Four companies. Four meetings. Four industries that look nothing alike on paper. And the same architecture underneath every pitch.
I spent two days this week listening to Ayar Labs CEO Mark Wade, Tenstorrent’s COO, Databricks CFO David Conte, and Wonder CEO Marc Lore walk through their businesses. Silicon photonics. RISC-V compute. Enterprise data. Food delivery. If you handed me the notes with the names stripped, I could not have told you which industry I was reading. Every one of them described the same moat: decouple the workload from the substrate, then own the substrate.
That is the reframe. Four layers, one moat. My bet coming out of those meetings is that this is the defining pattern of the next decade of infrastructure investing, and that most sell-side analysts are still siloed one layer at a time while the best operators are building horizontally.
A note on why I am writing about four private companies in a publication that is mostly about public-market allocation. The reason to track private companies at this stage is that they give you the framework to value the public companies they are competing with and working alongside. Ayar tells you something about Lumentum, Coherent, Credo, and Marvell. Tenstorrent tells you something about NVIDIA, AMD, and the HBM memory stack. Databricks tells you something about Oracle, Snowflake, and every enterprise SaaS incumbent. And Wonder, as I will argue below, tells you something about every publicly traded quick-service restaurant chain. What I heard from these four companies this week produced some of the most valuable public-market research I have collected all month.
Layer 1: Photons (Ayar Labs)
Ayar Labs is not new coverage for BEP. I have been tracking them since the Series C era, and most recently wrote about them in The Quiet Architect the week their $500 million Series E closed at a $3.75 billion valuation, positioning them as a direct competitor to Marvell’s Celestial AI Photonic Fabric in the scale-up optical race. Mark Wade was the most substantive speaker I heard all week, and the gap between the consensus public narrative on co-packaged optics and the pace he described was the widest of the four companies.
Total funding now stands at $870 million. NVIDIA has participated in four separate rounds. AMD in two. Intel Capital multiple times. TSMC is a multi-time strategic investor. The Hsinchu office opened the week before the conference. Investors still treating silicon photonics as a research program are reading the wrong news source. The capital stack says supply chain.
The 2028 ramp numbers: roughly 10 million optical chips per year at full run rate, 20 optical chips per GPU on leading-edge accelerators, ASPs between $100 and $1,000 per chip. At the midpoint that is a multi-billion dollar TAM that did not exist two years ago, concentrated into a handful of vendors inside TSMC’s COUPE flow. Wade was explicit that the path runs through TSMC specifically: “You are building in that ecosystem or you are probably not very relevant for the next few years.” That is a foundry concentration argument with real consequences for anyone trying to enter the market outside the TSMC orbit.
The technical claim is the part most investors underweight. A current Blackwell GPU escapes with about 20 Tbps of aggregate bandwidth across electrical I/O. Ayar’s 2028 target is roughly 10x the bandwidth, 10x the radix, and a distance ceiling that goes from one meter of copper to a hundred meters of fiber. That is not a speed improvement. That is a topology change. The rack stops being the unit of deployment. Two years of TSMC yield improvement, laser reliability gains, and UCIe standardization closed the gap from “maybe” to “inevitable.”
The reliability inversion Wade described is the update that matters most. Reliability used to be the argument against co-packaged optics. Now it is the argument for co-packaged optics, because copper reliability is degrading faster than optical reliability is improving. I wrote in The Great Photonic Divergence that the optical transition was a when-not-if question. Wade told the room the “when” is back half of 2028. GPU customers are no longer asking whether photonics survives the compute fabric. They are asking how many millions of units per month his team can deliver.
Layer 2: Silicon (Tenstorrent)
Tenstorrent was the one company I sat with directly this week. “We are Linux to NVIDIA’s Windows.” That was the pitch. Open compilers on GitHub. RISC-V instead of Arm. GDDR6 and on-chip SRAM instead of HBM. Ethernet mesh instead of NVLink. No CoWoS silicon interposer. No HBM supply queue. No CUDA lock-in. Jim Keller as CTO. Pricing that starts at $999 for a Blackhole PCIe card and $11,999 for an 80-billion-parameter workstation.
