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Spanish Deep Tech Ecosystem, Startup Partnerships – Selected

In my previous articles we were tackling the topics of the challenges the Spanish Deeptech faces and how we can solve these challenges by strengthening the Spanish Deeptech Ecosystem.

Now what’s next?

Deeptech startups face diverse challenges from funding to market access and technical / business expertise, among others. In order to access the resources they need, these startups rely on several stakeholders to help them address these specific needs.

Such collaborations are especially important for Deeptech startups since they find themselves at the crossroads of fundamental research and industrial application.

Therefore, before jumping into designing a platform for corporates to collaborate with Deeptech startups, let’s take a deeper look at what Deeptech startups look for when establishing corporate partnerships?

The following information is taken from interviews with various Spanish universities (UPC, UPF, IQS, UB, and UAB), some Spanish SMEs, some Deeptech experts and the Boston Consulting Group – Henderson Institute partnership with Hello Tomorrow reports (“The Dawn of the DeepTech ecosystem“;  “From Tech to DeepTech“).

In order to understand what Deeptech startups look for when establishing corporate collaboration, we first need to understand why Deeptech startup look to establish corporate partnerships.

Peculiarities of Deeptech startups

Deeptech innovations benefit from technological advances that often make them slower and more expensive to get off the ground than digital startups:

(a) Strong research base: Deeptech startups highly depend on fundamental research and advanced R&D. The latter combines a steady set of advanced skills, knowledge and infrastructure that lengthens the product time-to-market.

(b) Massive industrialization process: most products in the field are often using advanced materials, resources and technology that require high industrial skills, manufacturing and scale.

(c) Capital-intensive needs: in order to do fundamental research and be able to mass produce, Deeptech startups need substantial investment capacity over a specific period of time.

(d) Commercial application: as products are typically based on R&D, most of these products are in a “yet-to-be-defned” commercial application and therefore, need to open the doors to new markets that have yet to be created.

In that way, the time-to-market for Deeptech startups is usually longer than other startups because their products are based on a new technology that require longer development time, larger investment needs and “yet-to-be-defined” commercial applications.

The technology risk and complexity arise from the fact that Deeptech startups often use technologies at a low technology readiness and, after proving a technology in the lab, they are still facing a long road to turn the product into a demonstrated and tested solution.

In addition, stakeholders don’t properly anticipate the heavy demands on Deeptech startups and therefore the startups underestimate the time required to reach the market.

This often leads Deeptech startups to view corporates as the preferred partner to support the full scope of their needs mainly for market access and technical knowledge. Although corporate funding is not at the top of startups’ expectations, corporates usually offer it. That said, startups can still find themselves with many challenges when building collaboration with corporates.

Obstacles faced when establishing corporate partnerships  

Mid-stage Deeptech startups are often the most interested in building corporate partnerships and roughly half will succeed in establishing at least one partnership.

However, the latter encounter four key obstacles:

(a) Risk of misunderstanding on vision and objectives.

(b) Misalignment of timing and processes.  

(b) Lack of confidence in the technology and/or its maturity.

(c) Fear of lacking agility and reactivity (i.e. bureaucracy).

Therefore, establishing partnerships is easier through indirect recommendation at early stages because that arrangement enables both parties to build trust in the relationship and engage in free discussion without considering any immediate commercial application.

Type of collaboration that Deeptech startups look for with corporates

As Deeptech startups progress from a fundamental research idea to a commercial success, they go through different stages of development.

Based on the market readiness and the technology maturity level of the startup, the latter has its own needs, resources and preferred partners to collaborate with.

Therefore, we can identify four types of Deeptech startups looking to establish corporate partnerships:

(1) Potential wins are Deeptech startups that have a commercial-ready product and a market that is ready to adopt the technology. The immediate challenge is to scale up and initiate large production volumes. Therefore, they need fresh funding, market access and talent. In order to develop this customer base and the distribution network, these startups often turn to corporates although only 25% expect to get funding out of this collaboration. These startups expect to get visibility, credibility, business and technical knowledge.

(2) Demand bets are Deeptech startups with a product that is mature enough to be launched but doesn’t yet have a broad commercial application. Their main challenge is to identify and build a market for their innovation. They often lack a distribution network and face market resistance to change. Although funding is an important aspect, market access and business knowledge are their crucial resource needs for which they look to build synergies with corporates.

(3) Development bets are Deeptech startups that have identified a market opportunity and defined a value proposition. They are developing a technology to deliver but haven’t yet creation a market-ready product. They are looking to access technical expertise and overcome technological uncertainty. In order to cover this challenge they look to establish collaboration with corporates especially as research partnerships to share the costs and de-risk the R&D expenses. In that sense they are able to accelerate the development of the product.

