The idea for this piece came to me as I was thinking back on a conversation I had with a Deep Tech founder.

He was dealing with a very difficult situation. He had to bear production costs, but the margins of his business were very slim.

However, his solution was highly innovative, with a clear competitive advantage and positioned in an extremely critical sector.

At that point, being forced to do something to change course, he decided to try raising the price by an order of 10x.

It surely would have done his margins a lot of good, but would he have kept the customer?

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The result was that the customer did not even try to negotiate downward and accepted.

At that point, the entrepreneur realized, with some regret, that he probably should have asked for much more, given the value of his unique solution, and that he had underestimated the market opportunity for his own product.

It is a story that will certainly sound very familiar to anyone working either as an entrepreneur or as an investor supporting a Deep Tech startup in their portfolio.

Defining a value-based pricing strategy and the right business model is critical.

Not only because it has an impact on the business’s profitability and sustainability, but also because it defines the actual trajectory the startup will follow in terms of growth and returns.

In other words, it also defines whether or not the startup will become eligible for venture capital.

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So I decided to put together a piece that is a little different from the usual, focusing on what I have learned after more than 100 episodes of Deep Tech Catalyst and countless conversations with some of the best investors, entrepreneurs, and operators in the world who work in Deep Tech every day.

I’ll explore some scenarios, models, and strategies with the aim of inspiring and providing new ideas for those who build or support Deep Tech companies in the delicate phase of pricing their solution at the highest possible value.

And, consequently, defining a credible venture case.

The New Bottleneck Economy

To make the case for the concept of “priced by value,” and to offer some points for reflection ahead of a negotiation, I will follow a deliberately provocative line of reasoning based on a simple thesis:

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Surviving the Extremes for Outsized Gains

Electronics usually live in comfortable settings – your climate-controlled server rooms, sleek smartphones, or under a car hood at moderate temperatures.

But what about electronics that must endure hellish heat, bone-rattling vibration, or intense radiation?

These are the “Harsh-environments” often found in space, nuclear reactors, deep underground wells, supersonic flight, and beyond.

Historically, designing hardware for such conditions was a niche, expensive endeavor left to defense contractors and oil giants. Yet today, a quiet revolution is underway. New startups are tackling these brutal conditions head-on, developing sensors, packaging techniques, and materials that laugh in the face of thermal shock, G-forces, and cosmic rays.

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Why now. Reliability is being repriced. Space operators want more on-orbit autonomy; defense and energy buyers are hardening supply chains; geothermal and advanced nuclear are pushing deeper and hotter. These shifts reward components that simply don’t quit.

What changed. The stack matured. Wide-bandgap devices, ceramic/metal packaging, radiation effects testing, and qualification know-how have moved from exotic to deployable. The result: design-in persistence and sticky sockets across programs measured in years.

Where value accrues. It’s not just the chip—it’s materials, interconnects, hermeticity, and certification. Survive heat, vibration, and radiation, and you earn pricing power, long replacement tails, and service/monitoring attach that compound.

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This report shines a light on these opportunities. We’ll explore how extreme-resilience technologies—from heat-proof sensors to radiation-shielded computing—can unlock high-growth markets across space, defense, energy, and more—from wide-bandgap devices to ceramic packaging proven at 200–800 °C; from neutron-tolerant sensors for advanced reactors to radiation-shielded, fault-tolerant compute platforms that bring modern processing to orbit.

Along the way, we profile emerging and established players; we’ll highlight system contexts across space, geothermal, and nuclear; the failure modes that matter—thermal cycling, single-event effects, corrosion, shock—and a few practical reference points: validated operating ranges, radiation and vibration performance, relevant approvals, and program timelines.

1. The Challenge and Opportunity of Hard Environments

Electronics don’t like extremes. Most commercial chips and circuits are specified for a comfortable range—typically up to 85 °C for consumer grade, and perhaps 125 °C for military specification.

They certainly weren’t built to shrug off being baked at 200°C, shaken with 16,000 g’s of force, or bombarded by cosmic radiation. Yet these extremes are exactly what certain industries demand.

Let’s outline the brutal conditions and why traditional electronics fail – and conversely, why solving these challenges presents a unique business opportunity.

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1. From Trash to Treasure in the Tech Metal Age

In a world increasingly driven by deep electrification, the “vitamins of modern industry”—rare earth elements (REEs)—are more critical than ever.

Yet these metals (like neodymium, dysprosium, and europium) come with a catch: today, less than 1% of rare earth materials are recycled from end-of-life products [1].

For decades, it was often cheaper to mine fresh REEs in distant quarries than to recover them from our junked smartphones or spent EV motors. That is now changing rapidly. Investors and policymakers are waking up to a massive inflection point: urban mining of rare earths is moving from niche lab experiments to an emerging industry poised to “join the “billion-dollar club” by 2030 [2].

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Why now?

Several converging forces are at play:

Demand for REEs is skyrocketing thanks to the electrification, compute, and defense buildout – everything from electric-vehicle traction motors to smartphones and advanced defense systems relies on powerful rare-earth magnets and specialized materials.

At the same time, geopolitical tensions and supply chain chokepoints have exposed the vulnerabilities of over-reliance on mined materials. [3].

Recent years have seen export restrictions and trade war threats around rare earths [4], and as we have seen, in this 2025, China imposed export controls on certain rare earth products, causing real supply shocks – Ford even halted production at a Chicago plant due to a magnet shortage when Chinese shipments were delayed [5].

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These events underscored that business as usual (i.e. mining in China and discarding used material) is untenable for tech manufacturers and national security.

Enter rare-earth recycling – once a scientific curiosity, now a strategic imperative. Governments are pouring money and policy support into building a secondary supply. The U.S. Department of Energy announced nearly $1 billion in funding in 2025 to bolster critical minerals mining, processing and recycling domestically [6][7]. The European Union’s new Critical Raw Materials Act, passed in 2023, sets bold targets to recycle 25% of the EU’s rare-earth demand by 2030 [8]. And importantly, innovations in recycling technology are finally making it economically feasible to extract rare earths from discarded electronics, magnets, catalysts and lighting phosphors. The pieces are in place for a recycling revolution.

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For venture investors and deep-tech operators, this inflection point presents a “rare” opportunity.

The global rare earth recycling market is forecast to grow from roughly $0.7 billion in 2024 to $1.0 billion by 2030
[2], with some analyses projecting even higher upside (up to ~$1.5B by 2030) as end-market demand accelerates. [11]

In this report, we will dive into the state of rare-earth recycling and why it’s reaching an inflection point now.

We’ll break down the market dynamics driving growth, compare policy tailwinds, and examine the cutting-edge technologies unlocking efficient REE recovery (from advanced hydrometallurgy and ionic liquids to direct magnet-to-magnet recycling). Moreover, we’ll case some startup players who are pioneering this space – companies turning hard disk drives, EV motors, wind turbine magnets, spent catalysts and phosphor powders into goldmines of critical materials. Finally, we’ll discuss the challenges that still loom (technical, economic, and logistical).

By the end, you’ll have a clear picture of how “urban mining” of rare earths is moving from promise to profit – and practical takeaways on where to seize the opportunity.

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2. Rare Earths: Critical Tech Metals with a Critical Supply Problem

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This second chapter applies each valuation method to representative deep tech startup cases, crafted as analytical simulations that reflect the risk profiles, capital intensity, and time horizons common in the field. The examples span sectors such as orbital manufacturing, geothermal drilling, advanced materials, and next-generation battery chemistries, and are grounded in realistic fundraising narratives — modeled on actual investor behavior, market conditions, and term sheet dynamics.

Each case study demonstrates how structured valuation frameworks can help both founders and investors navigate pricing conversations — before revenue, before traction, and often before the market exists.

Then, we benchmark these simulated scenarios against real-world financings — from Varda Space and Lilac Solutions to Form Energy — to explore how structured logic aligns (or diverges) from valuations seen in practice.

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In short, valuing pre-revenue deep tech is as much narrative as it is numbers.

No formula captures every nuance, but the right frameworks provide structure — they prevent valuation discussions from becoming pure guesswork. They compel clarity: what must go right, what could go wrong, and how risk and potential translate into value.

When asking, “What do you think we’re worth?”, it’s far more compelling to say:

“Our peer-stage baseline is ~$X; we believe we warrant a 1.Y× uplift based on [scorecard factors]; our risk table adjusts ±$Z based on [known advantages or challenges]; and our exit model, with assigned probabilities, suggests an expected return consistent with investor targets.”

That kind of transparency builds trust, builds value, and saves time.

As Commonwealth Fusion’s recent mega-round showed, even early-stage hard tech can command high valuations — but only when both sides have a clear, disciplined view of what’s at stake.

Recap: Five Methods for Pre-Revenue Valuation

Before diving into the case studies, let’s briefly recall the five common pre-revenue valuation methods from Part 1 and when they’re useful. Each methods attempts to bring structure to the inherently subjective task of valuing a startup with no revenue or cash flow:

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So what then?
How do you value a Deep Tech Startup before revenue?

