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The signals started stacking up around November.

But they weren’t showing up in the usual places. Not in miner earnings. Not in policy decks. Not in the standard reshoring rhetoric that now surrounds every critical-minerals conversation.

They were showing up lower down — in fragments, in conversations, in cap tables.

One conversation was about battery black mass in Europe, and why the real bottleneck wasn’t feedstock availability so much as process economics. Another was about copper recovery from ore that operators had long stopped treating as especially interesting. Another was about gallium and scandium showing up in industrial waste streams that nobody, until recently, was really framing as supply.

At first, none of it seemed like the same market.

Then the pattern started to harden.

Between late 2025 and early 2026, four seed-stage companies raised around four very different technical approaches to the same underlying problem: how do you create new critical mineral supply from feedstocks the incumbent system already discards?

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A Danish startup using molten salt chemistry to crack open dead EV batteries and wind turbine magnets. Two competing approaches to recovering copper from low-grade ore and waste rock that conventional mining struggles to process economically. And a UT Austin spinout building cartridge-like separation modules to pull gallium and scandium from industrial sludge.

Four companies. Four different chemistries. One shared directional bet: the fastest path to new critical mineral supply may not run through a greenfield mine at all.

It may run through waste.

Not recycling in the loose, comforting sense of the word. Not shredding, smelting, and hoping the economics hold. Not substitution. And definitely not a 16-year permitting cycle to blast a new hole in the ground.

What’s emerging instead looks more like a recovery-first supply stack: modular, process-engineered systems designed to extract bankable volumes of strategic metals from feedstocks that already exist at scale — spent batteries, depleted ore, tailings ponds, refinery residues, and other material the legacy supply chain still tends to treat as nuisance, scrap, or write-off.

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That is what this edition of Rumors is about.

Not just four startups, but the possibility that they are early expressions of the same market formation: a new race to build a faster path to critical mineral supply by turning discarded material into usable output.

Rumors is The Scenarionist’s column for pattern recognition across emerging trends and market formations in deep tech.

It exists because we spend hundreds of hours mapping capital flows round by round, tracking inflection points, and staying close to the builders, backers, operators, and experts shaping these markets in real time. Most signals look marginal when you catch them one at a time. The interesting part is when they begin to cluster.

This is the seventh edition of Rumors.

If you join The Scenarionist Premium, you’ll get access to every previous edition of Rumors, along with the rest of our Premium Library!

You can find previous editions here:

And now, let’s begin.

Enjoy the read!

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✨ Rumors is a pattern-recognition layer for investors, founders, and operators who care about where the frontier is heading—before it becomes consensus, before the rest of the market connects the dots.

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1. From Fossil Heat to Photons: Rewriting the Process Baseline

Modern chemical manufacturing is placing unprecedented demands on energy systems, feedstocks, and carbon budgets.

The chemical sector is now the largest industrial energy consumer and the third-largest source of direct industrial CO2 emissions, according to the International Energy Agency [1]. When you fold in both fuel and feedstock use, chemicals and petrochemicals account for roughly 10 % of global final energy consumption and around 7 % of industry-related greenhouse-gas emissions—a share that is still rising as demand for plastics, fertilizers, coatings and specialty intermediates grows. Analyses of industrial energy use [2] and sector-level emissions breakdowns [3] converge on the same picture: a massive base of long-lived assets whose business model is built on high-temperature heat from fossil fuels and fossil-derived carbon as feedstock.

The result is a situation where product volumes keep climbing while the sector’s operating space tightens. Energy-intensive processes and steam crackers were tolerable when fuels were cheap and carbon was free; they look less comfortable in a world where energy-related CO2 emissions just hit a record 37.8 Gt [4] and industrial emitters are expected to shoulder a disproportionate share of future reductions.

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At the same time, producers face feedstock risk as oil and gas markets swing, regulatory pressure from emerging carbon border mechanisms and sectoral roadmaps, and customer pressure as downstream brands commit to “low-carbon” and “bio-based” materials.

Recent work on well-to-gate emissions from petrochemicals [5] and global GHG breakdowns by sector [6] suggests chemicals and petrochemicals alone contribute on the order of 5–6 % of global GHG emissions—putting the sector in the same league as steel and cement.

