Foreword
Five years ago, as I was still at BCG, we partnered with Hello Tomorrow to publish a series of three reports, which I co-authored, that sought to define Deep Tech as a distinct approach to innovation (**The Great Wave, Nature Co-Design, The Deep Tech Paradox**). The thesis was clear: deep tech ventures leverage scientific and engineering advances to solve fundamental problems, massively expanding the option space of possible solutions through accelerated Design-Build-Test-Learn (DBTL) cycles, technology convergence, and problem orientation rather than solution fixation.
That thesis has proven remarkably durable. But my perspective has radically shifted, from analyzing deep tech as a consultant from afar to being on the ground, building a deep tech company as an entrepreneur. And from this “privileged” vantage point, I can see now both what those reports got right and what they missed.
While deep tech succeeded at accelerating discovery, it failed in most instances at scaling production. The defining challenge of the next industrial era is not inventing faster, but transferring innovation into reliable, repeatable, industrial execution. I call this missing capability industrial transfer, and mastering it is what separates deep tech failures from Neo Industrial winners.
What the Deep Tech Reports Got Right
The core innovation engine we described has not only been validated, it has been turbocharged beyond our projections.
The DBTL cycle has been supercharged by AI. When we wrote about the Design-Build-Test-Learn cycle in 2021, we understood it as powerful. We did not anticipate just how profoundly AI would transform it. AlphaFold won the Nobel Prize in 2024 for solving protein structure prediction, a problem that had stumped scientists for fifty years. DeepMind’s GNoME expanded the number of known stable materials from 48,000 to over 421,000 structures. Insilico Medicine demonstrated drug discovery in 18 months at $2.6 million versus traditional timelines of 42+ months and $430+ million. The Design and Learn phases of the DBTL cycle have been accelerated by orders of magnitude.
Technology convergence has intensified. The three-domain convergence we identified - Matter & Energy, Computing & Cognition, Sensing & Motion - has accelerated faster than predicted. But AI has emerged as more than one technology among equals. It has become the universal connector, the accelerating force across all domains. As researchers now write in Nature Communications, the cycle might better be described as “LDBT”: Learning first, then Design, then Build and Test, because AI enables “zero-shot predictions” before any physical experiment begins.
The expanded option space is real and growing. Companies today access solutions that were literally unimaginable in 2021. The option space has expanded exponentially, and AI allows us to navigate it with unprecedented efficiency.
What the Deep Tech Reports Missed: The Industrial Transfer Gap
Here is what we got wrong, or more precisely, incomplete: we focused almost entirely on what is known as “tech transfer”, the movement of innovation from university research to startup prototype. We articulated how to cross the valley of death between laboratory discovery and working technology demonstration.
But there is another valley. A far more dangerous one. And we largely missed it.
The critical gap is “industrial transfer”: the movement from pilot scale to industrial scale manufacturing.
Evidence from 2021-2025 makes this brutally clear:
Zymergen raised $874 million and achieved a $5 billion valuation at IPO in April 2021. By August 2021, the stock had lost 75% of its value overnight. By October 2023, the company was bankrupt. Zymergen had world-class science. Their DBTL cycle worked brilliantly in the lab. Their Hyaline material performed as designed in laboratory conditions. But when it came to manufacturing at scale and integrating with customer production processes, the company failed completely. This was not a tech transfer problem; it was an industrial transfer problem.
Northvolt raised over $15 billion in equity and debt and was once valued at $12 billion, positioned as Europe’s answer to CATL and the continent’s best hope for battery independence. The company filed for bankruptcy in November 2024 (US) and March 2025 (Sweden), the largest bankruptcy in modern Swedish industrial history. What went wrong? Northvolt’s flagship Skellefteå gigafactory was designed for 16 GWh annual production; actual output reached just 1 GWh, less than 0.5% of the target. BMW cancelled a $2 billion contract in June 2024 after Northvolt fell two years behind on deliveries. The company burned $100 million monthly while production remained too low to generate adequate revenue. As one Chinese executive observed: “We can raise a factory’s battery yield to 96% in just four months. Northvolt took four years and only achieved 70%.” The technology existed. The manufacturing execution did not.