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Valuing a company before revenue is one of the most intricate challenges in venture finance. Without sales or EBITDA, investors must base their decisions on potential, not performance. As Investopedia notes, “for startups with little or no revenue… assigning a valuation is particularly tricky.”

Deep tech startups push this challenge even further. They are often built on radical technologies or entirely novel processes, making reliable comparables virtually impossible to find. A company working on geothermal infrastructure or space-based manufacturing may have no direct competitors, no sales, and no meaningful benchmarks—rendering traditional valuation tools largely ineffective.

Industrial deep tech ventures demand vast capital and lengthy development. They often spend years on R&D and prototype testing before any sales, and may require governmental or strategic partners. This contrasts with a software startup that can quickly iterate and attract early users. As a result, deep tech valuations must account for high technology risk, long timelines, and capital intensity.

For example, a fusion startup like Commonwealth Fusion Systems has raised $1.8 billion without selling any electricity, based on its expected breakthrough impact. Similarly, Varda Space—which aims to manufacture drugs in orbit—raised $145 million by 2024 after only one demonstration mission. These sky-high financings underscore that investors are betting on potential scalability rather than current profits.

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Because revenue projections are so speculative, many traditional methods—such as discounted cash flow (DCF) or market multiples—are unreliable. Instead, investors often use qualitative frameworks that reward strong teams, technology milestones, and large market potential. They may also adjust valuations with risk scores or scenario analysis. All of these approaches are subjective to some degree, but they provide a structured way to anchor a valuation even when hard numbers don’t exist.

The key is to be systematic and transparent about assumptions—an absolute necessity when you’re selling something that, technically, doesn’t exist yet.

If you're preparing to raise Seed or Series A capital—or seeking corporate venture funds, strategic partnerships, or government financing—your valuation framework must reflect the true nature of your industry. It should quantify your current level of technical maturity, define the milestones ahead, and explain how each of those milestones maps to clear value inflection points.

Common Pre-Revenue Valuation Methods

Valuing a company before revenue is, by nature, an imprecise exercise—especially in deep tech, where long timelines, high uncertainty, and minimal market data are the norm.

Over time, a handful of frameworks have emerged to make this process more structured. None are definitive. All are subjective to some degree.

But they offer a way to anchor conversations in logic rather than instinct.

These models blend qualitative assessment—of the team, the technology, the progress made—with lightweight financial structure. Each serves a different use case, depending on the maturity of the startup, the availability of comparables, and the risk appetite of the investor.

Below is a summary of the 5 most commonly used pre-revenue valuation methods in Deep Tech:

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For the last two decades, the industry has been optimized for software—scalable, capital-light, and asset-free. That playbook worked. It minted giants. But the next frontier isn’t digital.

It’s physical.

Right now, a new class of startups is pushing the boundaries of materials science, reshaping aerospace, semiconductors, energy storage, and defense.

Lighter, stronger composites that redefine manufacturing.

Next-gen semiconductors that push AI beyond silicon’s limits.

Battery breakthroughs that eliminate dependence on lithium.

And yet, most VCs are still looking the other way.

The common narrative? Too slow. Too hard to scale. Too different from software.

The reality? Industrial giants, corporate venture arms, and sovereign wealth funds are already moving in.

The next billion-dollar companies are being built right now. The investors who understand how to evaluate, fund, and scale these businesses will gain an edge that few others see coming.

This piece is built on the distilled wisdom of leading investors and founders, combined with deep research from academic and institutional sources. Now, let’s dive into the patterns, tactics, and dynamics that can shape a powerful framework for capturing value in this space.

Questions This Deep Dive Will Answer:

• What are the five essential questions you must ask a materials startup before investing?

• What’s the most common red flag in materials startups that kills venture-scale returns?

• What’s the one metric that actually matters when scaling a materials startup?

• How do you separate deep-tech moonshots from ideas that will never leave the lab?

• What’s inside a top investor’s due diligence checklist for materials startups?

• What’s the hidden bottleneck that prevents most materials startups from reaching scale?

• What are the most likely exit paths for an advanced materials startup?

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The Next $100B Deep Tech Market No One Is Talking About

In the summer of 1994, Jeff Bezos famously set up shop in a small garage in Bellevue, Washington, with some freshly purchased books and a rudimentary internet connection. Back then, the World Wide Web was a curiosity—an academic plaything in the eyes of most investors. Although a small cluster of visionaries saw its potential, the majority didn’t believe it would ever become a mainstream platform, let alone a transformative force in commerce, entertainment, and social interaction.

Fast-forward three decades. Software has become the very foundation of business, with VCs like Sequoia, Benchmark, and Andreessen Horowitz reaping extraordinary returns for backing the digital revolution early on. Their track records shaped the venture landscape, making “software eats the world” an almost unassailable mantra.

Now, imagine the next trillion-dollar disruption is not about lines of code, but about something far more tangible: the manipulation of matter itself.

The DNA of Revolution

Throughout history, each industrial revolution has been catalyzed by materials innovation. The Bronze Age enabled the rise of early civilization. The Iron Age revolutionized warfare, agriculture, and infrastructure. The 20th century belonged to two breakthroughs: plastics and semiconductors. One reshaped manufacturing, the other gave birth to computing—and, eventually, the AI revolution unfolding today.

Yet, in 2025, advanced materials remain one of the most overlooked and undervalued frontiers in venture capital.

While billions flow into AI, and fintech, a quiet transformation is unfolding in deep-tech labs from Zurich to Seattle—one that could redefine industries from aerospace to energy, semiconductors to packaging. Scientists are designing materials at the molecular level, unlocking properties that once lived in the realm of science fiction:

• Composites lighter and stronger than steel, redefining how we build everything from cars to skyscrapers.

• Biodegradable polymers, poised to replace petroleum-based plastics in packaging, textiles, and medicine.

• Next-generation semiconductors, capable of pushing AI performance far beyond the limits of silicon.

I vividly recall a conversation at a conference while I was still a PhD student with a researcher from a European lab who described advanced materials as “the DNA of the next industrial revolution ”

It’s a compelling way to frame the opportunity.

Just as genetic breakthroughs reshaped medicine, materials innovation is rewriting the rules of industrial production, supply chains, and sustainability.

Consider the transition from conventional plastics to plant-based polymers. This isn’t a marginal improvement—it’s a fundamental shift in how we manufacture, use, and dispose of products. The implications stretch across industries, affecting everything from corporate sustainability mandates to global trade flows.

The Tangible Magic of Advanced Materials

The Energy Storage Race

The future of batteries is shifting. Lithium-ion, once untouchable, is hitting its limits. The next wave?

• Solid-State Batteries → Toyota & QuantumScape are betting on safer, lighter cells with 2-3x the energy density.

• Synthetic Graphite → With 75% of graphite refined in China, Novonix is securing U.S. supply for Panasonic & Stellantis.

• Sodium-Ion Breakthroughs → CATL’s new tech could cut EV costs by removing lithium dependency.

The Semiconductor Rebuild

The last three years exposed a brittle chip supply chain. Governments & companies are rewriting the playbook, starting with materials.

• Thintronics → Reinventing dielectric materials for AI chip packaging.

• Mitsubishi + Boston Materials → Pioneering thermal materials to boost chip efficiency.

• The CHIPS Act → Less about "more chips" and more about securing material supply chains beyond Taiwan & China.

Defense: Betting Big on Materials

For the military, materials aren’t a cost—they’re a strategic weapon.

• Stealth & Hypersonics → Carbon-carbon composites survive Mach 5+ heat; radar-absorbing coatings make aircraft vanish.

• Body Armor & Exoskeletons → Graphene plates are lighter yet stronger; soldier exosuits need resilient composites to reduce fatigue.

• Battlefield Energy → Drones, lasers, & targeting systems demand ultra-dense, combat-proof power—solid-state, supercapacitors, lithium-sulfur.

However, VCs seem Shy with Materials Startup…

It’s not about a lack of market potential—far from it. The advanced materials sector is on track to hit $127.28 billion by 2034 according with Precedence Research. The challenge? The traditional venture playbook wasn’t designed for this space. VC thrives on capital-light investments, rapid iteration, and software-driven scalability. Advanced materials, by contrast, demand manufacturing, supply chains, and—most critically—time.

1. The Software-Centric Mindset – Venture capital has been shaped by the success of software: fast scaling, low CapEx, clear network effects. Materials innovation operates on a different timeline—but that doesn’t mean it’s unscalable.

2. The “Hard to Scale” Myth – There’s an assumption that materials breakthroughs take decades to commercialize. Yet, Tesla’s battery advances, SpaceX’s reusable rockets, and AI’s chip evolution all depend on rapid materials innovation.