Given this pressure, a new class of technologies is entering the conversation: light-driven, or photon-chemical, manufacturing. Instead of driving key steps with only heat and pressure, these approaches use photons as a first-class energy input, often at or near ambient temperature.

Visible-light photoredox catalysis [7] and related photocatalytic methods can mediate redox steps with LEDs rather than burners, while continuous-flow photochemistry [8] and modern reactor designs [9] solve old problems of light penetration, heat management and scale-up that kept photochemistry in the “lab curiosity” box for decades.

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In parallel, hybrid photo-thermal reactors combine concentrated sunlight with catalysts to turn CO₂ and green H₂ into synthesis gas and liquid fuels, while emerging photochemical routes promise to valorize lignin into aromatics instead of burning it [10], and to generate hazardous intermediates like phosgene in situ under light rather than storing them in bulk.

Large incumbents are taking notice. Pharma and specialty producers have already brought flow photochemical steps into GMP manufacturing [11] ; academic labs continue to publish high-productivity flow photoreactors [12] and metallaphotoredox scale-ups [13] ; and early industrial pilots are exploring solar-driven CO₂-to-fuel plants and “photo-on-demand” phosgenation in modular units.

A new cohort of startups is racing to productise this emerging toolkit: LED-driven photocatalysis platforms for fine chemicals, solar reactors for CO₂-derived fuels, modular photo-flow hardware for plants that want to “plug in” light, lignin-to-aromatics processes, and light-driven micro-plants for hazardous intermediates. Venture and corporate capital is following, treating photons + catalysts + flow hardware as a new infrastructure layer in chemical manufacturing rather than a niche lab technique.

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✨ This Rumor investigates why photon-chemical manufacturing is emerging now, what macro forces drive adoption, which problems it hopes to solve, and where the leading players are focusing.

At its core is a map of five startups across the US, Europe, and Japan — each attacking a different choke point in the photon-chemical stack: cleaner commodity routes, solar fuels, photochemical tooling, lignin-to-aromatics, and on-demand hazardous intermediates.

It analyzes market metrics, compares the approaches of these platforms, examines competitive dynamics, follows where capital is flowing, and offers a strategic lens on how these shifts affect chemical majors, utilities, engineering firms, and the broader industrial ecosystem.

It also considers risks and limitations, then looks ahead to how plants, assets, and supply chains might evolve over the next decade if light becomes a standard knob in the chemical process toolbox.

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2. Macro Frame: Why Now?

Photochemical processing is not a new idea; industrial plants have used UV-driven chlorinations, polymerizations and curing steps for decades in coatings, inks and plastics, as documented in early industrial photochemistry surveys.

Historically, though, the core of chemical manufacturing stayed with high-temperature, fossil-fuelled reactors because early UV systems were lamp-limited, hard to scale uniformly and awkward to integrate into continuous plants built around steam and burners.

Five converging forces are changing that calculus:

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✨ Inside This Rumor

Optical Circuit Switching in AI Networks

1. Introduction – Why the explosion of AI is forcing a rethink of data center networks, from copper to optical.

2. Macro Frame: Why Now? – Five converging forces—from surging GPU clusters to breakthroughs in silicon photonics—are accelerating the shift toward optical circuit switching (OCS).

3. Framing the Problems – A breakdown of bottlenecks in today’s networks: bandwidth ceilings, energy inefficiencies, rigid topologies, and spiraling costs.

4. Market Metrics That Matter – Key indicators of momentum, from network power consumption and costs in large AI clusters to venture capital bets on photonic interconnects.

5. Player Mapping: Startups Shaping the Rumor – A close look at six startups unbundling the data center network: one beam of light at a time.

6. The Competitive Edge – A complementary ecosystem: each team removes a bottleneck electrons can’t.

7. Strategic Lens – What this means for hyperscalers, semiconductor makers, and the broader AI infrastructure ecosystem.

8. Risk Assessment Framework – A grounded look at what could break: from reliability and integration challenges to incumbent responses.

