CES has always been a bellwether for where technology wants to go. CES 2026 felt different. This year wasn’t defined by a single breakthrough announcement or the unveiling of a bigger, shinier model. Instead, it marked a quieter, but more important shift in tone.
AI didn’t get bigger at CES. It got more real.
Across keynotes, booths, and conversations, the focus moved away from who can train the largest model and toward a harder set of questions: How do you run AI reliably? Where does inference actually happen? And what does it take to deploy AI systems outside of a controlled demo?
From Training Obsession to Inference Accountability
The most notable pivot at CES wasn’t a rejection of training, but instead it was an acceptance that inference is now the bottleneck.
Training remains the domain of a small number of hyperscalers and frontier labs. But inference is where AI meets reality: power budgets, latency constraints, connectivity gaps, and cost ceilings. This is where architectural decisions start to matter more than peak theoretical performance.
The economics tell the story. A model trained once can be deployed millions of times. Every inference event carries a cost in compute, power, and infrastructure. When you're processing video streams 24/7, analyzing sensor data in real time, or running vision models on battery-powered devices, efficiency stops being a nice-to-have. It becomes the entire business case.
At CES, conversations increasingly centered on:
- Predictable inference cost
- Power efficiency and thermal envelopes
- Deployment complexity
- Offline and near-edge operation
This isn’t a shift away from training, but a decoupling of roles. Training remains the workload that creates models and capabilities. Inference underpins how those capabilities are applied, embedded, and scaled across real systems. It becomes a tool that improves and accelerates every workload, from databases and video analytics to robotics and industrial automation. In that sense, inference is no longer an afterthought. It is the defining challenge for turning AI into something usable.
That’s the challenge Axelera AI was made for. At our CES suite, we demonstrated exactly what efficient inference looks like in practice: a 4-chip PCIe card capable of running up to 16 concurrent AI models processing 8K video on a single edge device. Pose detection, face recognition, and segmentation running simultaneously without thermal throttling or performance degradation.
Edge AI is Here, and it’s Demanding Definitions
Another clear theme was the broad push by chip makers into edge AI. On the surface, this looks like diversification. Underneath, it reflects something deeper: constraints force honesty.
Edge environments don’t allow for vague promises. They expose the gaps between marketing claims and deployable systems. At CES, “edge AI” was used to describe everything from embedded vision systems to rackmounted servers branded as edge appliances.
That ambiguity matters because edge AI isn't just about location. It's about operating under real-world constraints that datacenter AI never faces. True edge deployments must handle thermal challenges in industrial settings, operate reliably without constant connectivity, and deliver consistent performance on limited power budgets.
True edge AI raises hard questions that expose architectural choices:
- Can models run offline?
- What host system is required, and how heavy is it?
- How much power does inference actually consume?
- How easily can developers port existing models?
CES made it clear that edge AI hasn’t just arrived, it’s demanding clearer definitions and greater accountability.
Physical AI: Vision, Belief, and Skepticism
“Physical AI” emerged as the phrase of the week, often used to describe robotics, vision-guided systems, and realtime perception. The excitement is justified. These systems represent the next wave of AI value, where software directly interacts with the physical world.
Manufacturing lines that detect defects in real time. Autonomous mobile robots navigating dynamic warehouse environments. Agricultural systems that respond instantly to crop conditions. These applications unlock genuine business value by bringing AI capabilities to where physical work happens.
But CES also surfaced healthy skepticism.
Many physical AI demos glossed over fundamentals like deployment readiness, power consumption, or dependency on cloud connectivity. Belief in physical AI is widespread, but belief alone doesn’t ship products.
For physical AI to move from concept to scale, it must be:
- Deterministic: producing consistent results under varying conditions
- Efficient: operating within strict power and thermal budgets
- Cloud-independent: capable of operating without constant connectivity
In short, physical AI only works when inference works. The promise of robots and intelligent systems is constrained by the same reality facing every edge AI deployment: you need reliable, efficient inference that operates in the real world, not just in controlled demonstrations.
What We Heard
At Axelera AI, we spent CES listening and learning, and yes, showcasing technology. The most common questions we received weren’t about peak performance, but were about practical deployment:
- How flexible is your SDK, and how much control do developers have over the pipeline?
- How difficult is it to port an existing model?
- What does real‑world power consumption look like?
- Does the system require a full host or can it work with a lightweight one?
- Can AI workloads run fully offline?
- Is the supply chain ready for production deployments?
These questions signal a market that has matured. Teams aren’t experimenting anymore - they’re planning to ship.
The realities of practical deployment came up with independent software vendors (ISVs) who reinforced that while promptable open-vocabulary models are generating a lot of excitement, customers are still relying on traditional closed-set models with fine-tuned datasets.
We were able to work around using a vision language model (VLM) by implementing a LLM combined with a segmentation model such as the COCO dataset. There were others on the show floor implementing this same near-term solution because the point is not about cutting-edge research, but instead, production-ready engineering. While we’re excited to add VLM support this year, users can get the results they need today with this simple solution.
The lesson is clear. The models that ship aren't necessarily the ones generating papers. They're the ones that work reliably, deploy easily, and deliver consistent results under real-world conditions.
Building for the Inference Reality
CES 2026 reinforced something we’ve believed for a long time: edge AI success isn’t defined by hype cycles or buzzwords. It’s defined by whether inference survives contact with the real world.
The most compelling demonstrations at CES weren't the ones with the most impressive specifications. They were the ones solving actual business problems with measurable ROI.
For example, our partner, WebOccult, demonstrated quality control for commercial bakeries, using high-frame-rate cameras and computer vision to detect, classify, and count different products moving down manufacturing lines at 90 frames per second. WebOccult was able to highly customize their solution with the Voyager SDK and complete it within 30 days. These aren't aspirational use cases. They're production systems running today, solving problems that directly impact business operations.
Notable systems at CES included:
- Tooling that works with existing models, not against them
- Architectures designed for power‑constrained environments
- Systems that operate reliably without cloud dependencies
- Transparency around deployment requirements
As edge AI moves from aspiration to reality, the industry’s focus is shifting from what could be possible to what can be deployed, scaled, and supported. The technologies that succeed won't be those with the highest theoretical performance. They'll be the ones that solve real problems under real constraints.
CES didn’t mark the arrival of edge AI. It marked the moment edge AI started being taken seriously.
And that’s a far more interesting place to be.