3. Deep Tech Requires Deep Understanding – Unlike SaaS, where investors can analyze CAC and LTV, materials investing requires a grasp of chemistry, physics, and global supply chains—a skillset that many VCs haven’t developed.

A few months ago, I was speaking with a VC who built his career investing in enterprise SaaS. He had been watching deep tech with growing curiosity but voiced a fundamental concern:

“Scalability is everything in venture. The question isn’t whether materials innovation is important—it’s whether it can scale like software. That’s where it gets tricky.”

He wasn’t wrong. The software-first venture mindset favors high margins, rapid iterations, and asset-light businesses. Advanced materials, on the other hand, demand infrastructure and patience.

But here’s the disconnect: the industries that rely on materials innovation aren’t just growing—they’re fundamentally transforming.

Semiconductors, defense, aerospace, energy storage—these aren’t optional markets. They’re the backbone of the modern world. While traditional VCs hesitate, corporations, governments, and industrial funds are quietly investing billions into next-gen materials. Trillion-dollar industries are being reshaped before our eyes.

But…Strategic Capital Is Already Moving

One of the biggest misconceptions in venture capital is that corporations are slow and reactive, while venture capital firms (VCs) are agile and forward-thinking. Yet, in the case of materials innovation, the reverse seems to be true.

Corporate venture arms—including Applied Ventures (Applied Materials), BASF Venture Capital, NOVA (Saint-Gobain) and Diamond Edge Ventures (Mitsubishi Chemical)—have been consistently deploying capital into advanced materials, while most traditional VC firms remain hesitant.

General Motors is thinking beyond electric vehicles—it’s securing its future supply chain. That’s why GM Ventures co-led a $39 million round in Addionics, a company developing next-generation battery electrodes designed to increase energy density and accelerate charging. It’s also why they’ve backed Forge Nano, a company pioneering atomic layer deposition to enhance battery lifespan. These investments aren’t experimental; they are strategic bets on where the industry is heading.

In semiconductors, Applied Ventures, the investment arm of Applied Materials, is funding foundational technologies that power AI. Their portfolio includes MICROOLED, a company developing ultra-low-power OLED microdisplays, positioning itself at the center of next-generation augmented reality interfaces and AI-powered optics.

Aerospace and defense are undergoing similar shifts. Mitsubishi Chemical’s Diamond Edge Ventures is backing Boston Materials, a startup creating advanced thermal materials for semiconductors, while NOVA Saint-Gobain is supporting FibreCoat, a company that develops thermoplastic-coated fibers for composites and aluminum-coated fibers for shielding applications, such as electromagnetic interference and heat sinks. As AI models grow exponentially more complex, managing heat dissipation and energy efficiency is becoming just as critical as chip performance itself.

Government capital is following a similar trajectory. The CHIPS Act, the Inflation Reduction Act, and Europe’s Green Deal are channeling billions into materials innovation, targeting semiconductor packaging, energy storage, and sustainable manufacturing. The U.S. Department of Commerce has committed $300 million to post-silicon advanced packaging research, while companies like Novonix are securing multi-year supply deals with Volkswagen and Panasonic to help de-risk global battery supply chains.

Why Corporations Are Moving Faster Than VCs?

One of the biggest misconceptions in venture ecosystem is that corporates are slow and reactive, while VCs are agile and forward-thinking. Yet, in the case of materials innovation, the reverse seems to be true.

This isn’t happening by accident. Large industrial firms have structural advantages that make materials investments more viable for them:

1. They operate on longer time horizons. Unlike venture funds with fixed 10-year cycles, corporates can invest in technology that may take years to commercialize—because they’re thinking about long-term strategic advantage, not just financial returns.

2. They have built-in go-to-market pathways. A startup developing a next-generation polymer doesn’t need to build an entirely new market from scratch if it partners with a corporate that already has manufacturing scale. That’s why corporate-backed materials startups often have a faster path to commercialization than their deep tech peers in other fields.

3. They understand regulatory tailwinds. Whether it’s Europe’s Green Deal, the Inflation Reduction Act in the U.S., or China’s aggressive industrial policies, corporates are aligning their investments with regulatory shifts that are already reshaping industries. Many of the world’s largest firms—from automotive to consumer goods—will be required to use sustainable materials in the coming decade, giving early movers a built-in advantage.

How to Build (and Back) the Next Advanced Materials Winner

Alright, let’s get to it—how do you capture value in advanced materials? How do you spot the winners? And more importantly, how do you help them scale?

In 2011, Marc Andreessen famously said, “Software is eating the world.” Thirteen years later, venture capital still revolves around that idea—trained to evaluate SaaS deals through CAC, LTV, and ARR like it’s scripture.

And yet… despite the fact that advanced materials are quietly reshaping everything from EV batteries to aerospace to defense, the venture playbook for this space is still, well… all over the place.

There’s no repeatable formula like in software. No “just check net dollar retention” or “make sure the LTV/CAC ratio holds up.” That’s because raw materials, manufacturing processes, supply chain bottlenecks, and regulatory hurdles don’t fit neatly into a pitch deck slide.

Instead, assessing these companies feels more like detective work—piecing together regulatory signals, analyzing pilot projects in labs, understanding the nuanced needs of global manufacturers, and, of course, embracing pivots… multiple times.

If you’re stepping into advanced materials—especially for the first time—the landscape can feel like uncharted territory. You might find yourself asking:

• How do I evaluate a startup’s scientific claims?

• Who sets industrial standards, and how long does approval take?

• What does traction even look like in a materials context?

This piece is built on the distilled wisdom of leading investors and founders, combined with deep research from academic and institutional sources. Despite the diverse nature of advanced materials, common patterns, tactics, and dynamics emerge that can shape a powerful framework for capturing value in this space.

Let’s explore them together!

Have the founders done their homework?

Many advanced materials startups are born in university labs. Founders (often scientists themselves) assume that if their discovery is impressive enough, the market will beat a path to their door. But real-world traction doesn’t come from an elegant scientific paper; it comes from industrial partners who commit budgets to pilot production and scale-up.

The “Technology Push” Trap

It’s easy to get lost in the details of molecular structures or thermal degradation curves. Investors and corporate buyers can't all be chemists! Moreover, they care about what your technology does—can it lower weight, reduce cost, or solve an environmental headache?

Ok, there is a polymer that tolerates 2,000°F, is there a founder able to go beyond the exotic chemistry and articulate how the solution will enable new possibilities in hypersonic flight or advanced manufacturing? Where does he base his assumptions?

Deep Tech Without Deep Science: The 3 Questions That Matter

While advanced materials involve complex science, successful founders and investors focus on strategic, not technical, questions:

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Why? Because that’s where thermodynamic and logistical bottlenecks actually get resolved—while digital systems can only model them. That’s where you get paid big.

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In a world trained to chase frictionless scale, the deepest economic leverage now lies where matter pushes back—at the point where atoms shift state and physical constraints get rewritten.

We call this the phase-change margin: the economic asymmetry created when a small materials breakthrough unlocks a massive system-level gain—lower thermal loss, higher voltage, wider bandwidth, or tighter form factor.

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This isn’t margin earned through branding or UX. It’s margin enforced by physics.

A sliver of silicon carbide cuts inverter losses and lets EVs shed copper and coolant mass. A thin layer of GaN replaces entire power stages, delivering energy savings no SaaS update can match. A 100-micron aluminosilicate sheet collapses two manufacturing steps into one—bending like plastic, sealing like quartz. These aren’t materials. They’re architectures for margin.

When a material solves a physical bottleneck—heat, latency, energy loss, or yield—it doesn’t just earn a markup. It reshapes the profit pool. And even as unit prices fall, the leverage holds. That’s the power law of matter: small input → system-wide repricing → durable edge.

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Yet most investors still miss it—applying SaaS-style metrics to physics-based businesses. They forget: software scales by replication. Materials scale by irreversibility. Once designed in, they don’t just get used. They get locked in.

Markets don’t reward novelty alone. They reward scarcity, anchored in necessity.

A photonic chiplet or thermal interface material might only show mid-double-digit gross margins. But if it drops latency by 10× or knocks 5°C off a 700W GPU, it unlocks value far beyond what its BOM line suggests.

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This comprehensive analysis delves into how advanced materials bend system economics, embed physical lock-in, and generate asymmetric upside by aligning financial structure with thermodynamic leverage.

Each chapter examines a domain—from photonics to thermal packaging to fusion energy—to show why, when it comes to margins and moats, atoms can eat bits.

Covered Sections:

Section 1 — From Margins to Moats: The Capital Geometry of Advanced Materials
Section 2 — The Physics of Lock-In
Section 3 — The Filiera Stack: Where Materials Collapse Traditional Economics
Section 4 — From Unit Margin to Infrastructure Leverage
Section 5 — The Compounding Triangle: IP × Yield × Scale in Deep Tech
Section 6 — Trading Spreads, Not Spots
Section 7 — Energy Spread: Fusion PPAs vs. Gas Curves
Section 8 — Carbon Spread: Negative Intensity Premium
Section 9 — Portfolio Construction: Commodity Corridor Clauses

By the end of this analysis, readers will gain a structured understanding of how materials-based businesses unlock margin and durability not through scale alone, but by relieving physical bottlenecks that define entire system cost curves.

