Surviving the AI-Infra Correction: Inside Our 3Q26 Rebalance
No leverage, ~80 names, and a 25% drawdown absorbed — the decisions behind our 3Q26 AI-infrastructure rebalance.
Summary
This is a condensed version of Part 3 of our 3Q26 rebalance, diving into a selection of individual stock decisions — the full report, including our complete Soitec thesis, test and inspection, memory, and the silicon-photonics supply chain, is available at Convequity.
Recent blow-ups like Situational Awareness and Archegos reaffirm why our portfolio stays unleveraged and diversified across ~80 names, absorbing a 25% drawdown that would have been fatal in concentrated, levered books.
The rebalance’s central principle is that technical relevance alone is not enough — we exited positions where compute-native entrants or commoditising software layers eroded the strategic moat.
We favour bottleneck resolvers over bottleneck causers, with Soitec emerging as the marquee conviction because its Photonics-SOI wafers win regardless of which optical architecture prevails.
Where architectural outcomes remain unsettled, we prefer businesses positioned across multiple paths — hence increasing Ciena for coherent scale-across connectivity and adding to the under-appreciated test and inspection layer.
Pre-revenue and geopolitically exposed optionality is deliberately kept small — or held back entirely — a discipline made possible by the ballast of more mature, cash-generative holdings across the AI value chain.
Every Cycle Has a Cautionary Tale
In the latest AI-infrastructure boom-and-correction, Leopold Aschenbrenner’s Situational Awareness fund became the clearest cautionary example. Launched in 2024 by the former OpenAI researcher, the vehicle scaled from roughly $225 million to peaks of $20–45 billion and posted extraordinary returns — 439% net through the first half of 2026 — by concentrating in a narrow set of AI-infrastructure names and amplifying those bets with up to 4x leverage. The July 2026 sell-off turned that combination into a 67% monthly decline, forcing a fire-sale of the public equities book to Citadel and shrinking assets to around $8–10 billion. Even though the fund remained up roughly 80% for the year, the episode showed how quickly overconfidence, limited diversification and leverage can erase gains and threaten permanence of capital.
History repeats. In the 2020–2022 growth-stock cycle, Cathie Wood’s ARKK suffered an 80–85% peak-to-trough drawdown. Even with 30–50 holdings it remained far from true diversification and was heavily exposed to high-conviction, high-volatility names. A starker parallel is Bill Hwang’s Archegos, which concentrated in a handful of stocks and layered on massive swap leverage. When those positions reversed in March 2021 the structure collapsed in days, generating more than $10 billion in bank losses and wiping out the firm. Concentration plus leverage is a recurring recipe for asymmetric downside.
This is why construction matters.
The Convequity portfolio is deliberately different. It is unleveraged. It holds approximately 80 stocks spread across 11 segments that we map across the AI value chain — from semiconductors and optical interconnects through power, packaging, memory, software infrastructure, and adjacent enablers and to enterprise and consumer applications sectors. No single name or narrow cluster is allowed to dominate. Position sizing and rebalancing discipline are designed to keep risk manageable through volatility rather than to maximise beta in a one-way market.
That architecture has been tested this year. We added further AI-infrastructure exposure at the start of the second quarter of 2026. The timing proved constructive: after a –7% first quarter, the portfolio returned 37% in the second quarter and reached a year-to-date peak of +45%. When the July AI-infra correction arrived, gains retraced, yet as of early August the portfolio remains up approximately 8% for 2026.
To quantify the drawdown from peak: a nominal $100 at the start of the year would have stood at $145 at the high point and $108 today. The decline from that peak is roughly –25.5%. A 25% peak-to-current drawdown is material, yet it is a fraction of the 60–80%+ declines experienced by the more concentrated, leveraged books described above. Diversification across dozens of names and the absence of leverage absorbed the shock without forcing liquidations or permanent impairment.
The rebalancing decisions detailed in this Part 3 were taken in the final week of June. More than six weeks have since passed, and the intervening market moves have only reinforced the importance of the portfolio construction principles above. Markets will continue to deliver cycles of exuberance and correction. The managers who survive and compound through them are rarely the ones who ride the purest expression of a thesis with the highest leverage. They are the ones who respect position limits, maintain genuine diversification, and refuse to let temporary over-confidence dictate risk parameters. That is the discipline we apply.
