The Korean deep-tech market is experiencing a moment few expected to see this soon.
In the first half of 2026, the country’s venture capital volume hit all-time records, with billions of won flowing into tech startups before many of them had even proven they can operate outside a lab.
And that is exactly where the most interesting tension in this landscape lives.
There is no shortage of money, no shortage of engineering talent, and no shortage of impressive technologies being developed. What is increasingly in short supply is clarity on something far more pragmatic: can the company actually manufacture the product at scale? And is anyone genuinely willing to pay for it?
These two questions sit at the center of a conversation that the deep-tech B2B investment ecosystem needs to have more often. After all, a prototype that works flawlessly under controlled conditions is a meaningful technical achievement, but it is far from a complete investment thesis.
Consistent production, corporate customer approval cycles, and real readiness for CAPEX allocation are the elements that separate a technically impressive startup from a business with solid investment conviction. This article is about that filter. 🎯
The venture capital wave that raised the bar for evaluation
South Korea is putting significantly more money behind young technology companies. According to the country’s Ministry of SMEs and Startups, new venture capital investment reached a record KRW 8.87 trillion in the first half of 2026, a 54.3% increase compared to the previous year. Companies three years old or younger attracted KRW 1.83 trillion, a 56.4% jump.
Technology-intensive sectors were among the fastest-growing destinations for that capital. Investment in information and communication technology manufacturing rose 143.3% year-over-year, while the electrical equipment, machinery, and mechanical engineering segment grew 90.4%. The same ministry reported that 16 early-stage companies raised rounds of at least KRW 10 billion, with nine artificial intelligence and robotics companies accounting for KRW 315.3 billion of that group.
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These numbers make the quality of early-stage risk assessment far more important, because substantial amounts of capital can reach companies before mature revenue offers a long commercial track record. And that is precisely where the dilemma for the deep-tech investor emerges.
A startup can have valuable intellectual property, an exceptional engineering team, and proven performance, and still carry unresolved questions about production and customer adoption.
When the money arrives before maturity
The accelerated flow of capital into the deep-tech sector has brought a consequence that nobody discusses openly very often: money started reaching startups that have not yet solved the fundamental challenges of production. This is not necessarily a mistake by investors, because the logic of venture capital has always been to bet on potential before certainty. But in the context of B2B deep-tech, where development cycles are long, infrastructure costs are high, and corporate customers have approval processes that take months or even years, this early arrival of capital can create a dangerous mismatch between what the startup promises and what it can actually deliver when the contract is finally signed.
Byung Gun Song, a team leader focused on mobility, deep-tech, and digital transformation at Schmidt, is someone who has observed this problem from both the engineering side and the investment side. His prior experience includes work on the fuel cell design team at Hyundai Motor Company and in the investment division at ON Venture Investment, which gives him direct exposure to both worlds: technical development and investment evaluation of deep-tech businesses.
In an exclusive interview with KoreaTechDesk, Song boiled down the investment question for the B2B companies he evaluates to two practical tests: the company’s readiness to make the necessary CAPEX investments and the customer’s genuine willingness to adopt the product. According to him, these are the two most important factors, and that distinction moves the conversation beyond the usual question about technical superiority.
Technical performance is proof, not the investment thesis
A technical milestone answers an important question: can the technology perform the task it was designed for? But investment readiness demands additional answers, because a venture-backed company needs to reproduce that performance outside a controlled development environment.
Song is clear that technical performance alone is insufficient. Investors need evidence that the company can produce and deliver at the required scale, and that customers are ready to move beyond evaluation and into commercial use. Bringing those points together, three connected forms of proof emerge for industrial deep-tech:
- Technical proof: establishes product performance.
- Industrial proof: establishes that the product can be manufactured with viable economics and operational discipline.
- Commercial proof: establishes that a real customer organization has sufficient reason to adopt what the startup intends to manufacture.
The connection between these proofs is crucial because manufacturing decisions can consume large amounts of capital before revenue becomes predictable. A founder raising money for production equipment, capacity, or infrastructure needs more than a technically compelling reason to spend that money.