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This is not a theoretical exploration of materials science. It’s a capital-first framework for recognizing when atomic interventions become strategic choke points—where financial optionality compounds from thermodynamic necessity.

Whether you’re deploying into energy, compute, carbon, or mobility, this analysis is designed to help you distinguish between commodity inputs and margin-controlling components—between materials that sell by the kilo and those that reprice the downstream stack—and to understand why an advanced materials startup should often be evaluated through Commodity Logic, not SaaS Logic.

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Section 1 — From Margins to Moats: The Capital Geometry of Advanced Materials

Advanced materials don’t win on raw unit economics; they win by deforming the cost and performance geometry of entire systems. A breakthrough material may be only a minor line item in a bill of materials, but it can bend an S-curve in

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DeepSeek—a small, unheralded Chinese AI startup—has torn through the conventional logic that only mega-funded ventures can build state-of-the-art language models.

This article unpacks how DeepSeek’s “$6 million AI” rattled trillion-dollar markets and threw a spotlight on open-source innovation, sparking questions about whether Western AI juggernauts have been overspending.

ou’ll see why capital efficiency is newly paramount, how GPU giants like Nvidia may remain winners despite short-term shocks, and why data governance concerns run rampant when a Chinese-upstart's product is suddenly in global demand. We’ll also examine the prospects of “down rounds” among leading AI unicorns, the ripple effects on big tech’s data center spree, and whether a Sino-centric approach to AI might reshape the competitive landscape.

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Table of Contents

1. Prologue: When a $6 Million AI Disrupted Trillion-Dollar Markets

2. Revisiting the AI Arms Race: The Long-Standing Playbook
   2.1. A Self-Reinforcing Feedback Loop
   2.2. Tipping Toward Consolidation?

3. DeepSeek: The Genesis of a Surprise Disruptor

4. Why It Matters: Unraveling the Core Disruption
   4.1. Shaking the AI Cost Structure

5. Implications for Deep Tech AI Valuations
   5.1. The Overbuilt vs. The Lean and Mean
   5.2. Potential Down Rounds: Repricing the AI ‘Unicorns’
   5.3. A Fresh Look at the Funding Pipeline

6. Who Wins, Who Loses? The Shifting Value Chain
   6.1. Foundational Model Builders and GPU Giants
   6.2. The Middleware/Infrastructure Layer
   6.3. Application-Focused Startups

7. Censorship, Data Governance, and the China Question
   7.1. A Walled Garden?
   7.2. Privacy and Policy Risk

8. Could DeepSeek Expand West?
   8.1. Regulatory Snags and Geopolitical Tensions
   8.2. The “TikTok Syndrome” and User Trust

9. Knock-On Effects: Rethinking AI Infrastructure and Spending
   9.1. The Jevons Paradox for AI
   9.2. Data Center Build-Out or Build-Down?

10. Investor Sentiment: Surveys and Anecdotes

11. Future Scenarios: The Next 24 Months

12. Conclusion

1. Prologue: When a $6 Million AI Disrupted Trillion-Dollar Markets

Few events have rocked the global tech and venture capital scene like the recent emergence of DeepSeek, a Chinese AI startup that seemingly came out of nowhere to introduce a large language model—dubbed R1—with a training cost that looks laughably low compared to the budgets behind Western AI titans such as OpenAI or Anthropic. In a single Monday of trading, shares of multiple high-flying AI and chip companies collapsed; Nvidia alone shed over $500 billionin market cap, wiping out more value in one day than any company in U.S. history.

Hedge funds, retail investors, and even everyday employees in the Bay Area were left scratching their heads: How could such a tiny startup from Hangzhou—funded almost exclusively by a Chinese hedge fund—rock the very foundation of the AI industry, an industry whose conventional wisdom is that more capital, more parameters, and more GPUs are the path to success? Skeptics insisted that DeepSeek must have exploited or “distilled” existing Western models, or that it was aided by undisclosed sources of computational power. Still others posited that the new Chinese model was the result of an elaborate disinformation campaign, a Trojan horse, or both. Yet actual usage tests, from coding tasks to advanced knowledge queries, told a consistent story: DeepSeek R1 was competitive or better in many tasks that previously required ultra-expensive training resources.

In that instant, what had seemed impossible—achieving state-of-the-art generative and reasoning performance at a fraction of the known cost—became real. Investors recognized that the multi-billion-dollar war chests stockpiled by top AI startups could be called into question. Meanwhile, a wave of self-doubt overtook executives at Google, Microsoft, Meta, and dozens of well-capitalized AI companies: If DeepSeek truly pulled this off, what does it say about our own cost structures, our valuations, and the size of the moat we believed we had?

This piece aims to dissect the DeepSeek phenomenon—exploring its technological roots, the strategic bombshell it delivered, and how it might permanently alter the financing landscape for deep tech AI startups.

While some hail DeepSeek as a “Sputnik moment” that will spur more innovation, others see it as a force that may dramatically shrink the once-dominant moats of leading AI players. That looming sense of disruption raises a myriad of questions: Will we see down rounds among top-tier AI darlings? How might open-source, low-cost AI thrive under potential censorship constraints from a China-based developer? And could we be heading for a multi-billion-dollar recalibration of the entire AI value chain?

We are about to examine all of those angles—some purely pragmatic, others deeply existential for venture capital.

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2. Revisiting the AI Arms Race: The Long-Standing Playbook

To understand why DeepSeek’s “$6 million AI” is so disruptive, we need to recall what has been the going orthodoxy in AI:

1. Compute is KingFor the past few years, everyone has presumed that building state-of-the-art models requires access to the largest GPU clusters—predominantly Nvidia’s H100 or A100 chips. Training GPT-4, for instance, was rumored to cost anywhere from $100 million to $600 million on specialized hardware. A few insiders claimed it could have cost even more.

2. Scale, Scale, ScaleThe assumption that bigger is better reigned supreme. Model parameters were doubling, if not tripling, annually. Startups (Anthropic, xAI, Cohere, etc.) each raised billions under the logic that the path to advanced AI demanded large-scale compute outlays and massive data sets.

3. Closed Source, Premium PricingOnly well-funded organizations—be they Big Tech or heavily capitalized startups—could afford to train large language models. Those that succeeded commanded premium pricing. We saw usage fees from OpenAI or Anthropic hitting up to $100 (or more) per million tokens, a cost structure that has shaped how most enterprise AI budgets are allocated.

Under these rules, the entire AI landscape reflected a near-inevitability: if you didn’t have the capital to buy or rent staggering amounts of GPU time, you were out of the game.

Venture capitalists bet heavily on this premise, funneling more than $150 billion into AI companies in just the last two years—often at lofty valuations that soared to $30, $50, or even $100 billion for “foundational model” builders. A handful of deep-pocketed players, mostly in the U.S., dominated the field.

Then, in a single stroke, DeepSeek has apparently subverted this entire approach. If a Chinese startup can train a model that is roughly on par with GPT-4 or Claude—at least in certain benchmarks—without raising billions in capital, what does that say about the fundamental cost structure and valuations for AI?

2.1. A Self-Reinforcing Feedback Loop

This arms race wasn’t just about vanity. The prevailing view was that large-scale generative models would form the “platform layer” of the next computing era, akin to operating systems or internet protocols. You had to stake out territory early, accumulate GPUs, and hire the top deep learning PhDs before your rivals, or risk irrelevance. This belief fed into a cycle of:

• Mega Rounds: VC firms—fearing missing out on the next big platform—offered eye-watering checks, often in the billions.

• Headline Partnerships: Tech giants like Microsoft, Amazon, and Oracle joined or led these rounds, tying their cloud services to the success of these LLM developers.

• Astronomical Valuations: Because no one wanted to “miss the next Microsoft,” valuations soared, sometimes exceeding $150 billion for pre-IPO companies like OpenAI.

Beyond the big names, smaller AI ventures also thrived on the premise that they would eventually require major capital to train specialized models or deliver advanced analytics. In almost every pitch deck, references to the escalating cost of training were used to justify a “spend big or fall behind” mentality.

2.2. Tipping Toward Consolidation?

With the cost so high, many predicted that the AI market would naturally consolidate around a handful of super-funded players. Contrarians who believed in cheaper or more efficient AI were typically dismissed: “We have a 3+ year lead, we have billions in the bank, good luck catching up.” That is how the story sounded—until DeepSeek challenged that entire assumption practically overnight.

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3. DeepSeek: The Genesis of a Surprise Disruptor

DeepSeek’s creation story might be the stuff of legend if it proves out.

