§ I
Where we are standing
Gross & Cidecian Capital is run by two partners: Lars Gross and Christian Cidecian, both based in Zurich. This is not a fund. We do not manage outside capital. What you see here is our own money, our own reasoning, and our own record, disclosed in full.
Gross is an AI engineer. He builds production AI systems professionally and develops the proprietary models the firm uses for pattern recognition, anomaly detection, and price forecasting: transformer inference stacks, retrieval pipelines, evaluation harnesses, the plumbing that turns research into actionable signal. Over five years, he has maintained a personal investment track record averaging more than 30% annually. The edge is simple: he works every day on the same infrastructure the portfolio bets on.
Cidecian covers the other half of the mandate. His background is in informatics and technology, with a specialisation in blockchain infrastructure and commodity analysis. He has spent years developing deep expertise in digital assets and the commodity cycles that underpin the technology build-out, where on-chain data, supply-chain dynamics, and macro flows create informational asymmetries that most equity-trained analysts simply do not see.
Together we have operated this book for over three years, averaging more than 30% annually. The framework below is what has driven that outcome.
§ II
The analytical edge
Beyond domain knowledge, we invest on the basis of a proprietary set of AI analysis models we have built ourselves. These systems are designed to do what a human analyst cannot do at scale: process large volumes of heterogeneous data, including technical research, chip roadmaps, patent filings, inference benchmarks, on-chain transaction flows, commodity supply data, options positioning, and macro indicators, and surface anomalies, recurring patterns, and structural divergences between what the market has priced and what the underlying data actually implies.
These are not dashboards with canned signals. They are purpose-built systems, trained on the specific domains we invest in, updated continuously, and interrogated critically. The output is not a trade recommendation: it is a structured set of observations that either confirms or challenges our prior thesis. Every position in the book has been filtered through this process.
That analytical layer, combining domain expertise with machine pattern recognition, is what gives Gross and Cidecian the confidence to hold concentrated positions when the consensus is uncomfortable, and to step back when the data stops confirming the thesis.
§ III
The thesis, in one paragraph
We are in the early years of the largest capital-deployment cycle of our lifetimes: an industrial build-out of compute, energy, and data infrastructure to support models that will become economically indispensable across every white-collar function. Alongside that build-out, cryptocurrency is transitioning from a speculative asset class to institutional-grade financial infrastructure. And the commodities underpinning both, energy, metals, and rare earths, are structurally undersupplied relative to the demand that AGI and electrification will place on them. Markets understand these trends directionally. They consistently mis-estimate their magnitude, their durability, and who actually captures the rent.
The book is concentrated where the engineering reality, the on-chain data, and the commodity supply picture are furthest from the market narrative. That gap is where the mispricing sits.
§ IV
Five convictions that drive position sizing
1. Inference, not training, is the durable demand.
Training spend is lumpy, headline-driving, and finite per model generation. Inference is the recurring revenue layer: every query, every agent loop, every embedding refresh. Every capable model that gets deployed creates a permanent floor of inference demand that compounds with adoption. The book is biased toward names that capture inference economics: memory bandwidth, networking fabric, power-efficient accelerators, and the dark-fibre and cooling layers that make inference at scale physically possible.
2. Robotics is the next wave of the build-out.
The compute build-out does not stop at data centres. The same models becoming economically indispensable in the digital economy are now being embodied in physical systems. Robotics, from warehouse automation to humanoid form factors, will draw on the same chip architectures, the same inference stacks, and the same energy infrastructure as AI at large. The names that win in robotics hardware and the software layers that run on them are structurally early in their pricing cycle. We hold selective exposure where the engineering evidence is verifiable, not narrative.
3. Crypto is infrastructure now, not speculation.
Cryptocurrency has crossed a threshold. The institutional on-ramps are in place: spot ETFs, regulated custody, sovereign allocations. Bitcoin and Ethereum are no longer speculative assets priced on retail sentiment. They are global settlement infrastructure with an inelastic supply schedule. On-chain data lets Cidecian see positioning, accumulation, and liquidity shifts in real time, with a fidelity that is simply unavailable in traditional markets. The book holds crypto exposure where the on-chain thesis is clear, not as a macro hedge or narrative bet.
4. Commodities are the physical constraint on digital ambitions.
Every GPU requires cobalt, copper, and rare earths. Every data centre requires power infrastructure that requires steel, aluminium, and transmission equipment. Every electrification wave, EVs, grid storage, and hyperscale cooling, compounds the demand. Meanwhile, commodity supply has been structurally under-invested for a decade, a consequence of ESG-driven capital withdrawal and regulatory friction. The gap between what AI and electrification will demand from physical supply chains and what those supply chains can produce in the next five years is a durable source of asymmetric opportunity. We hold commodity exposure selectively, where supply constraints are verifiable and the demand catalyst is tied directly to our core thesis.
5. AGI is not priced in. Neither is what it breaks.
The market has priced AI as a feature. It has not priced AGI as a regime. Systems capable of autonomously executing multi-week knowledge-work projects, plausible within this decade on current scaling trajectories, would re-rate labour, capital allocation, and the relative value of every business that monetises cognitive output. That is not in the consensus discounted cash flow. The infrastructure required to run those systems is also not fully priced. The book is long that infrastructure, sized for an outcome that most models still treat as a tail risk rather than a base case.
§ V
How we size, when we sell, what we avoid
Concentration over diversification.
The book holds roughly six to twelve positions at any time. Top five typically account for more than half of capital. Diversification is the price you pay for not having a view. We have views.
Position-size by conviction, not by market cap.
A small-cap with a verifiable technical moat and asymmetric upside deserves more weight than a mega-cap consensus name where the entire street agrees with us. The biggest mistake any investor can make is to hold consensus names at index weight and call it a strategy.
Options and leverage are tools, not strategies.
We use long-dated calls on specific binary catalysts where the engineering evidence is strongly asymmetric and implied volatility under-prices the catalyst. We use leveraged instruments selectively for short-duration trades around clear technical events. Every such position is disclosed in the register with strike, expiry, and rationale. The default instrument is the underlying asset.
What we avoid.
Pre-revenue AI consumer apps whose moat depends on an API rate limit. Pure-play training-compute names priced for permanent peak demand. Crypto narratives with no on-chain substance. Commodity positions without a verifiable supply-side thesis. Sell-side consensus mega-cap longs at index weight: we either have a real view or we do not own them.
§ VI
What we owe the people reading this
Transparency, on a delay short enough to be useful and long enough to not be reckless. Every open position is on the public register. Every closed position stays on the register, including the losers, with the actual realised return, not the sanitised version. Theses are written before the position is opened and dated. When we are wrong, the post-mortem is published.
We do not promise alpha. We do not promise a smooth ride. Concentrated portfolios can and will draw down sharply. We promise only that what you see here is what we are actually doing with our own capital, and that the reasoning will be written down before the outcome is known.
§ VII
A note on what this is not
This site is not investment advice. It is not a solicitation. It is not a fund. Neither of us is a registered adviser in any jurisdiction. We run a personal book and write about it publicly because the discipline of writing is what keeps the reasoning honest.
If at some point in the future we raise outside capital, it will be on terms that are clearly disclosed and to qualified investors only. People who have been reading the letters will know first.
Lars Gross & Christian Cidecian · Zurich · Track record since 2022 · Est. 2026