Thoughts after the initial wave of major TMT earnings (hyperscalers): Bottom Line: This earnings season fundamentally changed the AI investment debate. For the past 18 months, investors questioned whether AI demand was real, whether hyperscalers were overspending, and whether unprecedented AI CapEx would permanently impair free cash flow. I think those questions are now largely behind us. Azure accelerated to +43% (guiding ~45%), Google Cloud accelerated to +82% (with TPU system sales beginning to be recognized as revenue), AWS posted its fastest growth in 18 quarters (+37%) while backlog reached $496B, and Oracle heads into earnings with expectations for another quarter of 90%+ OCI growth (I wouldn't be surprised if OCI approached ~110%). When every major cloud provider is accelerating simultaneously, this is no longer primarily a market-share story, it's a market expansion story driven by AI. That's a rare dynamic in technology. Normally, one platform's acceleration comes at another's expense. This time, everyone is accelerating because AI is expanding the size of the market itself. What stood out most wasn't the growth rate, it was how confidently management teams defended AI CapEx. Andy Jassy gave one of the clearest explanations of AI infrastructure economics I've heard on an earnings call. Servers begin generating revenue almost immediately after deployment and typically pay back in less than three years, while data centers have 30+ year useful lives, supporting multiple generations of hardware. AWS isn't buying servers speculatively, it has strong visibility into customer demand before triggering most of that spend. Even after raising 2026 CapEx to ~$220B, AWS says it remains capacity constrained and expects demand to exceed supply into 2027. Alphabet raised its own 2026 CapEx guide to $195-205B mid-year for exactly the same reason. The comment that caught my attention most, however, was Jassy's long-term framing. AWS has historically been viewed internally as a business capable of generating several hundred billion dollars of annual revenue. Today, Amazon believes AWS can ultimately become at least twice that size, and potentially a $1T annual revenue business. That's an extraordinary statement from one of the most disciplined capital allocators in technology. To me, it suggests AI hasn't simply accelerated cloud growth, it has fundamentally expanded the long-term TAM for cloud infrastructure. The biggest read-through from this earnings season is that the market is moving from debating whether AI CapEx destroys free cash flow to how much incremental free cash flow it can create over the next decade. We're also moving beyond asking whether AI demand is real and toward the much more important question: who captures the economics? Read-throughs to watch: Oracle ( $ORCL): Oracle is second-order beneficiary of everything discussed above. Every hyperscaler is telling the market demand still exceeds available capacity, while Oracle enters earnings after posting 93% OCI growth last quarter. If Oracle delivers another quarter of 90%+ OCI growth, or even approaches triple digits, it would further reinforce that OCI has become one of the preferred platforms for frontier AI workloads rather than simply another enterprise cloud provider. Oracle is increasingly participating in the AI infrastructure build-out, not merely enterprise cloud migration. AI Compute & Semis ( $NVDA $AMD): Three hyperscalers simultaneously increasing CapEx, AWS to $220B, Alphabet to $195-205B, while Microsoft continues signaling elevated AI infrastructure investment and demand that still exceeds supply, is a powerful demand signal for GPUs, networking and custom silicon. Google is commercializing TPUs. AWS disclosed its Trainium business has reached a $25B annualized run rate, growing triple digits. Custom silicon is accelerating alongside Nvidia, not replacing it. One point I think investors underappreciated: Jassy explicitly said post-training, reinforcement learning and agentic workloads are predominantly CPU-intensive. As AI shifts from training toward inference and autonomous agents, the CPU layer scales alongside the GPU layer, not instead of it. That's an important long-term read-through for AMD's EPYC franchise. Foundry & Semi Cap ( $TSM $AMAT $LRCX $KLAC): Every Trainium chip, TPU and custom ASIC ultimately runs through advanced manufacturing and packaging. TSMC remains the critical bottleneck for custom silicon, while rising hyperscaler CapEx reinforces demand for advanced packaging, HBM and leading-edge wafer capacity. The picks-and-shovels layer of the AI ecosystem continues to benefit regardless of which chip architecture ultimately wins. Cybersecurity ( $PANW $CRWD $FTNT): One read-through I don't think the market fully appreciates is cybersecurity, one of the few software cohorts I've remained constructive on this year. Andy Jassy noted that security is one of the first topics discussed in virtually every enterprise AI deployment, and AWS launched Continuum, an AI-native vulnerability discovery and remediation platform, in response. I don't see this as a negative for cybersecurity vendors, I see it as validation that AI is dramatically expanding the enterprise attack surface. Every AI agent, API, model endpoint, vector database and autonomous workflow creates another asset that must be secured, monitored and governed. AI infrastructure and cybersecurity are becoming two sides of the same investment theme. That continues to favor integrated platform vendors like Palo Alto Networks, CrowdStrike and Fortinet, which are increasingly securing AI environments rather than just traditional IT infrastructure. Enterprise AI Platforms ( $SNOW $MDB $DDOG $PLTR): Although, as a whole, I remain cautious on software, I'm still positive on this group. Hyperscalers aren't just building AI infrastructure, they're confirming that AI at scale requires a mature data, orchestration and observability layer underneath it. Every inference workload requires data, storage, vector databases, governance and observability. As AI moves from pilots into production, these layers become mission-critical infrastructure rather than discretionary software spend. The winners won't simply help enterprises build AI, they'll help them operationalize it. The inference build-out is still in its early innings. AWS noted that most enterprise production workloads still aren't using inference at scale, describing a large "middle of the barbell" between frontier AI labs consuming massive compute and enterprises broadly deploying AI into production. When that middle begins to move, and I believe it will, it represents the largest incremental source of AI infrastructure demand we've seen yet. Training builds the models. Inference commercializes them. I continue to believe we're still much earlier in the AI cycle than the market appreciates. The technology debate is largely over. The investment debate is just beginning. The biggest winners over the next decade won't necessarily be the companies building the best models, they'll be the companies that build the most durable platforms, infrastructure and business models around AI. #AIInfrastructure #CloudComputing #Earnings #Cybersecurity