9. 5–10 Years Out – Looking ahead: the vision of an AI data center built on light

10. Conclusion – Built on Light: the new baseline for the AI network stack.

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1. Introduction

Modern artificial intelligence workloads are placing unprecedented demands on data‑centre interconnects. As training runs scale into the tens of thousands of GPUs and accelerators, network bandwidth, latency, energy consumption and topology flexibility have become gating factors for innovation. Today’s hyperscale clusters routinely wire together 24 000 or even 30 000+ GPUs to train state‑of‑the‑art large language models and other deep‑learning architectures. Each accelerator must exchange petabytes of data per second during operations like all‑reduce or gradient shuffling, yet the underlying network architecture still resembles a multi‑tier Clos of electrical packet switches connected via copper or pluggable optics. Every hop through that fabric requires optical‑to‑electrical‑to‑optical (O‑E‑O) conversion, buffering, queuing and forwarding, adding microseconds of latency and burning hundreds of watts per device.

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The result is a situation where compute keeps getting faster while the network “streets” remain congested. Analysts have likened this to a fleet of race cars stuck in traffic. In many superpods the network alone can consume multiple megawatts and cost $150–$300 million to build. Studies by MIT and Meta showed that a full‑bandwidth Clos network for ~30 000 GPUs would draw about 4.7 MW, and scaling to 65 000 GPUs pushes network power toward 6 MW. At that scale, pluggable optical transceivers can account for 10 % of the total GPU power budget. NVIDIA noted that a hypothetical 400 000‑GPU supercomputer would behave like a 24 MW laser just to feed the optics. Such numbers drive home the reality that yesterday’s copper‑centric interconnects are unsustainable for tomorrow’s AI ambitions.

Given this pressure, a new class of technologies is entering the conversation: optical circuit switching (OCS). Instead of forwarding packets hop‑by‑hop through electrical devices, an optical circuit switch dynamically establishes end‑to‑end circuits using light. Photons travel through fibre with minimal latency and loss; there is no need for intermediate O‑E‑O conversions or store‑and‑forward buffering. In effect, an OCS acts like a gigantic, reconfigurable optical patch panel that can be programmed in microseconds or even nanoseconds to connect any two endpoints.

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Hyperscalers such as Google have already built custom OCS systems for internal TPU supercomputers, demonstrating performance and power advantages. Now a growing cohort of startups is racing to productise photonic switching for the broader market. Advances in silicon photonics, micro‑electromechanical systems (MEMS) mirrors and optical packaging have reduced costs and improved switching speeds by orders of magnitude. A wave of venture capital is following, with more than $300 million invested in OCS‑oriented companies since early 2023.

✨This report investigates why optical circuit switching is emerging now, what macro forces drive adoption, which problems it hopes to solve, and who the leading players are. It analyses market metrics, compares the approaches of six notable startups, examines competitive dynamics, follows where capital is flowing, and offers a strategic lens on how these shifts affect hyperscalers, semiconductor vendors and the wider AI infrastructure ecosystem. It also considers risks and limitations, then looks ahead to how data centres might evolve over the next decade if optical fabrics become mainstream.

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2. Macro Frame: Why Now?

Optical circuit switching is not a new idea; telecom operators have used large optical cross‑connects for decades. However, data centres and high‑performance computing systems historically stuck with electrical Ethernet or InfiniBand networks because optical switches were expensive, slow and complex to operate.

Five converging forces have changed that calculus:

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✨ Rumors is a pattern-recognition layer for investors, founders, and operators who care about where the frontier is heading — before it becomes consensus, before the rest of the market connects the dots.

Discover. Compare. Assess. Stay ahead.

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✨ Inside This Rumor: Cold AI

1. Introduction
   Superconductors shift compute from transistor economics to materials leverage.

2. Macro Frame: Why Now?
   Power budgets, foundry ceilings, and sovereign compute mandates are converging.

3. Framing the Problems
   Power budgets are exploding, precision scaling has stalled, and data centers are choking on inference costs. The bottleneck isn’t AI models—it’s physics.

4. Market Metrics That Matter
   1000× energy efficiency gains. 10–100 GHz logic speeds. Single-digit femtojoule operations.

5. Player Mapping: Five Startups Shaping the Rumor
   Early-stage efforts are converging on core cryo challenges—logic, memory, packaging, and fabrication. Here are five companies tackling distinct stages of cryo-AI acceleration: logic design, EDA tools, memory, and systems integration.

6. The Competitive Edge
   Zero static power. 100 GHz logic. Cold AI flips the energy-performance curve—and the capex model.

7. Follow the Money
   DARPA dollars, hyperscaler interest, and quiet university spinouts all point to a looming cold compute land grab.