Overview
This is the condensed version of the third and final instalment of our 3Q26 portfolio rebalance series, focusing on which companies are positioned to capture value as the AI infrastructure buildout moves from technological promise toward commercial deployment.
Across the holdings discussed below, we distinguish between technical relevance, commercial readiness and durable value capture. An important technology does not automatically make every participant in its supply chain an attractive investment. This framework informed our exits from businesses with weakening competitive positions, the retention of selected higher-risk optionality at modest size, and larger allocations to companies addressing unavoidable physical bottlenecks.
Ciena (CIEN): Increased as the coherent / scale-across play
Ciena has been increased from approximately 0.4% to roughly 1%. The stock rose only about 7% in the second quarter on relatively light news flow, leaving room for catch-up relative to other AI-infrastructure names.
The core thesis turns on the distinctive value of coherent technology. Coherent optics can do two things that conventional intensity-modulated optics cannot. Given the same bandwidth, it dramatically extends reach — enabling true scale-across architectures that connect AI clusters across campus distances (1–2 km) and multi-site distances (10–30 km or more). Given the same distance, it substantially improves recoverable signal quality, allowing systems to tolerate high insertion loss. The second property is becoming increasingly relevant inside the data center itself. Silicon-photonics-based optical circuit switches (OCS) in particular introduce significant optical losses; coherent technology offers a way to recover sufficient signal quality to make these high-loss fabrics practical. While large-scale commercial deployment of coherent specifically to solve SiPh-OCS insertion loss is still early, the requirement is emerging clearly as these architectures scale. In short, coherent is required both to leave the building and, over time, to help advanced optical fabrics work inside the building.
A simple mental model remains useful. Copper works only over the shortest reaches. Conventional optical transceivers extend distance but still hit limits as data rates rise, because the signal weakens and is distorted by dispersion; once the pulses are too degraded, simple intensity detection cannot recover the information. Coherent optics encode data in both the amplitude and the phase of the light wave. At the receiver a local laser mixes with the incoming signal so that advanced digital signal processing can compensate for the impairments and reconstruct a clean waveform. Ciena’s WaveLogic franchise is the clear commercial and technology leader in this domain. WaveLogic 6 Extreme remains the clear commercial leader at 1.6 Tb/s and is still the only solution in meaningful volume deployment. The company continues to push coherent capabilities closer to the compute layer through Coherent Lite and the Nubis co-packaged optics assets.
We frame the opportunity set as two distinct markets. Optical communication within the data center is becoming a red-ocean arena — crowded, well-understood, and heavily contested among short-reach pluggables, CPO, NPO and related technologies. Scale-across connectivity, by contrast, remains a blue-ocean market: fewer players have deep coherent expertise, the architectural requirement is only now becoming binding, and the market is still largely valued as a mature telecom/DCI business. Ciena’s decades of leadership in coherent systems position it to capture both the rising relevance of coherent inside the data center (as a solution to insertion loss) and the still-under-appreciated expansion of scale-across demand.
The market’s attention and capital have so far concentrated on the shorter-reach optical stack closest to the GPU. That framing is increasingly incomplete. The same physics that forced the industry from copper to short-reach optics is now forcing it from short-reach optics toward coherent once clusters become geographically distributed — and, in parallel, once high-loss optical fabrics appear inside the cluster. The increase in the position reflects that assessment.
Semtech (SMTC): Held, with a question mark on trimming
Semtech is maintained at approximately 1.5%. The company designs TIAs (transimpedance amplifiers), laser drivers and, critically, continuous-time linear equalizers (CTLE) that restore high-speed electrical signal integrity. In the optical chain these components amplify and clean the electrical signal before it enters the optical engine and after photodetection on the receive side. In that sense Semtech plays a role analogous to MACOM, but with particular relevance at the electrical equalization layer.
Amplifiers and equalizers are becoming structurally more important as data rates rise. Electrical signals rapidly approach the physical limits of noise, attenuation and frequency-dependent loss. Without high-performance equalization, even the best optical components cannot maintain clean links. The most strategic piece of Semtech’s portfolio in this context is CTLE.
