This Week in Turing Post:

Hello from CES 2026, the world’s largest consumer electronics gathering, where the pace of change has reached a point where predicting what comes next feels more like staring at an event horizon than drawing a roadmap. It’s striking how much strategic scheduling can shape perception. Judging by media coverage alone, you might conclude that CES 2026 revolved around Jensen Huang again. In practice, NVIDIA placed Jensen Huang’s “special presentation” at 1:00 p.m. PT, a high-attention slot. CES’s official opening keynote, delivered by Lisa Su, AMD’s CEO, ran in the evening, when many attendees were already running low on attention.

I attended three chip-focused presentations at CES on Monday – Qualcomm, NVIDIA, and AMD — to see the whole picture. And this editorial reflects that. First, I’ll cover what announcement excited me the most and what it matters for our future. Second, I’ll draw you the picture of the real competition between infrastructure giants and the futures they are optimizing for.

Part 1, what excited me most: Alpamayo, and what it means for Tesla and self-driving cars in general

During his keynote, Jensen Huang presented Alpamayo 1 – a reasoning-based vision–language–action (VLA) model for autonomous driving, developed alongside large-scale simulation and synthetic data pipelines. “Simulation is where we are the most comfortable,” he said. And I wasn’t sure if it’s purely technical or metaphorical as well. Alpamayo 1 is roughly a 10-billion-parameter model, designed to take multimodal inputs such as surround camera feeds, vehicle history, and navigation context, and produce driving trajectories together with explicit reasoning traces. Those traces make decisions inspectable, which is critical for validation, debugging, and safety review. It took them 8 years to develop this model and NVIDIA Drive AV system based on that.

This reasoning VLA treats driving as a continuous decision-making process rather than a collection of scripted responses. This matters for a long-running debate in autonomous driving: sensors versus intelligence. Alpamayo strengthens the case that LiDAR-heavy architectures were, at the very least, overemphasized. If a system can reason about scenes, infer intent, and generalize from prior experience, it does not need a sensor suite that reshapes the vehicle into a rolling research prototype. That opens the door to cars that are not only safer, but also lighter, more energy-efficient, and visually closer to what consumers actually want to drive.

Tesla demonstrated this path first. And they’ve been successful with their full self-driving (FSD) system. But they haven’t yet fully solved the autonomy.

What Elon Musk means is that getting to 99% is achievable – solving the last one percent is brutally hard. The long tail is made of rare, ambiguous, poorly labeled scenarios that defeat pattern recall. That is where open-sourced Alpamayo family (that includes Alpamayo 1, AlpaSim, and datasets) becomes structurally important. By pairing reasoning models with simulation and by open-sourcing key components, NVIDIA is proposing a shared foundation for autonomy, one that allows multiple carmakers to build compatible, safety-validated systems without copying Tesla’s exact approach.

Does Alpamayo solve the autonomy problem? It doesn’t. But it changes how the industry works on it. That is why I’m excited. By open-sourcing models, data, and simulation tools, NVIDIA is proposing a shared foundation for autonomy. I keep returning to this point because it matters. The signal here is a transition to a new chapter, one with more participants, shared assumptions, and more ways to make progress on the hardest parts of the problem. (btw NVIDIA's autonomous vehicle team is 7,000 people, means ~20% of NVIDIA employees work on autonomous cars, according to Ben Bajarin. Pretty serious commitment).

Part 2: It Matters Where AI Runs

The central insight across Qualcomm, NVIDIA, and AMD is that AI is reorganizing computing around where it runs, not around a single “best” chip.

Qualcomm’s Snapdragon X2 narrative is the clearest window into the client-side surface. The presentation kept returning to always-on experiences and the practical problem of running them without draining the battery or spiking fan noise. Snapdragon X2 Plus and X2 Elite are pitched around an ~80 TOPS NPU baseline, paired with an ecosystem story: Windows features and third-party applications shifting specific workloads onto the NPU.

This is not a subtle change. If a PC is expected to summarize threads, caption audio, enhance video, and power “settings agents” continuously, then the NPU stops being a checkbox and becomes the always-on subsystem that keeps the rest of the machine stable. Microsoft’s presence onstage reinforces the architectural premise: hybrid AI is the long-term design (we will be talking about Hybrid AI more in the upcoming articles), with cloud models reserved for heavy reasoning and local compute handling latency-sensitive background tasks.

NVIDIA is working on the opposite end of the spectrum: centralized compute and physical deployment loops. Vera Rubin is the flagship proof. NVIDIA describes it as a rack-scale platform composed of multiple tightly integrated components including the Vera CPU, Rubin GPU, NVLink switch generation, ConnectX networking, BlueField DPU, and Spectrum-X networking, with strong emphasis on throughput, cost per token, and trusted computing features.

Vera Rubin is the “AI factory” surface, aimed at training and large-scale inference where the limiting factors are memory bandwidth, interconnect, power delivery, and utilization.

Alongside that, NVIDIA is extending the stack outward into the physical world via its “physical AI” package: world and robotics models, simulation tooling, and edge modules. The pre press-brief was explicit about a closed loop that spans training, simulation and validation, and deployment on robot and vehicle-class hardware. Alpamayo is positioned as an open reasoning VLA model for autonomous driving. Jetson T4000 anchors the deploy surface as a Blackwell-based module, described as a production-friendly upgrade path for robotics developers. As always, NVIDIA also provided the best show.

AMD’s keynote stitches the two worlds together. Lisa Su’s framing is an explicit bet on heterogeneity: cloud racks, PCs, edge, and scientific systems all sit on the same demand curve. “AI Everywhere, for Everyone,” was repeated a few times. AMD’s Helios rack-scale platform and MI455 accelerators represent its data center surface, built to scale training and inference with tightly integrated CPU, GPU, and networking. On the client side, AMD emphasizes Ryzen AI systems and OEM breadth, which is a parallel attempt to make local AI compute a default property of mainstream PCs, even if the NPU numbers differ from Qualcomm’s messaging.

Lisa Su also pulled up a heavyweight lineup with Fei-Fei Li (World Labs), Greg Brockman (OpenAI), Ramin Hasani (Liquid AI), Michael Kratsios (U.S. Office of Science and Technology Policy), John Couluris (Blue Origin) and others.

When you look at all of this together, the competitive picture becomes clearer. Qualcomm is making continuous local inference feasible at consumer price points. The ambition is focused and pragmatic: ensure that always-on AI can run locally, efficiently, and predictably, without relying on constant cloud access.

AMD is building continuity across cloud and device while offering full portfolios. The ambition is infrastructural. AMD is positioning itself as the connective layer across racks, PCs, and edge systems, assuming that heterogeneity is not a temporary phase but a permanent condition of AI computing.

NVIDIA is turning AI into an industrial system that spans centralized compute, simulation pipelines, and edge deployment. The ambition is systemic. By coupling large-scale platforms with simulation-first development and physical AI tooling, NVIDIA is aiming to control the loop by which intelligent systems are built, validated, and deployed in the real world.

Overall, extremely exciting times. Do you think, it is the moment when singularity begins?

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That’s all for today. Thank you for reading! Please send this newsletter to colleagues if it can help them enhance their understanding of AI and stay ahead of the curve.