DeepSeek emerged from the AI division of Zhejiang High-Flyer Asset Management, a Hangzhou-based hedge fund founded by Liang Wenfeng. On paper, it looks more like an in-house research project than a potential rival to America’s best-funded labs.

Liang himself is a quant trader who recognized the power of large-scale language models for trading signals and risk analysis. Sometime in mid-2023, he decided to spin off the AI group as an independent startup—keeping sole ownership within the hedge fund.

Crucially, DeepSeek had a hidden advantage: it anticipated U.S. export restrictions early on and amassed around 10,000 older-generation Nvidia A100 chips. These might not be the glitziest GPUs on the market, but they have proven more than sufficient for training large models if used with the right optimizations.

At the same time, DeepSeek tapped fresh PhD talent from top Chinese universities, who were willing to experiment with open-source code from Western models like Meta’s Llama and push the boundaries of GPU efficiency.

After releasing smaller open-source models (V2, V3) through 2024, DeepSeek made a giant leap this January with R1, a model specifically targeting reasoning abilities.

The biggest bombshell: The entire training budget apparently hovered around $5.6 million. Even if that figure excludes overhead costs for prior experimentation or hardware outlays, it remains dramatically lower than the accepted norms.

Within days, R1 soared on Chatbot Arena, beating or matching heavyweight models from OpenAI and Google on certain tasks. Marc Andreessen called it AI’s “Sputnik moment.” Tech stocks trembled. And the entire venture ecosystem started to ask: “Wait, have we been massively overpaying for AI?”

Where did this figure come from? The short answer: DeepSeek claims to have meticulously tracked the main production run for R1, excluding “non-essential experiments.” They mention that 2,000 A100 chips were used concurrently over a period of about 4 weeks (equivalent to 224,000 GPU hours).

4. Why It Matters: Unraveling the Core Disruption

One might ask, “What’s the big deal if a Chinese startup built a GPT-4 competitor on the cheap?” Possibly:

• It Exposes Efficiency Overkill: If DeepSeek truly approached GPT-4-level performance on older GPUs with clever optimization, it suggests the Western approach of ‘throw more money at the problem’ may be outdated.

• It Undercuts the Dominant Moat: The main moat for big LLM developers has been cost: a $500 million or $1 billion barrier to entry. If that number is closer to $5 million or even $30 million, then the entry barrier plunges. That alone transforms the competitive landscape.

• It Encourages Open-Source Momentum: R1 is open-sourced. That potentially allows thousands of smaller developers—and even some mid-tier companies—to adapt it for specialized tasks at minimal expense. In other words, the code is out there, and it’s cheaper to run.

It Signals a Chinese Breakthrough: For years, the U.S. believed it was at least a year or two ahead in advanced AI. DeepSeek’s performance, plus its short time-to-market, suggests the gap may not exist—or at least it’s rapidly narrowing.

4.1. Shaking the AI Cost Structure

Consider the real-world impact if AI training costs plunge from $100 million per model to $10 million. That 90% cost reduction would open the floodgates for dozens, if not hundreds, of new AI labs and startups. Instead of requiring a mega-round at a $10–$50 billion valuation to train and deploy advanced models, you might do it at a much more modest scale.

In that scenario, can you truly charge $40, $50, or $100 for a million tokens of inference? Possibly not. Price competition, especially in the face of open-source solutions, will push costs downward. This is terrifying for incumbents that pinned their entire revenue projections on ultra-premium pricing for their generative AI APIs.

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5. Implications for Deep Tech AI Valuations

From a venture capital perspective, the consequences loom large. The billions thrown at the largest LLM developers were predicated on massive capital-intensity and an unassailable moat. One question overshadows the rest:

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Building factories isn’t just about securing funding—it’s about navigating a maze of permits and compliance requirements. Shipping a single piece of hardware across borders means grappling with the regulations of every country involved, from safety standards to packaging rules.

And let’s not forget: these technologies and companies aren’t just businesses; they’re strategic assets for nations. That makes them uniquely vulnerable to the accelerating—or potentially devastating—impact of new governments, geopolitical tensions, and shifting alliances.

The question isn’t whether you’ll adapt, but how quickly. Can you see the risks ahead and turn them into opportunities? Whether you're a founder, investor, or executive, understanding these dynamics isn’t optional—it’s the difference between thriving and fading into irrelevance.

That’s why having a clear perspective isn’t just helpful—it’s essential.

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The global competition for energy and tech dominance is turning into a high-stakes spectacle, and the last week provided more than enough drama to keep observers riveted. In the U.S., President Donald Trump hit the brakes on offshore and onshore wind projects with an executive order that’s sparked fierce debate.

Critics call it a setback for clean energy progress, while supporters argue that wind power, with its intermittency and reliance on subsidies, warrants a harder look. Whatever the reasoning, the move sends a clear signal: fossil fuels are back in favor.

But the real fireworks came a day later when Trump announced the $500 billion “Stargate” initiative, a massive AI venture involving OpenAI, SoftBank, and Oracle. Stargate promises to build up to 20 massive data centers, with the first $100 billion supposedly ready to roll. Yet financing the project is shaping up to be as ambitious as the venture itself. While OpenAI, SoftBank, and Oracle are major players, their financial contributions seem limited given the scale of the endeavor. Oracle, for instance, has $10.9 billion in cash but also $90 billion in debt—not exactly the war chest required to bankroll a project of this magnitude.

Across the Atlantic, the European Union is taking a different tack. The forthcoming “Competitiveness Compass” is a five-year plan to boost the bloc’s industrial capabilities and deep tech innovation. It includes shared supercomputing hubs and measures to cut energy costs, all part of an effort to keep up with the U.S. and China. The ambition is clear, but the funding? Not so much. With an estimated €800 billion needed annually, Europe’s roadmap risks being more a wish list than game plan.

Meanwhile, Italy stole the spotlight in Europe with its announcement to revive nuclear energy by 2027. The plan focuses on small modular reactors (SMRs), which offer a flexible, low-carbon alternative to complement renewables. It’s a bold move for a country that once turned its back on nuclear, but public skepticism and hefty costs remain significant hurdles.

Table of Contents

1. The European Union’s Competitiveness Compass

2. A U.S. Pivot: Trump’s Executive Orders and Energy Strategy

3. Trump, Tariffs, and the Prospect of an External Revenue Service

4. Clean Energy in Limbo: Offshore Wind in the Crosshairs

5. Clean Tech, Fossil Fuels, and the European Response

6. Italy’s Nuclear Resurgence

7. Stark Contrasts: National Energy Emergencies and the Path Forward

8. Global Competition for Deep Tech: Europe’s Emerging Initiatives

9. Defense, National Labs, and DOE Appointments

10. Evolving Supply Chains and Industrial Policy

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1. The European Union’s Competitiveness Compass

In Brussels, quiet corridors within the European Commission are reportedly buzzing with a sense of purpose. A new draft strategy, set to be released by the end of January, promises a roadmap for the next five years that aims to keep the bloc technologically competitive against the United States and China (according to Bloomberg). Aptly named the Competitiveness Compass, this initiative builds on a crucial premise: that Europe’s economic resilience is tied to its ability to innovate in advanced fields like artificial intelligence, cleantech, robotics, cloud computing, quantum research, and other “deep tech” domains.

Though the official text remains a draft, leaks indicate four primary targets. First, the EU hopes to address regulatory bottlenecks so that emerging technologies can move from lab to market more smoothly. Second, it wants to make funding streams more accessible, especially for smaller and mid-sized enterprises—those 30,000 or so companies that, as currently defined, miss out on the benefits that smaller enterprises receive under EU guidelines. Third, the strategy pledges to reduce energy costs in order to keep manufacturing and research from drifting overseas. Fourth, it looks to reduce dependencies in critical raw materials, an area of strategic sensitivity as global supply chains become more fragmented.

At the heart of the Competitiveness Compass is a proposed body modeled after the famed U.S. Advanced Research Projects Agency (ARPA) framework. The EU envisions that this new advanced research agency will channel resources into technologies deemed vital for Europe’s economic and security interests—whether that means specialized AI chips, innovative battery prototypes, or robotics platforms. The impetus for this stems from a recognition that the region remains reliant on foreign suppliers for many key components, from microelectronics to specialized software for next-generation computing.

Yet, even as the EU sets forth these ambitious plans, one thorny question looms: how to fund them. Former European Central Bank President Mario Draghi, who led a high-profile commission on competitiveness, estimated that anywhere from €600 billion to €800 billion in annual public and private investments might be necessary for Europe to reach its mid-century sustainability and technology goals. While the strategy references these financial needs, no single mechanism for raising such sums has yet emerged.

Potential avenues include expanding the EU’s next multi-year budget, forging new public-private partnerships, or engineering sovereign wealth vehicles dedicated to deep tech.