8. Strategic Lens
   Cold compute isn’t just a chip story—it’s a new stack, a new datacenter model, and a new control point.

9. Risk Assessment Framework
   Fab constraints, EDA immaturity, cryo logistics, and a limited developer ecosystem.

10. 5–10 Years Out: Strategic Horizon
    If superconducting logic scales, the world’s most powerful inference engines will run at 4 K.

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1. Introduction

In a compute landscape where Moore’s Law has flatlined and energy ceilings are becoming the new cap tables, the next architectural shift isn’t hotter — it’s colder.

The future of high-performance computing may lie at extremely low temperatures, where superconductors and quantum-informed architectures can unlock levels of speed and efficiency far beyond what traditional silicon can deliver.

Over the past 18 months, startups across the U.S., Europe, and Israel have revealed prototype superconducting processors, cryo‑CMOS control chips, and quantum architectures that challenge the dominance of room-temperature computing. Their shared goal is to address the two defining bottlenecks of today’s compute landscape — surging power consumption and limited quantum scalability — by rethinking hardware from the ground up.

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At temperatures near 4 Kelvin (–269 °C), superconducting materials eliminate electrical resistance, enabling logic and interconnects that consume only a fraction of the energy required by standard silicon. In this regime, physical phenomena like Josephson junctions are no longer scientific curiosities — they become practical building blocks for systems offering 100× greater energy efficiency, ultrafast switching, and zero static power draw.

The sections that follow explore why this shift is happening now, what challenges still stand in the way, and how a new generation of companies is shaping this emerging paradigm.

This analysis focuses on five ventures, each addressing a critical constraint within the cryogenic stack — from thermodynamic AI accelerators to superconducting logic, from RF signal acquisition to full quantum–classical system integration.

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What connects them is not hype, but a shared premise: power limits are compute limits. And when GPU racks are projected to draw 600 kW, shifting the performance curve downward in temperature is no longer a theoretical option — it’s becoming an architectural necessity.

We are witnessing the early formation of a new compute stack, and its implications are already material:

• Over 1,400 dilution refrigerators are now installed globally

• Superconducting accelerators are demonstrating 100 GHz logic paths

• Cryo-CMOS transistors have achieved 0.1% of the power draw of their room-temperature counterparts

• Governments in the U.S., EU, and Israel are deploying hundreds of millions in non-dilutive capital into the ecosystem — not as research, but as infrastructure

The teams driving this shift believe that the future of hardware lies not in pushing CMOS further, but in rethinking its physical assumptions entirely. Superconductivity — with its zero-resistance pathways, minimal switching energy, and native compatibility with cold environments — offers a new foundation for logic, memory, and interconnect. Cooling, in this context, becomes a design advantage — not an operational burden.

The ambition is to write a new playbook for computing: one where transistors are augmented or replaced by quantum-compatible devices, and where 4 K becomes a viable operational layer for AI, HPC, and hybrid systems.

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2. Macro Frame: Why Now?

The concept of cryogenic computing and superconducting chips is not new, but a confluence of four forces is turning it from a scientific curiosity into a commercial imperative.

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✨ Rumors is the pattern-recognition feed for investors, founders, and operators who care about where the frontier is heading—before it turns into consensus and before the rest of the market connects the dots.

Discover. Compare. Assess. Stay ahead.

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✨ Inside This Rumor

1. Introduction
   Where Cloud Geophysics Meets Industrial-Grade Capital

2. Macro Frame: Why Now?
   Structural drivers pull discovery upstream

3. Framing the Problems
   High spend, low yield, slow cycles

4. Market Metrics That Matter
   Capex inflation, data deflation, timing pressure

5. Player Mapping: Startups Shaping the Rumor
   Early-stage teams building the new stack

6. The Competitive Edge
   Data flywheels, SaaS margins, royalty optionality

7. Follow the Money
   Capital reallocates upstream, pre-drill

8. Strategic Lens
   SIaaP reshapes control in the mining value chain

9. Risk Assessment Framework
   Model drift, stack friction, IP exposure

10. 5–10 Years Out
    Exploration as an inferential process

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1. Introduction — Where Cloud Geophysics Meets Industrial-Grade Capital

Two trillion dollars of energy-transition capex hinges on copper, lithium, and nickel that—under legacy methods—take fifteen years to move from anomaly to revenue. That lag is now a balance-sheet liability. A cadre of deep-tech startups is turning it into callable upside.