The architecture is the thesis. In The Packaging Paradox I argued CoWoS plus HBM is the real AI bottleneck, not transistor density. Tenstorrent routes around both by keeping weights in on-chip SRAM, leaning on commodity GDDR6, and skipping the HBM allocation fight entirely. The trade is weaker training throughput versus B200 class, but inference on LLMs and video generation is where they point the marketing. The benchmark claims out of the meeting were aggressive: “5 to 15x” speed advantages over NVIDIA B200, Groq, and SambaNova on targeted inference workloads. Vendor-published numbers. I treat them as positioning until third parties confirm at the May 1 TT Deploy event in San Francisco.
The real question for investors is whether Tenstorrent has a defensible place against NVIDIA, or whether it ends up as another SambaNova or Cerebras: real technology, real capital, no durable dent in NVIDIA’s share. In NVIDIA’s Inference Stack Depth Strategy I argued that “they sell chips; NVIDIA sells stack depth.” NVIDIA’s moat is not the GPU. It is Mellanox plus NVLink plus CUDA plus Run:ai plus Dynamo plus the Groq IP license plus TSMC allocation priority plus the installed developer base. A competitor who wins on the chip alone does not win. Tenstorrent understands this, which is why they are not competing on the chip alone.
My verdict: Tenstorrent has a real place, but a bounded one. Three vectors matter.
First, sovereignty buyers. Tenstorrent’s COO described sovereign buyers, countries like Brazil, telling him exactly what I wrote in AI for the Rest of the World. Countries do not want to bet national AI infrastructure on a single US vendor that can be cut off by export policy. Tenstorrent already has announced sovereign partnerships with Cyprus, UAE, Japan, and Korea. RISC-V plus open compilers plus commodity GDDR6 is the sovereign-compliant alternative NVIDIA structurally cannot match without giving up CUDA margin. Bounded TAM, but real.
Second, latency-bound workloads. XTX Markets invested $200 million because shaving microseconds off inference in algorithmic trading is worth buying a second-source silicon stack. Similar logic applies to high-frequency anomaly detection, real-time fraud screening, and on-premise sensitive-data inference where sending workloads to cloud GPU is not an option. These are not markets NVIDIA loses. They are markets NVIDIA has structurally said no to.
Third, the HBM-sidestep bet. SanDisk is co-developing floating-gate flash for AI weights because weights do not change every ten nanoseconds, so paying HBM prices to store them is architecturally wasteful. If that product works at scale, inference TCO shifts meaningfully. Not enough to break CUDA, but enough to make a class of workloads cheaper on open hardware.
Floor case: profitable sovereign plus algorithmic-trading plus developer workstation revenue. Ceiling case: the May 1 benchmarks get independently validated and the Quasar 4nm chiplet generation ships on time. I am watching, not owning.
Layer 3: Data (Databricks)
Databricks reported a $5.4 billion revenue run rate growing more than 65% year-over-year in Q4 2025, with $1.4 billion from AI products alone. Net retention is above 140%. More than 800 customers spend above $1 million annually, more than 70 above $10 million. Free cash flow is positive. The Series L in December 2025 raised $4 billion at a $134 billion valuation, roughly 25x annualized revenue.
Three things landed from the CFO’s session. The Neon Postgres acquisition is not a database play, it is an agent play: agents generate orders of magnitude more queries than humans, and running those on legacy enterprise database pricing does not pencil. Databricks has open-sourced every core technology where customers derive value and captures revenue at the layer above. On security and SIEM, the entry criteria are “adjacent to what we do” and “unfair advantage.” If you index your enterprise inside Databricks to train models against, you may as well run anomaly detection on the same substrate.
I wrote in Is Software Dead? that “Systems of record dominate digital context today, your CRM knows your customers, your ERP knows your inventory, your HRIS knows your employees.” Databricks is doing the same thing Oracle is doing from a different direction. Oracle owns the relational substrate and is adding agentic AI on top. Databricks owns the lakehouse and is adding a transactional database, agents, and security underneath. Both converge on the layer where private enterprise data meets foundation models. Whoever owns that layer collects rent on every inference call an enterprise ever makes against its own data.
The fourth company is the one most readers will miss. It is also the one with the clearest public-market read-through, and the reason holders of legacy QSR and fast-casual equity should calendar the S-1.