Leading-edge 224G SerDes IP is tightly controlled by Nvidia and Broadcom. Tier-2 silicon designers that want to field competitive networking or accelerator chips often cannot access that IP on acceptable terms. When Google explored an inference-oriented TPU variant with MediaTek and sought to avoid heavy Broadcom royalties, the required SerDes performance proved extremely difficult to achieve. CTLE was used as a compensating technology — effectively a sophisticated analog patch — to recover enough signal quality to make the link viable. As more competitors attempt to challenge the Nvidia/Broadcom duopoly without paying for their SerDes, licensing advanced CTLE becomes one of the few practical ways to close the performance gap. Looking further ahead, when the industry moves toward 448G SerDes, even the tier-1 players will be pushing physical limits and will themselves require more aggressive equalization. Semtech’s CTLE technology is therefore relevant to second-tier players today and potentially to the leaders later.
The near-term risk remains real. Google is reportedly considering scaling back or exiting the MediaTek collaboration because the project has underperformed. Semtech was tied into that program, so any volume reduction would remove a meaningful near-term demand driver. The stock had already run roughly 100% in the quarter, which makes the risk of a pullback more acute and continues to argue for considering a partial trim.
On the financial side, consensus appears to be pricing roughly 25% durable growth. Our internal view sees potential for near-term growth closer to 50% as adoption of these high-speed analog components scales, settling into a more sustainable ~30% baseline as Semtech moves from a marginal to a more established position in the signal-integrity path.
The team remains split between taking some profit after the large run (and in light of the MediaTek risk) and holding for the longer-term structural adoption story. We are keeping the full weight for now, while remaining open to a modest trim if the near-term catalyst turns more negative or if the valuation stretches further without corresponding evidence of accelerating design-win momentum.
AXT Inc (AXTI): Modest intended exposure to the InP wafer bottleneck
AXT was identified for a small ~0.5% position as the near-monopoly supplier of indium-phosphide (InP) substrates, particularly as the industry begins migrating from 3- and 4-inch to 6-inch wafers. InP wafers remain one of the most acute bottlenecks in the optical supply chain; current supply is estimated to meet only around 30% of demand, leaving a substantial gap that is likely to persist as high-speed pluggable and co-packaged optics volumes scale.
The structural case is clear: AXT controls essentially the entire early 6-inch InP capacity and therefore sits at a critical chokepoint for lasers used in AI optical interconnects. It is both a bottleneck causer and, through its expansion plans, a partial resolver. There are effectively no other listed vehicles that give clean public-market exposure to this specific layer of the AI value chain; the remaining producers are Chinese-listed.
The risks, however, are material and multi-layered. Virtually all of AXT’s production sits in China through its Tongmei subsidiary, and China controls the majority of global indium supply. Although indium is not currently treated as a military-critical material and has not been subjected to the same export restrictions as some other critical minerals, the geopolitical exposure is real and largely hidden behind a U.S. listing. China could change its stance.
On the competitive side, Chinese producers are already manufacturing InP wafers at meaningful scale and are expanding rapidly. The competitive set is mixed: some players have upstream indium or related resource advantages and have integrated downstream into substrates, while others are dedicated compound-semiconductor or substrate specialists scaling under domestic supply-chain policies. Most of this new Chinese capacity remains concentrated on the more mature 2- and 4-inch formats. AXT retains a clearer know-how and production edge in early 6-inch crystal growth and is directing a meaningful portion of its 2026–2027 capacity expansion toward larger-diameter capability. Even so, the longer-term overhang from expanding domestic Chinese wafer capacity remains a concern and helps explain management’s relatively measured expansion posture.
While AXT is a genuine bottleneck exposure, the combination of concentrated China operational risk, rising domestic Chinese wafer competition, and elevated valuation relative to realistic 2028 revenue potential argues for only modest sizing. We therefore intended to establish a small allocation but were unable to purchase the shares, leaving the name as a monitored bottleneck exposure rather than an active holding at this stage.