Amyris built sprawling infrastructure of industrial fermentation vats, achieved peak revenue of $153 million, accumulated $1.33 billion in debt, and filed for bankruptcy in August 2023, despite multiple pivots from biofuels to cosmetics to consumer brands. Again: the technology worked. The manufacturing at industrial scale and unit economics never did.
The pattern extends beyond synthetic biology and batteries. Lilium burned through $1.5 billion before filing for insolvency in late 2024. Climeworks designed direct air capture facilities for 36,000 tons per year, but actually operates at approximately 105 tons, a gap of over 99%. Across deep tech sectors, the same story repeats: laboratory success followed by manufacturing failure.
The exceptions prove the rule. Commonwealth Fusion Systems has raised over $2.1 billion and represents perhaps the gold standard for industrial transfer thinking—validating enabling technology before commercial commitments, designing SPARC and ARC for parallel development, securing customer commitments (a 200 MW Google PPA) before commercial operations. Form Energy built a pilot facility first, then scaled to commercial production. Sila Nanotechnologies designed for manufacturing from inception. What distinguishes these successes? They treated manufacturing capability as core competence from day one.
The Neo Industrial Age Emerges
This evidence points toward something larger than a correction in deep tech strategy. We are witnessing the emergence of a new industrial age.
We have entered what Nicolas Colin calls the Late Cycle Investment Theory, i.e. the maturity phase of the computing and networks revolution, our equivalent of the 1970s in the age of oil, automobiles, and mass production. The startup funding collapse of 2022 was not merely cyclical but structural. AI breakthroughs come from massively capitalized entities, not garage startups. The fog of uncertainty that characterized earlier technological phases has lifted. Optimization, not disruption, becomes the focus.
In Colin’s words: “The future doesn’t belong to software eating the world. It belongs to manufacturing eating software, embedding intelligence into the physical world.”
As we consume the final phases of the current technological cycle, a new phase is emerging—one in which the industrial dimension of the economy is being completely redefined. I call this the Neo Industrial Age: a period in which the foundation of industrial infrastructure built in the 19th and early 20th centuries is being completely rebuilt utilizing the deep tech approach to innovation.
The geopolitical context makes this urgent. China has outmanufactured the West. The world is fragmenting. Ongoing wars have upended the meaning and role of technology in conflict. We are witnessing a transition from “Petrostates to Electrostates“ and what Packy McCormick terms “the Electric Slide“, a fundamental shift in how energy powers civilization.
And critically, the US and China have made very different bets. America is betting that whoever wins intelligence, in the form of AI, wins the future. China is betting that for intelligence to truly matter, it needs energy and action. If you control energy and manufacturing, making intelligence abundant strengthens your position.
As a consequence, the Neo Industrial Age demands a new organizational form, one capable of mastering both tech transfer and industrial transfer, both intelligence and manufacturing. That organizational form is the Neo Industrial Company.
The Building & Testing Bottleneck: Building Hardware at the Speed of Software
Understanding what comes after deep tech requires understanding why the DBTL cycle, despite being turbocharged by AI, still faces fundamental constraints. The answer lies in a profound shift in where the bottleneck sits.
The Asymmetry of AI Acceleration
AI is an incredible enabler, and its power in the context of deep tech cannot be stressed enough. It truly requires a different approach to innovation. But here is the critical insight: the limiting factor in the DBTL cycle is no longer in designing new solutions or in Learning from data. These phases have been powered by several orders of magnitude.
Building and Testing are now the biggest bottlenecks, with profound implications for how Neo Industrial Companies must operate.
Innovations compound, making the option space even bigger. With increased AI power, that space can be navigated more efficiently than ever. But every designed solution must still be built physically. Every hypothesis must still be tested in the real world. The speed of atoms has not changed.
The Building Bottleneck
To keep pace with AI-accelerated innovation cycles, organizations must dramatically accelerate their prototyping capabilities. This has to be achieved along two dimensions: internal and external.