Funding aside, the Commission’s plan addresses another concern: supercomputing infrastructure. By proposing “mega AI factories,” the EU aims to offer startups and research institutions a shared resource for training large models, a process that grows more computationally intensive each year.

Officials believe this aggregated supercomputing capacity will help Europe catch up in an environment where the U.S. and China frequently outspend and outcompute their rivals. The Commission has already launched a €1.5 billion initiative to establish AI facilities, with seven such centers planned across member states. The new plan goes further by envisioning a pooled resource that harnesses the collective horsepower of Europe’s national HPC (high-performance computing) centers. Whether this will be enough to level the playing field remains to be seen.

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2. A U.S. Pivot: Trump’s Executive Orders and Energy Strategy

While Brussels refines its 2030 agenda, Washington has rattled global markets with an entirely different form of strategic recalibration. On just his second day back in office, President Donald Trump signed a series of executive orders that could significantly reconfigure American energy policy.

Among them is a directive temporarily halting new offshore wind lease sales in federal waters, pausing approvals for onshore wind as well, and reviewing permitting practices for renewable projects. According to the text of the executive order, the administration intends to investigate environmental impacts on wildlife, assess the intermittency costs associated with wind energy, and weigh the effect of subsidies on the industry’s long-term viability.

Simultaneously, the president orchestrated a $ billion private-sector investment in AI infrastructure, featuring a venture codenamed “Stargate,” backed by OpenAI, SoftBank, and Oracle. Days later, White House officials clarified that the total capital committed could eventually reach $500 billion, with substantial funds going to data center construction and job creation. Indeed, Oracle cofounder Larry Ellison and SoftBank’s Masayoshi Son publicly credited Trump’s intervention for accelerating the venture. The rationale behind this pivot appears twofold: continue fueling the momentum of AI innovation—deemed crucial for national competitiveness—while tightening the grip on conventional energy to shore up domestic resources and reduce costs.

Critics argue that the administration’s move to freeze wind leases and slash green energy subsidies undermines climate objectives, including some that had garnered bipartisan support. Proponents counter that an all-out emphasis on fossil fuels strengthens national security by lowering energy costs, expanding exports (particularly liquefied natural gas), and generating government revenues. According to the North American Electric Reliability Corporation, the U.S. grid faces rising demand from data centers and other power-hungry users. The Trump team contends that more domestic drilling, combined with relaxed regulations, can accommodate these needs.

Yet, confusion about the exact mechanics of the policy remains. The White House has referenced the possibility of establishing an “External Revenue Service” to collect tariffs and duties, especially from partners like Canada or the European Union. For many in the private sector, the worry is that such moves may inflate import costs for everything from specialty minerals to advanced components. As a result, industry groups are urging caution, hoping for a narrower and more strategic set of tariffs aligned with national security or critical infrastructure, rather than blanket measures. In the near term, these conflicting impulses—pro-AI spending, anti-wind licensing, and prospective tariff expansions—create a dynamic tapestry of risk and opportunity.

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3. Trump, Tariffs, and the Prospect of an External Revenue Service

Historically, the Trump administration’s trade posture has embraced tariffs as a tool for restructuring trade balances. The White House’s recent announcement of an “America First Trade Policy” memorandum calls for investigating the drivers of the large U.S. goods trade deficit. If deemed necessary, officials suggest they may pursue global supplemental tariffs or propose an overhauled system for collecting import taxes, possibly culminating in this so-called External Revenue Service. While Customs and Border Protection (CBP) already handles tariff collection, some observers interpret this new entity as a rebranding exercise that might concentrate more policy-making power in the executive branch.

A senior fellow at the Peterson Institute for International Economics, Jeff Schott, highlights that the administration’s posture toward Mexico and Canada—including the threat of a 25% tariff—could complicate the updated US-Mexico-Canada Agreement (USMCA). Others caution that unilateral tariff hikes could heighten inflationary pressures on raw materials and consumer goods.

The Consumer Brands Association, for instance, argues that imposing levies on commodities like coffee beans, spices, or other items with limited domestic production does little to advance strategic aims and may simply raise prices.

Meanwhile, Washington’s approach to China remains a question mark. During Trump’s first term, escalations with Beijing led to reciprocal tariffs on billions of dollars’ worth of imports. The new policy calls for reexamining trade agreements with China, referencing the possibility of more targeted duties. At stake are supply chains that feed into advanced manufacturing, AI hardware, and consumer electronics.

4. Clean Energy in Limbo: Offshore Wind in the Crosshairs

Another major storyline involves the future of clean energy, specifically offshore wind.

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In the race toward global electrification, optimism has long fueled ambitions.

Yet, recent developments suggest it may be time to question assumptions about how this journey will unfold.

The financial troubles of Northvolt, a European battery manufacturing champion, and the policy pivot signaled by President Trump’s recent energy-focused inaugural address underscore a new reality: the road to a green future is not without detours, speed bumps, and recalibrations.

Northvolt’s Chapter 11 filing in late 2024—a bid to reorganize debt while preserving its ambitious battery manufacturing goals—highlights the financial and operational pressures mounting within the EV ecosystem.

At the same time, Trump’s promise to roll back EV mandates and refocus on fossil fuels has introduced uncertainty into a market long driven by government incentives.

These concurrent shifts prompt reflection: Are the foundations supporting the global push for electrification as stable as they seem? Or is it time to rethink the roadmap?

What to expect

• The inside story of Northvolt’s rise, challenges, and Chapter 11 filing.

• How Trump’s energy agenda could reshape the global EV landscape.

• Why Europe’s green industrial strategy is at a critical crossroads.

• How China’s dominance in battery production is reshaping competition.

• Strategic insights to navigate the shifting future of electrification.

The challenges facing Northvolt and the broader EV ecosystem underscore the importance of strategic foresight and adaptation.

The twists and turns of this story remind us: progress is never linear. But for those prepared to adapt, opportunities abound.

1. Introduction

Electric vehicles (EVs) have long been seen as a cornerstone of the global push toward sustainable energy. Northvolt, a Swedish battery manufacturer founded in 2016 by former Tesla executives, was once positioned as Europe’s leading hope for domestic battery production. Backed by major automotive clients and substantial venture capital, the company symbolized Europe’s potential to compete in a market historically dominated by Asia and the United States.

However, in late 2024, Northvolt announced it had filed for Chapter 11 reorganization in the United States, as stated in its press release on November 21, 2024. This development highlighted the growing financial and operational challengesfacing the battery production industry.

Meanwhile, President Trump signaled a significant shift in U.S. energy policy during his inauguration speech on January 20, 2025. With promises to “reclaim our sovereignty” and an “America First” the Trump administration’s inaugural policy signals suggest a reevaluation of government-backed incentives that had previously spurred U.S. demand for EVs.

These concurrent developments underscore critical questions about how government policy, market dynamics, and industrial strategies align—or fail to align—in shaping the trajectory of alternative-power vehicle manufacturing. At stake is more than the future of a single Swedish company; it is a broader question of how energy priorities, industrial policy, and capital markets converge to define the next phase of global mobility.

The immediate question is whether the Trump administration’s focus on traditional energy sources and its apparent retreat from pro-EV measures might exacerbate the challenges faced by companies like Northvolt.

A further question is whether these policy changes foreshadow broader difficulties for Europe’s ambitions in the battery sector.

Northvolt, in its November 2024 statement, described the Chapter 11 filing as a step to “restructure its debt, adjust operations to meet current customer needs, and ensure a sustainable foundation for continued growth.”

The company’s efforts, filed with the U.S. Bankruptcy Court for the Southern District of Texas, aim to protect its flagship Northvolt Ett facility in Skellefteå, Sweden, while maintaining commitments to employees and clients. However, the success of this strategy depends heavily on the strength of global and U.S. EV demand—a factor now clouded by shifting energy policies.

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2. Trump’s Speech Highlights: A Rolling Back of EV Support

President Trump’s inaugural address on January 20, 2025, marked a potential turning point in U.S. energy policy. His pledge to “bring prices down” through expanded domestic drilling and “save our auto industry” by ending federal mandates for electric vehicles signaled a decisive shift in focus.

By declaring his administration’s intent to “revoke the electric vehicle mandate”, Trump has introduced new uncertainty into a market that has long relied on consistent policy support.

For over a decade, the United States has played a pivotal role in advancing global electric vehicle (EV) adoption.

Federal tax credits,

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Introduction

In the journey of biological sciences, progress has traditionally moved at a methodical pace, tackling broad and complex questions. This changed dramatically with the groundbreaking work by James Watson and Francis Crick in delineating the intricate structure of DNA. This discovery not only revolutionized our understanding but also opened up a new era of rapid scientific developments.

Approximately two decades later, the field witnessed another leap forward with Herbert Boyer and Stanley Cohen's introduction of recombinant DNA technology, an advancement that further expanded the toolkit available for genetic research.

