorous and time-consuming. If this foundational work drags on, it will sustain valuation pressure on the sector. High stock valuations and extreme market concentration leave little room for disappointment. The current setup, with the S&P 500 trading at a forward P/E of 22x, means any stumble in earnings growth could trigger a meaningful correction. The operationalization phase is the ultimate stress test for the AI narrative.

The bottom line is one of selective optimism. The forward view expects continued bull market gains, driven by earnings growth and a solid economy. But the path will be narrower. The catalysts to watch are not new AI announcements, but quarterly reports that show AI capex slowing while margins improve, and earnings that demonstrate the promised productivity boost is finally materializing. The market is no longer buying hype; it is demanding proof.