Internal prototyping acceleration means translating the software-based MVP approach to the physical world. This requires leveraging physical solutions developed for “orthogonal” industries, cross-pollinating techniques from aerospace, automotive, pharma, and other sectors. It requires extensive use of 3D printing and rapid prototyping technologies. It requires building internal capabilities to iterate on physical designs at something approaching software velocity.
External prototyping acceleration - supply chain iteration capability - is equally critical. Neo Industrial Companies must curate their entire supply chain for speed. The capability for suppliers to iterate fast and develop needed parts and components becomes as important as internal R&D velocity. This is not about cost optimization in the traditional sense. It is about clock speed alignment across the entire value chain.
But there is a limit to what can be prototyped physically in a fast manner. Consider Northvolt’s challenge: building gigafactory-scale battery production. You cannot “simply” build a full-scale prototype and see if it works; it would cost too much and take too long. Northvolt discovered this painfully: their production ramp took four years to reach 70% yield, while Chinese competitors achieve 96% yield in four months. This implies that a significant portion of the innovation cycle must happen “in silico”.
The Digital Original, Not the Digital Twin
The conventional approach to digital modeling creates a “digital twin”, a digital replica of something that exists physically. You build the bioreactor, then create a digital model to monitor and optimize it.
Neo Industrial Companies invert this logic. They develop what I call the “Digital Original”, a comprehensive digital representation created first, validated extensively in simulation, with the physical system built only after virtual proof. The physical system becomes the “Real Twin” of the Digital Original.
The option space needs to be created first at the software level, using AI and simulation. The DBTL process needs to happen “in silico” first, where multiple options are tested and refined. Only what has gone through the complete “in silico” innovation cycle should then be built physically.
This workflow inversion changes everything:
Traditional approach: Design on paper → Build physical prototype → Test equipment → Identify issues → Modify designs → Rebuild. Timeline: 18-36 months.
Digital Original approach: Create Digital Original → Test 100+ variants digitally → Optimize → Validate virtually → Build Real Twin once. Timeline: 6-12 months.
The key difference is where iteration happens. In the traditional model, iteration occurs in physical space at high cost. In the Digital Original model, iteration occurs in digital space at low cost. The physical build becomes an execution phase rather than a discovery phase, building what you know works, based on comprehensive virtual testing.
This creates a compounding effect. Once you have developed and validated a component in the Digital Original, whether a bioreactor design, a downstream processing module, or a complete process train, it becomes a reusable digital asset. Each facility built makes the next one faster and cheaper.
The Testing Bottleneck
Testing is a radically underestimated constraint in redesigned innovation cycles. It is, together with building, the fundamental bottleneck that the Neo Industrial Age must address.
The limit is no longer set by the capability to learn and design. Quite the opposite, AI has given us orders of magnitude superior capabilities in those dimensions. But those capabilities are now limited by the data generated after prototypes have been built.
This point is not confined to the prototype phase. It is true throughout the industrial cycle, including at industrial scale during ongoing production.
The critical insight:
the design of hardware should be determined not only by the “physical” specs of the object being produced, but by the requirements in terms of data needed to fully leverage the potential of software and AI.
This represents a fundamental inversion. Software and data should not be seen as an additional layer to be added to a primarily physical product. Rather, software and the needed data become core drivers of physical design. The hardware exists, in part, to generate the data that enables learning.
Consider what this means in practice. Zymergen’s failure was not just about manufacturing capability, it was about designing systems that could generate the data needed to learn and improve at industrial scale. A traditional bioreactor is designed for volumetric efficiency, mixing quality, sterility maintenance, and cleaning ease. A Neo Industrial bioreactor is designed for all of those, plus optimal sensor placement for data generation, instrumentation access for continuous monitoring, and data architecture that feeds machine learining models in real time.
This is what it means to be “software/data first” while producing physical output.
Building Hardware at the Speed of Software
These points about Building and Testing can be summarized in a phrase that is admittedly catchy but captures something essential: “Building hardware at the speed of software, starting from the Digital Original, leveraging the deep tech approach also in silico, and designing the hardware with a software/data first approach.”