Customer adoption as the real thermometer
If production capability is the first filter, customer adoption is the second, and perhaps the most revealing of all. In the B2B deep-tech context, adoption does not mean a company agreed to run a pilot or that an executive said in a meeting that the product looks promising. Real adoption means a customer paid for the product, integrated it into their own production or operational process, and made a conscious decision to keep using it even after encountering the real-world problems that any technology presents outside a controlled environment. That is an entirely different level of commitment from a signed agreement or a letter of intent that shows up in a pitch deck.
Song raises an important warning on this point. Industrial startups often work closely with engineers, innovation teams, or operational departments inside large corporations during technical evaluation. Positive feedback from these people is valuable, but he cautions against assigning it more commercial significance than the organization has actually demonstrated. A strong relationship with the customer’s operational-level contact is valuable, but it should not be confused with a finalized purchase decision.
He points to factors like supplier diversification, qualification processes, budget approval, alternative vendors, and broader corporate considerations as elements that can influence the final purchasing decision. A person who champions the startup internally can be an important advocate without controlling the entire buying process.
Global B2B research shows just how broad that process can become. A 2026 Forrester study on enterprise purchasing found that a typical buying decision involves 13 internal stakeholders and nine external influencers. Procurement professionals act as decision-makers in 53% of enterprise buying cycles. The same research noted that more than 60% of buyers use some form of trial before purchasing, while just over a third reported planning to convert to a fully paid version with the same vendor. That number illustrates well the distance that can exist between evaluation activity and real purchasing commitment.
Production and investment: two sides that need to meet
The relationship between scalable production and sustainable investment in deep-tech is more complex than it looks at first glance, and it is worth paying close attention to because it is precisely where many startups encounter the biggest obstacles. When a deep-tech company can demonstrate that it has a repeatable production process, with per-unit costs that decline as volume increases and a minimally structured supply chain, it fundamentally changes the kind of conversation it can have with investors.
Song emphasizes that production introduces a second layer of uncertainty even after a prototype succeeds. He identifies production quality and cost as recurring risks during scale-up. Yield, consistency, and process stability can shift when new manufacturing processes are introduced, creating a gap between the assumptions established during prototype development and the actual economics under commercial production. A process that works well at small scale can behave very differently when transferred to mass production.
The problem is that reaching this level of production maturity requires capital-intensive spending before the company has the revenue to justify it. Production equipment, certifications, reliability testing at scale, and hiring professionals with industrial operations experience are costs that show up long before any meaningful revenue line. This is the so-called deep-tech valley of death, which is a later stage than the traditional valley of death for software startups, but equally dangerous. It is during this phase that the technical capability of the company’s leadership needs to expand beyond pure engineering and start including competencies in operations, supply chain, quality, and financial management of manufacturing businesses.
A production plan built on unrealistic yield assumptions can distort unit economics. Unexpected process instability can require additional equipment or engineering work, while quality issues can affect delivery timelines and customer confidence. That is why manufacturing readiness is a crucial part of investment readiness, and not a task that begins after the funding round closes.
The most expensive proof comes after the demo
Deep-tech investment becomes difficult precisely because the greatest engineering achievement can arrive before the greatest business evidence. A successful prototype gives the investor a reason to keep looking. The questions that follow are about commitments that become harder and more expensive to reverse: production capacity, operational infrastructure, customer qualification, and the capital needed to sustain all of it.
Song’s analytical framework treats production readiness and customer adoption as interdependent parts of the same investment equation. Production capacity is hard to justify without credible demand, while customer interest has limited commercial value if the startup cannot produce and deliver the technology at an economically viable scale.
The Korean ecosystem has some structural advantages to help startups cross this valley, including government co-investment programs, access to shared labs in technology parks, and connections with large industrial conglomerates that can serve as early customers and production partners at the same time. But those advantages only materialize for companies that have already reached a minimum level of operational readiness.
For Korean deep-tech startups attracting larger volumes of early-stage funding, this offers a more useful definition of progress. Lab discovery remains essential, but investment conviction grows when the company can clearly explain how that discovery will become a repeatable product, navigate the customer’s buying process, and sustain a viable business beyond the lab. 🚀
In the end, what the market is making increasingly clear is that the filter for deep-tech is getting more demanding, and that is healthy. Startups that can show consistent production and real customer adoption are in a completely different position at the negotiating table with investors, and that advantage will only keep growing as the volume of available capital finds it increasingly difficult to identify where to truly bet with conviction.