By 2003, the Human Genome Project was completed, and scientists were already

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The chemical industry is a pivotal component of the global economy, impacting various sectors from pharmaceutical production to materials science. The advent of artificial intelligence (AI) is poised to trigger a revolution in this sector, enhancing efficiency and productivity while reducing costs.

Forecasts suggest that by 2032, the market value of AI in the chemical sector could reach a market size of $17.9 billion, with a compound annual growth rate (CAGR) of 31.2% from 2023 to 2032.

Key drivers of this expansion include the demand for efficient and sustainable chemical production processes, advancements in machine learning algorithms and computing power, the growing need for predictive maintenance, and real-time process optimization. The increased focus on the research and development of new materials and formulations, along with stringent regulatory requirements necessitating compliance solutions, are additional factors contributing to this growth.

In the chemical domain, generative AI is employed in a variety of applications, from molecular modeling to virtual screening, process optimization to predictive maintenance, all the way to supply chain management. By analyzing vast datasets and performing complex calculations, AI algorithms can uncover patterns and correlations that would otherwise remain unnoticed, leading to the discovery of new materials, formulations, and processes.

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During the Covid-19 pandemic, for instance, AI proved crucial for studying the structure of the coronavirus, its lifecycle, and infection pathways in much shorter timescales than would have been possible otherwise.

As technology matures, an increasing number of chemical companies are adopting AI-based solutions to maintain a competitive edge, accelerate research, and create more sustainable products and processes. Additionally, a recent survey conducted by IBM revealed that over 80% of chemical industry executives anticipate AI will be important to the success of their business in the next three years.

For instance, Hitachi has developed an artificial intelligence capable of identifying the key components for developing new materials.

With access to a database containing over 100 million records, this technology significantly accelerates the material development process, reducing the time and costs associated with research and development. For example,

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Today, we're delving into the transformative world of steel production, a sector at the crossroads of innovation and sustainability. We're focusing on how deep tech startups are revolutionizing this industry to meet the rising demands for green steel.

Let’s start with a quote to size the importance of steel today:

Steel is more than just a material; it's the backbone of modern civilization, shaping our buildings, vehicles, and industries. It's a barometer of development, with global steel production expected to reach 2.2 billion tonnes by 2026. Yet, this indispensable resource is also a significant environmental concern, contributing about 8% to global carbon emissions.

The challenge we face is clear: How can we ramp up steel production while minimizing its environmental impact?

The answer lies in green steel, a revolutionary approach that eliminates the use of fossil fuels in steel production. This shift is not just a necessity but a timely response to the escalating demand for sustainable practices in heavy industries.

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Quick Facts:

🟡 Why steel?

Steel is the foundation of our buildings, vehicles, and industries, with its production and consumption rates often seen as indicators of a nation's development.

To give you an idea of the demand side, by 2026, the global steel industry is projected to reach a production level of 2.2 billion tonnes.

Here are two projections of demand in millions of tonnes over time.

However, steel products, particularly in buildings, machinery, and vehicles, often have a very long lifespan.

So, over 75% of all steel ever produced is still in use.

This means that today only about 1/3 of production can come from recycling, but with a growing trend.

Therefore, “fresh” steel must be produced in large quantities for a rapidly growing global market.

However, the production of steel is also a significant contributor to global carbon emissions, accounting for approximately 8% of all greenhouse gas emissions.

🟡 Problem

Producing more steel while emitting the least amount of CO2 possible.

🟡 Solution

Green Steel: steel produced without the use of fossil fuels.

🟡 Why now?

In recent years, there has been growing interest in the development of green steel, which is produced using renewable energy and has a much lower carbon footprint than traditional steel.

Green steel is a key part of the transition to a more sustainable, low-carbon economy, and has the potential to significantly reduce carbon emissions from the steel industry.

Essentially, green steel is the manufacturing of steel without the use of fossil fuels.

Meanwhile, in the background, there is strong demand from corporate consumers who are willing to pay a premium for Green Steel, with a strongly growing market estimated to reach +6B by 2032 according to GlobalNewswire.

🟡 Deeptech startups into play:

Startups in this space are raising increasing sums from venture capital investors.

For example, Swedish startup H2 Green Steel has received billions in debt financing from major public and private banks.

Boston Metal, based in the United States, raised a Series C in January with ArcelorMittal among its investors, one of the world's largest steel producers.

🟡 Barriers and Future Scenarios:

Setting aside technological risks for a moment (after all, we're talking about Deep Tech), in the short to medium term, the innovations most likely to reach the market will leverage hydrogen as an alternative energy source.

Therefore, one of the biggest barriers to green steel production is the availability of low-carbon and low-cost hydrogen.

However, the production of green hydrogen is currently gaining momentum thanks to significant investments by governments and major energy producers, such as electricity companies.

The growing availability of green hydrogen could accelerate the production of green steel, ultimately leading to a more sustainable and low-carbon future for the steel industry.

Now let’s dive deeper into the problem-solution space, startups, and key dynamics.

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Context: Our Global Society is Built on Steel

The steel industry is a crucial component of the global economy as it provides raw materials for a wide range of products, from cars and buildings to appliances and packaging.

Today, it is the most widely used metal in the world and the most recycled material, with

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"Stellantis to invest in French sodium-ion battery maker for EV output" - Reuters

Why?

“The price of lithium has gone to insane levels!” - Elon Musk

Obviously, this is an extreme simplification of the context and the drivers, but it serves to position our thoughts on the right track to delve deeper into this topic.

So, let's start from the beginning...

One of the primary reasons lithium dominates the battery market today is deceptively, frustratingly simple: its small size.

In the most literal, atomic sense, lithium is the third lightest element, heavier only than hydrogen and helium. When it comes to creating

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Battery recycling is a topic of significant environmental, economic, and ethical considerations. As we delve into the intricacies of battery life cycles, we encounter both challenges and opportunities. The disposal methods of yesteryear, such as landfills and incineration, are no longer viable options. They present safety hazards and squander valuable minerals that could be recaptured and reused. The necessity for efficient and effective battery recycling systems has never been more critical.

The economic perspective of battery recycling is just as compelling as the environmental one. As we examine the market trends, it's evident that the industry is on an upward trajectory, with a market value that's forecasted to swell to billions by the end of the next decade.

From technological breakthroughs to market dynamics, this deep dive unravels the potential of battery recycling to redefine sustainability in our electrified era.

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Please keep sending us your feedback so that we can provide the most valuable content for you and our community.

You can reach us at info@thescenarionist.com

Introduction

Our world is going electric, with electric cars, bikes, and numerous other devices leading the charge in this transformation.

While this shift promises cleaner energy, it also raises a pressing question: what happens to these batteries when they reach the end of their lifespan?

Those cherished lithium-ion batteries we rely on for electric vehicles?

They typically endure approximately 500 charge-discharge cycles before displaying signs of wear. This implies that valuable minerals within them go to waste unless we embrace recycling.

Let's delve deeper.

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Hydrogen production and usage are undergoing a significant transformation, driven by a growing need to transition towards more sustainable energy sources.

Globally, hydrogen consumption reached 95 million tons in 2022, with an annual increase of 3% according to the International Energy Agency (IEA). Although this growth is promising, it remains concentrated in traditional uses such as refining and the chemical industry, with less than 0.1% coming from new applications.

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Goldman Sachs estimates that decarbonization and achieving net-zero emissions will require cumulative investments of $5 trillion, with the total potential market for hydrogen generation possibly reaching $1 trillion by 2050.

Yoshinori Kanehana, president of Kawasaki Heavy Industries and co-chair of the Hydrogen Council, emphasizes that despite the urgency driven by growing concerns for energy security, the on-the-ground deployment of hydrogen is not moving quickly enough and needs acceleration to realize its benefits.

Over 30 countries have already released official hydrogen strategies and plans, outlining consumption trends and infrastructural plans. These actions are a testament to the 'clean hydrogen revolution', an area where investors and governments are collaborating to finance and capitalize.

China has emerged as a leader in electrolyzer capacity, a key component in green hydrogen production, with installed capacity reaching over 200 MW in 2022. It is forecasted that this capacity will increase to 1.2 GW by the end of 2023, representing 30% of global capacity. This development is crucial for the transition to low-emission hydrogen and aligns with growing concerns over energy security and environmental sustainability. More details on this development can be found in the IEA's "Global Hydrogen Review 2023" report.

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On the other side of the world, Europe and the United States are making significant strides. The European Union recently announced the creation of a European Hydrogen Bank with a budget of 3 billion euros, an initiative underscored by the statement from Commission President Ursula von der Leyen, emphasizing the need to move from a "niche hydrogen economy to one on a large scale". This is an important step in increasing investments in the sector and accelerating the development of the hydrogen market.

At the same time, the Inflation Reduction Act in the United States and the REPowerEU program in Europe have given a significant boost to the hydrogen transition.

However, the hydrogen sector faces significant challenges, particularly in terms of production costs.