All of these points become even more important in 2026 than they were in 2021 because the geopolitical situation has dramatically changed. Manufacturing and industrial prowess are now essential components of geopolitical discourse.
Again, and with the risk of repetition, I cannot stress this enough: China has leapfrogged the West when it comes to manufacturing. The world is no longer as global as it was in 2021. Ongoing wars have completely upended the meaning and role of technology in warfare. We are witnessing a transition from Petrostates to Electrostates.
Also, important to remind that the US and China have adopted fundamentally different views on AI. The US is focused on computing and achieving “AGI”: artificial general intelligence as an end in itself. China is focused on embedding AI in the real world and manufacturing: AI as a means to physical-world dominance.
The data on execution speed is stark. BYD takes 21 months from concept to production for a new electric vehicle. Mercedes-Benz and Volkswagen take 36-48 months. Chinese battery manufacturer Hithium builds Giga-scale plants in 16 months. European equivalents take 58 months. These are not incremental differences. They represent fundamentally different organizational capabilities, the capability to build hardware at the speed of software, supported by industrial districts (e.g. Shenzen) and supply chains which operate at the same clock speed.
The Neo Industrial Age demands companies capable of operating at this level and at this speed. These are the Neo Industrial Companies.
Enter the Neo Industrial Company
As we consume the final phases of what Nicolas Colin calls the Late Cycle, a new phase is emerging. The industrial dimension of the economy is being completely redefined. The foundation of industrial infrastructure built in the 19th and early 20th centuries is being rebuilt, or, in the case of China and the Electric Slide, built for the first time in its modern form, utilizing the deep tech approach to innovation.
What is happening with the electrical transformer is a perfect example of how the industrial foundation is being rebuilt. For over a century, Stanley’s basic design: copper wire wrapped around a heavy iron core, has remained largely unchanged. But today, a new wave of startups is developing Solid-State Transformers (SSTs).
Instead of relying solely on bulky magnetic coils, SSTs use high-frequency power electronics and advanced semiconductors (like silicon carbide) to route electricity. This makes them significantly smaller, digitally controllable, and capable of bidirectional power flow.
The new era emerging from rebuilding the industrial foundation is the Neo-Industrial Age. And it demands a new organizational form: the Neo Industrial Company.
The Ten Pillars of the Neo-Industrial Company
Neo-Industrial Companies share a distinctive set of characteristics that distinguish them from both traditional industrial corporations and conventional deep tech startups. These can be distilled into ten defining pillars.
1. Software/Data First, but Physical Output
Neo Industrial Companies make things: physical products, manufactured goods, built infrastructure. But despite their physical output, they are fundamentally driven by software and AI. Their approach to innovation is steered by data. They design and build hardware to accommodate data generation and AI integration. The way AI is embedded in how they operate is native to their operating system, not bolted on as an afterthought, but architected from the foundation.
This is the radical inversion at the heart of Neo Industrial thinking: companies no longer begin with hardware and layer intelligence on top. Instead, they begin with intelligence - AI, algorithms, sensing systems - and allow that intelligence to shape the infrastructure. The hardware exists, in part, to generate the data that enables learning.
2. AI-Native Operating System
Neo Industrial Companies don’t merely use AI tools. AI is embedded natively in their operating system, in how they design, build, test, learn, manufacture, and operate. AI-assisted feedback loops analyze production data in real-time, predict and prevent process deviations before they impact output. The AI layer is not an optimization of existing processes; it is foundational to the process architecture itself.
3. Deep Tech Approach to Innovation
Neo Industrial Companies operate according to the deep tech approach, constantly widening the option space, leveraging technology convergence and the AI-powered DBTL cycle. Their innovation cycles are incredibly fast and powerful. But unlike pure deep tech ventures, they extend this approach from R&D all the way through industrial production. The DBTL cycle doesn’t stop at prototype; it continues through manufacturing scale-up and ongoing operations.
4. Economies of Learning over Economies of Scale
Because of the AI-powered DBTL cycle spanning from innovation to industrial production, Neo Industrial Companies are fuelled primarily by economies of learning rather than traditional economies of scale. Economies of scale still exist and matter, but they are secondary.
