Thank you Lam Capital for the great support and summary! GTC 2026 clearly showed that the AI stack is being fundamentally rewritten. A few reflections from our side at TetraMem – Accelerate The World: 🧠 The real bottleneck is shifting As AI becomes the infrastructure layer, the challenge is no longer just compute scaling — it’s data movement, memory, and energy efficiency. 👉 The industry is moving from: Compute-centric → memory-centric architectures Training focus → inference at scale ⚡ Inference is where efficiency matters most We fully agree — inference is now the strategic battleground. At scale, the key metrics are: *Energy per token *Latency *Cost efficiency This is exactly where compute-in-memory (CIM) and new architectures can deliver step-function improvements. 🔗 Beyond interconnect — reducing the need for movement Optical interconnect is a critical step forward, but it also highlights a deeper issue: We are still trying to move massive amounts of data faster. At TetraMem, we believe the next leap is: 👉 Eliminating unnecessary data movement altogether 🚀 Our perspective With memristor-based analog in-memory computing: *Compute happens where the data resides *Memory and compute are unified *Efficiency scales from edge to data center 🎯 Bottom line AI is becoming infrastructure — but its scalability will be defined by how efficiently we handle data, not just how fast we compute. Excited to see the ecosystem pushing forward across compute, memory, and interconnect. #AI #GTC2026 #Inference #EdgeAI #DataCenter #Semiconductor #Memory #ComputeInMemory #RRAM #Memristor #DeepTech
🚀 GTC 2026 reinforced just how quickly the AI stack is being rewritten, and where the next wave of venture opportunity lies. At Lam Capital, a few themes stood out clearly: 👉 AI has become the infrastructure layer. Compute, data workflows, design, automation, everything now assumes AI as the default substrate. Companies no longer “add AI”; they build on AI. This shift is opening entirely new system‑level opportunities for startups reshaping compute, memory, networking, and software orchestration. 👉 Inference has become strategically important. As models continue to scale, the challenge has shifted from training breakthroughs to delivering low‑latency, cost‑efficient inference at massive scale. This is driving new architectures and new silicon, and accelerating demand for specialized acceleration, sparsity, and memory‑centric compute. Lam Capital portfolio companies like d-Matrix, and TetraMem - Accelerate The World are directly aligned with this transition, pioneering inference‑optimized compute architectures built for the real workloads. 👉Optical interconnect is emerging as the answer to the latency wall. Electrical interconnect simply cannot scale to the bandwidth and latency requirements of next‑gen AI clusters. Optical solutions such as photonic fabrics, chip‑to‑chip links, optical I/O were front‑and‑center at GTC as the path forward for multi‑die and multi‑rack AI systems. We’re proud to support companies like Avicena Tech, and OpenLight each pushing the frontier of photonics and optical interconnect to enable faster, larger, and more efficient AI systems. 👉 Agentic AI is becoming a company‑level strategy. Agentic systems, autonomous, multi‑step, goal‑directed AI, will transform how businesses operate internally and how products interact externally. The shift is as significant as the move to cloud a decade ago. Successful companies will adopt an “agent‑first” architecture across workflows, tooling, and customer‑facing experiences.