Currently, the estimated cost of producing renewable hydrogen is between $4.5 and $6.5 per kilogram. However, a reduction to $2.5-4.0 per kilogram is expected by 2030, attributed to advancements in electrolyzer technology and decreasing costs of renewable energies, as reported in the Hydrogen Council's "Hydrogen Insights 2023 December Update."

The Central Role of Hydrogen in the Global Energy Economy

Globally, hydrogen production is still predominantly reliant on fossil fuel-based technologies. In 2022, 70% of the energy required for hydrogen production was met using natural gas, and about 30% by coal, predominantly utilized in China. However, low-emission hydrogen production accounted for less than 1% of total hydrogen output in 2022, despite a 5% increase from 2021.

The regulatory landscape is evolving, with supportive measures such as tax credits for production and financial backing for hydrogen hubs in the United States, renewable hydrogen mandates in the Renewable Energy Directive (RED III) in Europe, and contracts for difference (CfD) in Japan. Nevertheless, regulatory uncertainties persist, impacting the effectiveness of these policies in the market.

Throughout 2023, the hydrogen sector has demonstrated significant developments, reflecting its growing importance in the global energy economy. A notable expansion in the pipeline of clean hydrogen projects has been observed, with over 1,400 initiatives announced. These projects collectively represent investments exceeding $570 billion USD anticipated up to 2030. This increase is a testament to the rising interest and commitment in the clean hydrogen field, with Europe standing out as a leader in investment volume, announcing over $190 billion USD, closely followed by North America and China. This trend underscores the increasingly strategic position of clean hydrogen in the global energy economy.

Two Decades of Hydrogen: Its Role in Industries

Over the last twenty years, the energy landscape has witnessed a substantial increase in the use of hydrogen, a versatile and crucial element for many industrial applications, highlighting trends that warrant in-depth examination.

In the refining sector, hydrogen consumption has remained dominant, reflecting its fundamental importance in the process of transforming crude oil into finished products. This consistent demand underlines not only the essentiality of hydrogen in the treatment of fossil fuels but also the ongoing centrality of such energy sources in the current economy.

Simultaneously, the chemical industry has seen a marked increase in the demand for hydrogen. Here, hydrogen is primarily used in the synthesis of ammonia and methanol, indispensable compounds for a variety of chemical processes. The rise in demand within the chemical sector indicates the global expansion of the industry and its inherent dynamism.

The steel industry, too, has shown significant hydrogen demand, mainly used in the reduction of iron oxide in steel production. Although the volume of hydrogen used is lower compared to the refining and chemical sectors, its application in steelmaking is particularly important for the transition towards environmentally friendly production processes.

Attention has also turned to other industrial areas, transportation, and injection into the electrical grid, albeit representing a smaller portion of total consumption. These emerging sectors, while not yet having an impact comparable to that of traditional industries, are gaining relevance in the perspective of a future economy based on cleaner and renewable energy sources.

Hydrogen, the most abundant element

When discussing hydrogen as a clean energy carrier, it is crucial to understand its production process, as not all forms of this gas are considered "clean."

Hydrogen is the simplest and most abundant element in the universe. However, on Earth, it rarely exists in gaseous form, thus requiring a process of separation from other elements.

Hydrogen only emits water when burned. However, creating it can involve a high carbon intensity.

Green hydrogen is the only form produced in a climate-neutral manner, making it an essential candidate to contribute to the global reduction of emissions, aiming to achieve zero emissions by 2050. The demand for hydrogen has more than tripled since 1975. According to a report from the International Energy Agency, "The Future of Hydrogen," nearly all hydrogen is produced from fossil fuels.

At € 1.5/kg of H2, the so-called "grey" hydrogen is the most economical form available, but its production emits 9.3 kg of CO2 per kilo of hydrogen, not qualifying it as clean energy.

When CO2 is removed using carbon capture technologies, "blue" hydrogen is created, but this process increases the price of hydrogen and its carbon footprint.

Green or "clean" hydrogen is produced through the electrolysis of water using renewable energy. This keeps the carbon footprint lowest but currently costs between €2.5/kg and €5.5/kg.

When "geological hydrogen" is not produced but extracted, it is called white hydrogen - a natural form sometimes found underground. However, practical methods for extracting it are limited, yet according to Beam Earth, a pioneer in clean oil extraction, white hydrogen can be produced at a significantly lower cost, from 0.5 to 1 dollar per kilogram.

Different Shades of Hydrogen. Hydrogen Production.

Now, to better navigate the intricacies, let's delve into greater detail about the production methods of hydrogen and understand its nuances...

As we have mentioned, hydrogen can be produced from a variety of resources

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As EVs become more common, they still face significant challenges like extended charging times, limited range, high costs, and environmental impact. The advancement in battery technology is crucial to address these issues and enhance the appeal of EVs.

In this deep dive, we will explore the potential of flow batteries in transforming the EV market. We'll examine why this innovation is crucial at this moment, how it stands compared to the prevalent lithium-ion batteries, and the innovative startups that are spearheading this technology.

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Quick Facts

1. Electric Vehicles (EVs) are becoming more prevalent, but they still face issues like long charging times, inadequate range, high entry costs, and environmental impact. Better batteries are needed to make them more attractive.

2. According to a Castrol study, the average electric car needs to offer charging times under 31 minutes, a range of 470 km, and cost less than $36,000 to achieve mass adoption to reach mass adoption.

3. BloombergNEF predicts that the magic spot for EV prices will be competitive with internal combustion engine costs when battery costs fall below $100 USD/kWh.

4. Flow batteries show potential in addressing concerns about electric car performance and usability and have the potential to revolutionize the electric vehicle market. The flow battery market is expected to reach USD 5 billion by 2026 with a CAGR of 18.0%.

5. Most of these use a vanadium electrolyte solution that unlike lithium-ion batteries. It is one of the most promising flow batteries out there. Basically, it has an almost infinite life cycle, almost no degradation, enormous safety margins. Moreover, it is almost 100% recyclable, and is incredibly environmentally friendly and inexpensive.

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Why does it matter today?

The electric vehicle revolution is well on its way to dominating the automotive industry, but these incredible machines are far from perfect.

Even Tesla, arguably the leader in the electric vehicle market, still struggles with long charging times, inadequate range, supply chain instability, high entry costs, battery degradation, and the environmental impact of production, not to mention the fact that lithium-ion batteries have an annoying tendency to catch fire, creating a new need for extensive safety measures to be put in place.

Don't get me wrong, Teslas are fantastic. But let's face it, if we want the world to go electric and reach our climate goals and save the planet, we need to do better.

The EV revolution won't go mainstream until we make better batteries!

And now imagine...you've run out of battery in your electric car, but instead of having to plug it in and wait for the battery to fill up, you could instead refuel at a gas station in just a few minutes with a fluid full of electric energy, just like you would for a combustion engine car.

How is this possible? But, let's go in order and see the status of today's EV batteries.

The EV batteries status quo in a nutshell

Currently, electric cars require access to charging points to power up.

While these charging points are increasing in number, they are certainly not as widely available as traditional gas stations.

The current charging time also significantly exceeds the time needed to fill up a combustion engine car. Drivers have to wait anywhere from 30 minutes to several hours to recharge their vehicles, making them far less convenient.

Moreover, there are concerns about "range anxiety," where potential electric car owners are nervous about switching from their fossil fuel vehicles because long-distance trips would require more frequent energy charges than they would need for gas refills.

There’s a need for a change to boost mainstream EV adoption

According to a 2020 study by Castrol (a British oil company), the average electric car will need to offer charging times under 31 minutes, a range of 470 km (291 miles), and cost less than $36,000 to achieve mass adoption.

As for prices, according to a BloombergNEF study, the sweet spot for electric vehicle prices will be competitive with internal combustion engine costs when battery costs fall below $100 USD/kWh.

As of writing, the average price hovers around $153/kWh with peaks up to $650/kWh for some automakers.

BloombergNEF also predicts that the magical threshold of $100 USD/kWh could be reached as early as 2024 and by 2030, they should reach $61 USD/kWh.

Despite much progress in battery energy density in the last decade, rapid charging is still a significant challenge: can EVs achieve a sufficient state of charge to last at least 470 km (291 miles) in just 31 minutes?

One emerging idea to overcome this challenge is to replace the common lithium batteries used in EVs with redox flow batteries (RFBs).

Have You Ever Heard of Flow Batteries?

Here’s how John Cushman, professor of earth and atmospheric sciences and mathematics at Purdue (and one of the leading researchers on liquid battery technology) describes this breakthrough technology.

Basically, scientists are working to develop rechargeable, or so-called flow, batteries that can be refueled in just a few minutes at a network of converted gas stations.

It's a change that could make electric vehicles (EVs) more attractive to drivers who are wary of long charging times.

Sounds like magic? Probably a little bit. Now we'll try to dive into it to easier navigate tech and innovation.

What are Flow Batteries?

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