The core characteristic is how they maximize the rate of learning, defined as: Rate of Learning = Velocity × Quantity. This is a consequence of the importance of Design and Learning in the DBTL cycle, and also an indirect consequence of the software/data first approach. Techniques typical of software development - modularization, composability, standardization of interfaces - become core to physical production.
The indirect impact: production becomes more distributed. Components and materials bifurcate; some become hypercommoditized (available everywhere, interchangeable, competing on cost alone), while others become decommoditized (proprietary, differentiated, competing on performance and integration).
5. Vertical Integration Across the Value Chain and the Supply Chain
Neo Industrial Companies fundamentally focus on the value chain, either covering it end-to-end or addressing significant portions of it. This is necessary to maintain consistent innovation velocity across the entire chain, to apply the deep tech approach throughout, and to develop the supply chain and ecosystem needed to sustain the required speed. The supply chain and the value chain blend together, as they need to operate at the same velocity.
Packy McCormick calls companies with this characteristic “Vertical Integrators“ and offers a crucial insight: for these companies, “the integration is the innovation.” They take the risk on the combination of proven technologies, not just on unproven science.
Because of their focus on whole or significant parts of the value chain, their need to master the supply chain, and their technology-agnostic approach, Neo Industrial Companies have natural propensity to become consolidators in their industries.
6. Production Capital Stack
Neo Industrial Companies consider the capital stack as a core instrument to reach industrial scale. They do not rely solely on venture capital and dilutive equity. Instead, they use VC wisely to address technological risk, then work to de-risk the endeavor as early as possible to shift funding to asset-backed financing and project finance.
Brett Bivens calls this “Production Capital“, initial venture equity for R&D and product development, followed by targeted asset-based debt as a wedge to unlock project-level financing once the core technology demonstrates commercial viability. As deployment scales, warehouse facilities or securitization structures finance multiple deployments simultaneously.
As Bivens writes: “Look at any successful hardware company and you’ll see the same pattern: they evolved from pure manufacturers into financial powerhouses. Tesla isn’t just a car company, it’s one of America’s largest consumer lenders. John Deere, Siemens, and ABB all built their own banks. This financial maturity isn’t optional.”
7. Design for Manufacturing from Day One
As Elon Musk says, and VC giant A16Z recently reminded us, “the factory is the ultimate product”. Neo Industrial Companies do not treat manufacturing as a downstream problem to solve after the technology works. They architect for production from inception.
This is perhaps the single most important lesson from the failures of Zymergen, Northvolt, and Amyris: all had technology that worked, Zymergen’s Hyaline material, Northvolt’s battery chemistry, Amyris’s fermentation processes. All failed at industrial transfer because manufacturing was not a core competence from the beginning.
8. Complex Adaptive Systems Organization
Neo Industrial Companies operate as complex adaptive systems capable of iterating at the velocity of deep tech innovation while matching the speed of the evolving environment and technological development. They embrace complexity rather than seeking to eliminate it.
They have the capability to build and develop very disparate capabilities - what might be called “crafts” - and get them to work together. Very different profiles must be brought together: innovation balanced with industrial expertise, development profiles with operational profiles, engineering with science, laboratory with industrial scale.
Given this, the team and organizational setup are as important as - if not more important than -the technology stack. If a Neo Industrial Company is focused on the right problem, once that problem is solved, the market is predictably there. The core focus should always be on industrial transfer potential, and the team and its mix of experience, capabilities, and mindsets is the most important asset for reaching industrial scale.
9. Energy as Technology
Neo Industrial Companies consider energy - particularly electricity, but not only - as a technology and an integral part of their technology stack. As Azeem Azhar articulates: energy has shifted from commodity to technology. Solar and lithium-ion batteries have learning rates above 20%—for every doubling in production, prices decline by 20%. Energy that thinks, responds, and adapts is fundamentally different from energy that is merely consumed. Neo Industrial Companies design with this understanding, treating energy as programmable infrastructure that co-evolves with sensing, software, and process requirements. With Energy impacting often up to 30% of the OPEX costs, the impact on profitability is massive.
