We research the AI buildout end-to-end — from nuclear fuel and grid equipment through wafers, optics, hyperscale compute, models, and agentic execution — and we translate architectural reality into unit economics, gross-margin trajectory, and long-duration valuation.
The AI buildout is not a single trade. It is a chain — and value migrates along it as bottlenecks shift. Click any segment to see our current view, what's tight, and the names sitting on each side of the constraint. See Coverage for the disrupted-incumbent watchlist.
Most research is finance-first — and misses the architectural moats — or tech-first, and waves away unit economics. We do both, and we translate one into the other.
"I started Convequity because I kept seeing the same disconnect — Wall Street pricing companies off trailing metrics while the real story lived in the architecture, the market dynamics, and the founder's roadmap. We go deeper into the technical stack and wider into the structural forces to understand where growth and profitability are actually heading. Then we marry that to the financials with rigor. The asymmetry between what the market sees and what the technology enables — that's our entire edge."
We dissect tech stacks layer by layer — wafer chemistry, packaging, optics, custom silicon, data planes, cloud abstractions. Architectural bets compound across capex cycles and dictate where margin actually accrues.
Architectural choices and market structure (top-down GTM vs. bottom-up adoption, seat-based vs. consumption) determine entry barriers, gross-margin trajectory, and operating leverage. We model it explicitly — full assumptions, scenarios, and sensitivities, in transparent Google Sheets clients can stress-test themselves.
Wall Street's short-termism dismisses vision as intangible, focusing instead on minor details for quarterly models. Yet, a founder's vision is fundamental—it drives the product, shapes the culture, and guides the long-term roadmap. We explicitly weight this, backing visionaries who have a proven track record of executing their ambition.
A selection of currently open long positions, mapped to the AI Value Chain. Returns are unrealized, since position open. Live portfolio verifiable on request.
As of week ending 2026.05.01 · refreshed weekly
Returns shown are unrealized P&L on selected open long positions from a 70+ name technology portfolio co-managed by Convequity, expressed as percentage gain from cost basis at position open through the most recent weekly mark. Losing positions exist alongside winners, and the full portfolio (open and closed) is available to qualified subscribers on request. Past performance is not indicative of future results. This is not investment advice or a solicitation.
No clicks, no dropdowns. The full coverage list, organized by AI Value Chain segment. The disrupted-incumbent watchlist is included as names to avoid, short-bias, or exit on rallies.
Coverage list is illustrative and updates as the chain evolves. Other tickers are actively being added to our coverage. Full report archive available to subscribers.
Tower's SiGe BiCMOS + SiPho platform on 300mm wafers eliminates GFS's sole advantage, delivers 30% lower power than InP at 1/3 the cost with 85%+ yields, and cements TSEM as the dominant SiPh foundry for both hyperscale AI and Space DC deployments.
Convequity’s AI Bubble Barometer shows AI valuations easing further this week while commercial demand for compute remains strong. Productive usage is still concentrated among developers, but is expected to broaden significantly as models improve and costs decline.
Part 3 examines why epitaxial qualification — not hardware — is Aixtron's true moat, how InP substrate constraints gate capacity expansion, and why Veeco's competitive gains appear bounded enough that Aixtron can lose share while still growing into a much larger market.
Single-thesis arguments, framework updates, and cross-segment observations. Published when the thinking warrants it — not on a schedule.
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Palantir just posted a quarter that validates the "NVIDIA moment" thesis — 85% revenue growth, 54% FCF margins, and a Rule of 40 score most enterprise software companies never touch. But the real debate isn't the numbers; it's whether agentic AI disrupts Palantir's position or supercharges it. Our analysis unpacks why the application-layer moat is far more durable than the market fears, where the one genuine risk actually lives, and why collapsing inference costs may expand the TAM faster than competition can erode share.
Every leading silicon photonics foundry starts with the same wafer — and Soitec is the near-monopoly supplier. Our analysis maps five independent demand drivers that all converge on Soitec's Photonics-SOI platform simultaneously, explains why the pluggable-to-CPO transition intensifies rather than substitutes wafer content, and details the proprietary roadmap that keeps Soitec's moat widening as the industry scales to 1.6T and beyond. This is one of the most structurally advantaged positions in the entire AI optical supply chain, and most investors haven't mapped it yet.
POET's Optical Interposer promises to do for photonics what standardized packaging did for semiconductors — replace precision manual assembly with wafer-level integration and open the transceiver market to high-volume manufacturers previously locked out. The opportunity is real, but so is the execution cliff. Our analysis maps POET's credible path to volume through the Marvell–Celestial AI–Nvidia supply chain, explains why Broadcom's CPO stack is structurally closed to it, and confronts the valuation head-on: at 79× forward EV/Sales on minimal revenue, this is a venture-grade bet with asymmetric upside and zero margin of safety. We lay out exactly what must go right — and by when.
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