Subsurface Imaging-as-a-Platform, or SIaaP, does for geology what high-frequency data feeds did for equities: it collapses discovery cycles from field seasons to calendar days. Raw magnetics, gravity, and dated core logs are streamed to cloud clusters; deterministic physics kernels constrain neural nets; out comes a fully ranked 3D ore cube that can be queried via REST. The drill plan becomes an output file—not a moonshot guess.

Macro math is merciless. S&P Global projects a 9.9-million-tonne copper deficit by 2035; the IEA pegs lithium demand at 51× 2020 levels by 2040. Even if every shovel-ready project hits schedule—unlikely—supply still trails electrification curves. That spread is an arbitrage window for anyone compressing discovery’s critical path.

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Cost curves are obliging. AWS cut G-series GPU pricing by 45 percent in June, reducing inversion runs from five-figure line items to software-standard COGS. Meanwhile, a sub-$6,500 drone-mounted cesium mag rig now performs the same work once reserved for $160,000 helicopter packages. Add optional sub-1% NSR royalty layers, and SIaaP vendors deliver SaaS margins with commodity convexity—precisely the hybrid exposure pension funds and family offices have been seeking in battery metals, but never quite captured.

Incumbents are responding. Hexagon has stepped up its acquisitions in automation and AI for geoscience applications. Across the drilling sector, there’s growing interest in service models that integrate rig operations with digital data streams. On the policy front, the EU Critical Raw Materials Act and sustained IRA tax incentives are rewarding ESG-aligned projects backed by structured, high-quality data—exactly the kind of outputs next-generation subsurface imaging platforms can deliver from day one.

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This edition of Rumors maps five companies setting the tempo for Subsurface Imaging-as-a-Platform (SIaaP)—a new class of AI- and cloud-native systems reframing mineral exploration as a problem of data, not drilling. The promise: reduced timelines, lower costs, and higher certainty across copper, lithium, and other critical metals.

What follows is a strategic lens on the shift underway—where discovery is no longer measured in meters drilled, but in model accuracy and iteration speed. The analysis outlines how the informational advantage is migrating upstream, and why software-first approaches are beginning to reshape the economics of resource conversion.

Profiles of five emerging teams are presented, alongside relevant market signals, early-stage M&A dynamics, and a framework for assessing which platforms are positioned to become infrastructure—rather than tools.

Let’s drill smarter.

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2. Macro Frame: Why Now?

2.1 Demand Shock for Critical Metals

Copper demand could double by 2035 under energy-transition scenarios; the supply gap hits

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✨ Rumors is a pattern-recognition layer for investors, founders, and operators who care about where the frontier is heading—before it becomes consensus, before the rest of the market connects the dots.

Discover. Compare. Assess. Stay ahead.

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✨ Inside This Rumor:

1. Introduction

2. Macro Frame: Why Now?

   Five converging forces are accelerating the shift toward cloud-native, AI-augmented electronics development.

3. Framing the Problems

   A breakdown of persistent friction points, such as fragmented toolchains, slow iteration cycles, or the growing cost of late-stage errors.

4. Market Metrics That Matter

   Key indicators of structural momentum—across adoption, cost sensitivity, and vertical demand.

5. Player Mapping: Startups Shaping the Rumor

   A close look at the startups unbundling the electronics lab—one bottleneck at a time.

6. The Competitive Edge

   How these platforms differ from incumbents—and why their leverage compounds with usage.

7. Follow the Money

   Where smart capital is placing upstream bets—and what types of infrastructure it’s backing.

8. Strategic Lens

   What this means for deep tech stakeholders.

9. Risk Assessment Framework

   A grounded look at what could break: from verification integrity and IP exposure to interoperability debt.

10. 5–10 Years Out

    Looking ahead and conclusions.

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1. Introduction

Hardware development is undergoing a cloud-powered transformation.

A growing cohort of deep tech startups is virtualizing the core functions of the electronics lab—design, testing, and validation—through cloud-native platforms augmented by artificial intelligence.

These platforms aim to deliver what hardware has long lacked: real-time collaboration, software-like iteration speeds, and programmable access to testing infrastructure. In doing so, they promise to make electronics development more accessible, distributed, and radically faster.