10. Technology Agnostic, Problem Focused
Most Neo Industrial Companies are not built on a single technology. Because of their focus on significant portions of the value chain, their emphasis is on solving the economic and industrial problem, not on proving a technology. In some cases - Commonwealth Fusion Systems and superconducting magnets, for instance - the enabling technology is key, particularly in the early stages. But the focus is never solely on proving the technology; it is always on solving the industrial problem behind the value chain.
This means materials, sensors, and data become the trifecta around which new value chains are built, not proprietary technology as “magic wand.”
Chinese Companies Are Already There
The uncomfortable reality is that China is already building Neo Industrial Companies at scale.
BYD exemplifies the model. The company has achieved unparalleled control over its production cycle, only tires and windows are entirely outsourced. BYD controls battery cells, electric powertrains, semiconductors, electronic modules, axles, transmissions, cockpits, brakes, and suspensions. Where Western automakers take 3-4 years from concept to production, BYD takes 21 months. In 2024, BYD’s revenue reached $107 billion, exceeding Tesla’s $97.7 billion.
McCormick observes: “Underestimating Chinese companies as copycats is a mistake, particularly BYD. Among all Chinese electric companies, BYD is the most vertically integrated, and innovates on both the components and at the system level.”
Integration drives innovation. BYD made batteries, then started making cars, and the deep knowledge of both allowed it to bet on LFP chemistry early and develop the Blade Battery that propelled it to global dominance. Manufacturing and design are inextricably linked. When you make things, you learn how to make them better.
The contrast with Northvolt is instructive. Northvolt raised $15 billion and built impressive facilities. But Chinese competitors achieve in four months what took Northvolt four years, not because the technology was different, but because the organizational capability for industrial transfer was fundamentally different.
The Stakes
The Neo Industrial Age is not a prediction. It is already here. The question is whether Europe and the West will develop Neo Industrial Companies capable of competing, or whether they will cede industrial capability to those who understood the shift earlier.
This is not primarily a matter of policy, though policy matters. It is a matter of building organizations capable of mastering both tech transfer and industrial transfer, both AI-powered innovation and manufacturing excellence, both software velocity and physical-world deployment.
The companies that achieve this will not merely succeed commercially. They will define what it means to make things in the 21st century.
The Neo Industrial Age demands nothing less.
Afterword
In going from being a consultant to becoming an entrepreneur I went from one extreme of the spectrum to the other. I went from observing to doing. And from this new “privileged” position, I cannot stress enough how difficult it is to build each of the ten pillars described above. Simply because each of them is somehow antithetical to the way businesses have been built over decades.
Writing this piece was a “smooth” abstraction and consolidation exercise, driven by my learnings on the ground. Instead, building the Neo Industrial Age is a massive effort that requires collaboration at all levels.
Neo Industrial Entrepreneurs will not be able to succeed without the support of “enlightened” investors, policy makers, other business leaders who understand and share the vision behind the Neo Industrial Age. Which is what motivated me to write this piece, hoping it is going to trigger a healthy discussion.
Finally, the concepts described in this essay are admittedly biased by my work with Arsenale on Industrial Biotech. There might be additional perspectives that could round it and make it more compelling.
I encourage everybody with insights that can contribute to shape the Neo-Industrial Age to reach out and engage.
Tech and industrial share a similar core process: I
Iteration. The computer can do it so fast that it appears there is intelligence in the process. Industrial has materials to work with that are real instead of abstract bits and bytes that only cost a stream of electrons. Different mediums and different rates of iteration where both are faster then by hand. Imagine Euler with a calculator. Then a computer. He was using tables while today we use tables called spreadsheets.
Tech is a commodity being treated as if it is still an innovation. A new generation of bored bright minds are going to find their fulfillment in a new frontier: reindsutrialization which you termed as neo-industrial.
I enjoyed reading this piece, thanks Massimo. I particularly like the term industrial transfer, and building hardware at the speed of software. I wonder what the digital original approach will look like in practice. I recently wrote about what commercial traction looks like for hardware startups before scale, and your idea added a very interesting perspective.