Is AI actually taking entry-level jobs?

AI agents are not replacing SaaS — but they are restructuring it, compressing some categories, and expanding others. The "SaaSpocalypse" narrative that dominated markets in early 2026 proved premature: the software sector lost over $1 trillion in market capitalization between January and April 2026, then partially recovered by Q3. The real shift is architectural and commercial, not existential. AI agents are genuinely disrupting customer support, sales development, and certain point-solution categories. They are not meaningfully disrupting ERP, core financial systems, cybersecurity, or complex multi-jurisdiction compliance software. Every major SaaS incumbent — Salesforce, ServiceNow, Microsoft, SAP, Workday, Oracle — has responded by embedding agents directly into their platforms rather than ceding ground. The most consequential change is the pricing model: per-seat subscription is being replaced by hybrid and outcome-based models, which fundamentally alters SaaS revenue economics without necessarily destroying them. Agent-native startups (Sierra, Decagon, Harvey) are growing fast but primarily expanding the market into the services budget rather than cannibalizing existing SaaS spend. The decision-maker's frame: not "replace or survive" but "which categories, on what timeline, at what pricing, with what governance."

Training a frontier AI model in 2026 is one of the most capital-intensive activities in the private sector. The total cost of the most expensive single training runs is now estimated in the hundreds of millions of dollars, with credible projections of billion-dollar runs by 2027. But the headline "training cost" figure that appears in press releases—DeepSeek's famous $5.6M, for instance—typically covers only the final pre-training compute run, excluding hardware acquisition, preliminary experiments, failed runs, data curation, human annotation, and R&D staff. When all costs are included, total development expenditure is typically 3–10× the final-run compute figure.

Texas has become the fastest-growing data center market in the United States and is on track to dethrone Northern Virginia as the global leader by 2030. JLL counted 6.5 GW of Texas data center capacity under construction at year-end 2025—the most of any jurisdiction in North America—and Modo Energy's October 2026 tracker puts the figure at roughly 12 GW, with a pipeline of 140+ planned projects representing an additional 75,000 MW if built. The forces driving this concentration are mutually reinforcing: cheap and abundant power from an independent grid (ERCOT), vast and inexpensive land, an aggressive state and local tax incentive regime, a deregulated electricity market that allows faster interconnection than most U.S. grids, and the absence of many regulatory barriers that constrain development elsewhere.