This bottleneck has long been accepted as the cost of doing hardware—especially in chip design and production. But with the convergence of AI and scalable cloud infrastructure, that assumption is now being systematically challenged across multiple layers of the stack. Together, these developments point to a broader paradigm shift: the rise of the electronics lab in the cloud.

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As one founder put it, this could be “the most important innovation in the electronics industry in 50 years”—a leap that could compress a “six-month hardware design process into just two weeks”.

Startups in this space are building toward a shared vision: that hardware should be developed with the same velocity, flexibility, and observability as software.

That writing firmware or designing a board should feel more like coding a web app—complete with instant feedback, version control, and team-wide visibility. And that the lab itself—once physical, local, and opaque—can become an intelligent, cloud-based platform.

2. Macro Frame: Why Now?

The semiconductor industry is at a pivotal inflection point, shaped by five converging forces that are reshaping the landscape of electronics research and development.

2.1 Maturity of Cloud-Native EDA Infrastructure

The Electronic Design Automation (EDA) market is experiencing strong momentum, with projections showing growth from approximately $6.7 billion in 2024 to over $14.2 billion by 2032—representing a compound annual growth rate of 9.8%. Industry leaders such as

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Over the past few months, we’ve spent hundreds of hours mapping Deep Tech capital flows—round by round, week by week...And as we went deeper, something interesting started to emerge.

We began noticing patterns.

At first, it was just a passing observation.

As we immersed ourselves in the details—who’s raising, who’s hiring, where bets are being placed—we began to notice patterns.

Startups that, on the surface, seemed unrelated—until you realize they’re quietly building toward the same market.

Repeat signals across different geographies.

Here and there, a startup would pop up in a space still considered too early.

Another would quietly raise a seed round in the same direction.

Maybe a third would land a strategic hire, or publish a preprint.

Alone, these moments seemed like noise.

But as they repeated, they began to cluster.

It felt like watching micro-markets trying to form

A convergence of technology, capital, and talent that still lies beneath the noise or, as often happens in deep tech, is politely dismissed with gentle skepticism.

At first, we called them rumors—half-jokingly, in internal calls.

Now, we’re sharing them with you.

Welcome to Rumors!

✨ Rumors is an intelligence on emerging micro-trends in deep tech where we follow the money, the people, and the patterns across Deep Tech before they get mainstream.

Each edition is an early lens. A pattern-recognition layer for investors, founders, and operators who care about where the frontier is heading—before the narrative hardens.

It’s for Premium Members who want to sharpen their lens, connect dots faster, and be in the room before the memo goes public.

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In this edition:

In this first edition of Rumors, we dive into a quiet but accelerating movement reshaping the foundations of scientific research: The rise of AI-powered lab automation.

It’s one of those shifts that didn’t happen all at once.

At first, just a few scattered startups—ambitious, maybe a little too early—were trying to bring AI automation into the wet lab.

But then, the signals started stacking up.

Suddenly, capital was flowing.

Researchers were leaving top-tier institutions to join these teams.

Infrastructure was maturing, and for the first time, it felt like the lab itself was becoming programmable.

We’ve seen enough of these signals converge to believe something real is taking shape.

So in this edition, we map the terrain.

Here’s what we cover:

• ⁠Why AI-for-labs is happening now

• Five startups leading this movement, from Boston to Munich

• The capital flowing into the thesis ($250M+ in the last 18 months)

• What could go right (or wrong), and how to think about risk

What you’ll find isn’t just a list of companies.

It’s a window into a pattern: five teams, across geographies and business models, each taking a different shot at the same big idea—

That scientific experimentation can be scaled, accelerated, and made vastly more repeatable through software, robotics, and intelligent orchestration.

Some of these companies are building full-stack platforms.

Others are solving the invisible but critical problems—connectivity, standards, throughput—that make automation possible in the first place.

Together, they sketch the early shape of something larger:

A potential transformation in how R&D is done, funded, and imagined.

✨ This is a free teaser.

You’re reading a short preview of Rumors.

The full edition—complete with startup profiles, funding details, market context, and our own reflections on what this might mean for the future of scientific discovery—is available to Premium Members Only.

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1.⁠ ⁠Macro Frame: Why Now?

So, Why is this happening now? After all, rudimentary robotics and basic lab software have existed for years. Our exploration surfaced a few key catalysts:

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