(4) Technology bets are Deeptech startups that have identified a promising technology that lacks a market application yet. Their main goal is to develop a viable product and ensure that it will fit a market need. The crossroad these startups find themselves at are long development time and technological uncertainty. Obtaining corporate knowledge and support is extremely difficult as technological uncertainty makes funding risky. Therefore, these startups look to work on both technology / product development and market identification in order to reach collaboration with corporates.


As a general trend, corporate-startup partnerships are becoming more prevalent however, misalignments, bureaucracy and lack of technological confidence amongst others are still creating challenging obstacles to overcome.

Understanding all interests and needs is essential in order to build a platform that best nurtures collaboration between corporates and startups.

How can we build it?

Stay tuned for Part 4 of “How to build the Spanish Deeptech Ecosystem” 😉

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London-based AI laboratory Ineffable Intelligence has emerged from stealth with a $1.1 billion seed round at a $5.1 billion post-money valuation, the company confirmed on 27 April 2026. The financing is the largest seed round ever raised by a European company and one of the largest first-money-in rounds in the global history of artificial intelligence. The round was co-led by Sequoia Capital and Lightspeed Venture Partners. Participating investors included Nvidia, DST Global, Index Ventures, Google, and the UK Sovereign AI Fund, the British government’s recently established vehicle for backing strategic AI capacity on home soil. A bet on a different path to general intelligence Ineffable Intelligence was founded in 2025 by David Silver, the former Vice President of Reinforcement Learning at Google DeepMind and the principal architect of AlphaGo, AlphaZero and AlphaStar. He is joined by three further DeepMind alumni: Wojciech Czarnecki, Lasse Espeholt and Junhyuk Oh. All four have spent the past decade at the frontier of reinforcement learning research, the discipline behind some of the most consequential demonstrations of machine learning over the past ten years. The company describes its objective as building a “superlearner” — an AI system capable of acquiring knowledge directly from its own experience rather than from human-generated text or imagery. “Our mission is to make first contact with superintelligence,” Silver said in a statement accompanying the launch. “We are creating a superlearner that discovers all knowledge from its own experience, from elementary motor skills through to profound intellectual breakthroughs.” The framing is a deliberate departure from the dominant industry trajectory. Most leading AI laboratories, including OpenAI, Anthropic and Google DeepMind itself, have built large language models trained primarily on the corpus of the internet, then refined that training with human feedback. Ineffable’s wager is that the marginal returns on scaling text-based pretraining are diminishing and that the next leap in capability will come from agents that learn endlessly from the consequences of their own actions, in much the same way AlphaZero learnt the game of Go without studying any human matches. Why $1.1 billion at seed The size of the round is unusual even by the inflated standards of the 2026 AI capital cycle. Two factors appear to explain it. First, frontier reinforcement learning at the scale Ineffable describes is computationally extraordinarily expensive: the company will need to operate vast simulation environments and train very large models against them, an undertaking that consumes capital at a rate closer to physical R&D than to traditional software. Second, the round signals a strategic move by Europe’s investor and policy ecosystems to retain the most ambitious AI researchers on the continent. The presence of the UK Sovereign AI Fund alongside Sequoia, Lightspeed and Nvidia is the clearest expression of that intent. The British government has publicly framed the investment as a bet on breakthrough AI that “can discover new knowledge”, positioning the country as a willing co-investor in domestic frontier laboratories. For Ineffable, the implication is access not only to capital but to compute, regulatory engagement and the still-resilient academic talent base around UCL, Oxford, Cambridge and Imperial. Founder pledge of historic scale Alongside the funding announcement, Silver disclosed that he is committing 100 per cent of any personal proceeds from his Ineffable equity to charity via the Founders Pledge network — described by the organisation as the largest pledge in its history. At the round’s $5.1 billion valuation, that commitment could ultimately exceed several billion dollars if the company succeeds. It is a meaningful gesture in a sector where the reputational stakes around concentrated AI wealth are escalating, and one likely to be referenced in subsequent founder-led commitments. Implications for the European AI landscape Ineffable’s emergence reshapes the European AI map in three concrete ways. It establishes London as the home of the continent’s largest-ever seed-stage company, complicating Paris’s recent narrative of frontier-AI primacy after Mistral’s earlier rounds. It validates a thesis — that reinforcement learning, not transformer scaling, is the next frontier — that has lately been losing capital share to language-model incumbents. And it confirms that the UK government is now willing to act as a balance-sheet co-investor in domestic AI laboratories, a posture much closer to the French model than to the predominantly grant-based regimes elsewhere in Europe. The execution risk is non-trivial. Reinforcement learning at frontier scale has historically required years of careful environment design before producing competitive systems, and Ineffable’s “first contact” framing sets a high bar against which it will be judged. But for now, with a billion dollars on the balance sheet, four of the discipline’s most accomplished researchers in the founding team and a sovereign co-investor at its back, Ineffable Intelligence is the most heavily resourced new entrant in the European AI cycle. Sesamers covers European fundraising rounds across deeptech, fintech and AI. Source: tech.eu.

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