Genomics: Exits and Retention
CRISPR Therapeutics has been fully exited. Although the position was added only about six months ago, the opportunity cost of holding it has risen as higher-conviction AI-infrastructure ideas compete for capital. The deeper reason is structural. AlphaFold demonstrated that large-scale compute could solve a problem — predicting a protein’s three-dimensional structure from its amino-acid sequence — that many in the traditional healthcare industry doubted could be cracked by compute alone. That advance, however, addresses the downstream “backend” of the problem. The harder and still largely unsolved “frontend” challenge is target identification: knowing which protein, pathway or cellular state is actually worth modulating in the first place. Most complex diseases remain poorly understood at the causal level; we can now predict the shape of almost any protein, yet we frequently still do not know which ones are the true drivers, whether inhibiting or activating them will help, or how the broader biological network will compensate.
The next transformative breakthroughs in this upstream domain appear more likely to come from AI-native groups that treat massive compute and foundation-model development as their primary engine, rather than from companies whose core competence remains earlier-generation wet-lab platforms such as gene editing. In the traditional model, discovery begins in the physical lab: a hypothesis is formed and then tested sequentially through cell assays, cultures and animal studies. Progress is slow and the searchable biological space is narrow. CRISPR is a powerful molecular tool for editing genes once a target has already been chosen, but it does not itself solve the harder problem of identifying which targets matter. AI-native approaches invert this sequence — large-scale models trained on biological and clinical data propose the most promising targets, and the wet lab becomes the validation step rather than the primary discovery engine. In that sense, finding the eventual genomics equivalent of OpenAI or Anthropic before the “ChatGPT moment” is the higher-upside path — and public markets may only gain meaningful access later via secondaries or IPOs. CRISPR does not clearly sit on that compute-driven inflection, and the capital has been redeployed accordingly.
Tempus AI was retained at approximately 0.8%. It survived the review because it is more deeply embedded in the AI-native side of the landscape. The company is building and post-training its own models, but its more durable advantage is the large-scale proprietary clinical and molecular data it collects through downstream partnerships. Even if Tempus is not the single pioneer that ultimately delivers the decisive computational breakthrough in causal biology, its data moat positions it to play a significant role in whatever ecosystem emerges. No extended debate was required; Tempus remains the preferred holding within the genomics and precision-medicine portion of the portfolio.
Marvell (MRVL): Monitored, no action
Marvell was discussed at length as a potential new position and as a case study in competitive dynamics the team initially under-weighted. The market has generally interpreted Nvidia’s roughly $2 billion investment as a straightforward endorsement or partnership. The strategic intent appears more sophisticated.
Marvell is one of the few meaningful alternatives to Broadcom in optical DSP. Broadcom can already leverage its DSP IP inside broader custom-silicon bundles; Nvidia historically lacked an equivalent lever. Backing Marvell gives Nvidia influence over a critical part of the DSP supply chain and helps balance Broadcom’s power in that layer.
At the same time, Marvell has been structurally weak in both custom AI accelerators and AI networking silicon relative to Nvidia and Broadcom, in large part because it lacks ready access to leading-edge 224G SerDes IP. The contrast between Marvell-designed Trainium 3 (widely viewed as disappointing) and the success of Google’s TPUv7 underscored the gap. Nvidia’s response has been to pull Marvell closer into its own orbit: rather than Marvell continuing to develop independent high-speed I/O for networking chips, the path of least resistance becomes designing compute silicon that attaches to NVLink. In effect, Marvell risks becoming a shadow custom-ASIC design partner within the Nvidia ecosystem rather than a fully independent competitor.
The same dynamic extends to scale-up networking standards. The field has largely coalesced around two camps — NVLink (Nvidia) and Scale-up Ethernet (Broadcom). UALink emerged as the alternative backed by the remaining players, with Marvell designated as the primary switch supplier. Once Marvell is financially and strategically aligned with Nvidia, the incentive to champion a competing UALink standard diminishes sharply. The investment therefore simultaneously (1) secures Nvidia greater influence over DSP supply, (2) redirects certain custom-ASIC efforts toward NVLink, and (3) weakens a potential scale-up competitor to NVLink.
We remain sceptical of treating every Nvidia partnership as an automatic positive. The game-theoretic overlay is complex enough that the team elected not to initiate a position at this stage. Marvell stays on the monitored list with a clear structural caution around the long-term economics and strategic independence of being an Nvidia ally.