The top five tech trends to watch in 2026

Lori Mac VittieDistinguished Engineer and Chief Evangelist

It’s that time of year again, when everyone makes predictions about what will (or won’t) happen in 2026.

I don’t do predictions. I do, however, prognosticate. And if your superpower is also pedantry, you already know that means I derive implications from data and trendlines, not tea leaves. Prognostication isn’t prophecy; it’s pattern recognition with receipts. I don’t guess what’s coming; I extrapolate from what’s already moving and measure how fast it’s gaining momentum.

In other words: I’m not predicting the future. I’m just paying attention to the parts of the present everyone else is ignoring.

So, with that in mind, I am happy to share what I believe are the top five tech trends to watch in 2026. You ready? We’re doing it anyway. Let’s go.

1. Inference 

becomes the dominant cost center, overtaking training

We’ll see a major shift: enterprises will spend more on inference infrastructure (serving, scaling, latency) than on training. Training is episodic; inference is 24/7. I’ve already flagged this trend, and the market is seeing indicators of the same. Our research told us 80% of organizations were already operating their own inferencing services. That moves it firmly into the list of “first class workloads” and indicates significant costs shifting already.

1. Dell publicly reports that its AI/server business is exploding. For example, the company claims that AI server sales grew roughly sixfold from FY2024 to FY2025, and it projects reaching $20 billion in AI server revenue in FY2026.
2. IDC projects that spending on accelerated servers (i.e. servers optimized for AI/inference) will become more than 75% of server AI infrastructure spending by 2028, with a five-year CAGR of about 42%.

Why it matters:

• Design tradeoffs: you care about latency, throughput, capacity, burst scaling, cooling, power, and locality.
• Organizations that built for training will scramble to rearchitect for inference performance.

2. Inference
as a Service becomes table stakes

“Model hosting” services morph into full Inference-as-a-Service offerings, just like “Compute as a Service” did for infrastructure. Multiple industry sources cite predictions that about 78% of organizations will depend on Inference as a Service going into 2026.

Why it matters:

• Smaller teams and companies can compete without owning entire inference stacks.
• You’ll see marketplaces for low-latency inference endpoints, model versioning, and SLA guarantees.
• The barrier to deploying real-time AI drops.

3. Inference
is pushed into new domains thanks to agentic AI

With agentic AI gaining momentum, inference won’t just be called for static predictions or classifications. It will be used continuously in interaction loops: state management, tool invocation, planning, dialogue, etc. Gartner predicts 40% of enterprise apps will embed task-specific agents by 2026. Our research uncovered a lot more than dabbling in agents and agentic AI, with 5% already running in production and many more in the “we’re getting them ready” stage.

Why it matters:

• Inference becomes compositional: many micro-model calls per “task.”
• Latency budgets tighten. You’ll need smart routing, caching, guardrail layers, and partial evaluation.
• The boundary between edge and cloud inference blurs.

4. Inference
at the edge goes mainstream

Because you can’t always tolerate round-trip latency or dependency on cloud, more inference will shift to the edge or hybrid (edge + cloud) modes. Real-time workloads in AR/VR, autonomous systems, IoT, and industrial domains will demand it. This is implied in “inference as infrastructure” trends as well as the explosion of “AI PCs” and the distribution of AI on endpoints like smart phones.

Why it matters:

• You’ll see hardware specialization: tiny accelerators, TPU/ASIC at the edge, model quantization, pruning, runtime adaptation.
• Models will degrade gracefully between cloud-edge decisions, fallbacks, and “local-only” islands.

5. Inference
governance and explainability controls become mandatory

As inference scales, out-of-bounds or unfair decisions will hurt brands and compliance. Expect regulation and enterprise policy to demand traceable inference decisions, causal explainability, drift detection, and audit logs of every inference. For example, Deloitte’s trend analysis highlights safety, sovereignty, and control as critical themes for 2026. Our own research uncovered that organizations are protecting everything, even prompt logs. Eighty-seven percent of large organizations already use RBAC to govern prompt/log access.

Why it matters:

• Your inference fabric will need built-in logging, provenance, versioning, and guardrail layers.
• Semantic probes and runtime sanity checks (as you’ve already been thinking about) will be table stakes.
• Vendors will compete not just on speed and cost but on trust and explainability.

Inference is not just the next workload. It’s the new runtime, the one that tests—and often breaks—every lazy architectural assumption we’ve carried forward since the cloud boom.

Safety, sovereignty, and control aren’t buzzwords; they’re the currencies of trust in an AI-powered enterprise. By 2026, those who treat inference as infrastructure, not inspiration, will be the ones still standing when the hype burns off.

So yeah, you only need one word to sum up 2026: inference.

About the Author

Lori Mac VittieDistinguished Engineer and Chief Evangelist

More blogs by Lori Mac Vittie