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Dutch startups raised $3.5B in 2024. Here are the 10 largest funding rounds - Related to dutch, nvidia, largest, rounds, blows

Dutch startups raised $3.5B in 2024. Here are the 10 largest funding rounds

Dutch startups raised $3.5B in 2024. Here are the 10 largest funding rounds

It’s been a great year for the Dutch startup ecosystem.

Venture capitalists have, so far, invested $ into Netherlands-based early-stage companies, . That makes 2024 Dutch tech’s second-best funding year ever, surpassed only by 2021.

Dutch startups have raised almost 50% more ($ cash this year than in 2023 — and. There are still two weeks to go. It’s a striking uptick, especially considering the rather muted funding environment in broader Europe. Which is on course for its worst year since 2020.

The Netherlands stands to be Europe’s fourth best-funded ecosystem for 2024. It ranks two places higher than last year, beating out Sweden and Switzerland. Unsurprisingly, the UK will clinch the top spot, with $17bn raised so far. Germany is second at $, while France is coming in at a close third with $.

So, with that in mind. Here are the 10 largest funding rounds that made 2024 such a lucrative year for the Dutch startup ecosystem. (Disclaimer: For this list, we count all early-stage companies headquartered in the Netherlands, not necessarily founded there).

This was a whopper. The startup, which builds full-stack AI infrastructure for tech firms, secured the equity in December in a deal led by Nvidia, Accel, and other blue-chip investors.

*(While Dealroom counts Nebius as a startup, they’re a public enterprise so we’re not so sure.).

The Dutch online supermarket unicorn bagged the funding in January to fuel its international expansion, as it looks to become a profitable grocery delivery service (which has proven bloody difficult for most).

The Rotterdam-based enterprise makes advanced tools for inspecting computer chips during manufacturing. Adding to the Netherlands’ wealth of semiconductor success stories.

Cha-ching! We have a new unicorn in the house. More accurately, in our house, seeing as Mews is based at TNW City in Amsterdam. The startup, which provides a cloud-based property management system (PMS) for the hospitality industry, raised $110mn in March at a valuation of over $ Then in September. It bagged another $100mn.

Cha-ching, again! DataSnipper, which makes AI-powered accounting tools, raised $100mn in February at a $1bn valuation making it the Netherlands’ second newly minted unicorn for 2024.

The biotech firm is developing treatments for inflammatory diseases. It will use the Series B funding to bring its lead product, CIT-013, into Phase 2a clinical trials.

Founded in 2022, Cradle uses generative AI to design and optimise proteins. Aiming to reduce the time and cost associated with protein engineering. The Amsterdam-based startup wants to put its software “into the hands of a million scientists.”.

The Eindhoven-based startup is developing chips, known as AI processing units (AIPUs), that enable computer vision and. Generative AI in devices like robots and drones. The funding round was led by Samsung’s venture arm. For a chip startup, that’s not a bad investor to have onboard.

Payt Software’s AI-powered platform streamlines the debt collection process through automated invoicing. Not exactly ‘sexy’ tech, but the startup’s product is used by over 13,000 businesses in the Netherlands alone so it must be doing something right.

Founded in 2019. Vico is developing therapies for severe neurological diseases. The corporation is the third health tech startup on this list and for good reason — the Netherlands has established itself as one of the world’s leading hubs for biotech and life sciences.

Overall, the Dutch startup ecosystem has proven its resilience and. Strength in 2024, achieving remarkable growth despite broader challenges in the European funding environment. Standout performances come from the sectors of biotech, AI, and semiconductors. As we look ahead, these funding successes position the country’s startups for even greater global impact going into 2025.

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Google DeepMind’s new weather forecaster blows away the competition

Google DeepMind’s new weather forecaster blows away the competition

Google DeepMind researchers have built an AI weather forecasting tool that makes faster and more accurate predictions than the best system available today.

Dubbed GenCast, the new model outperformed the ENS forecast. Widely regarded as the world leader, 97% of the time for predictions up to 15 days in advance. It was tested on over 1,320 weather scenarios, including tropical cyclones and heatwaves.

“Outperforming ENS marks something of an inflection point in the advance of AI for weather prediction,” Ilan Price, a research scientist at Google DeepMind. Told the Guardian. “At least in the short term, these models are going to accompany and be alongside existing, traditional approaches.”.

GenCast is a diffusion machine learning model. Similar to those used in generative AI for tasks like image or text creation. However, it’s uniquely adapted for weather prediction, trained on four decades of data from the European Centre for Medium-Range Weather Forecasts (ECMWF) — the agency behind ENS.

During the experiments. Researchers asked GenCast to generate a forecast for 2019. They then compared the results to the actual weather during that year as well as ENS’ predictions.

GenCast creates an ensemble of 50+ different predictions. Each showing a possible future scenario. This data helps authorities prepare for extreme weather events like hurricanes or wind farm operators enhanced predict power output days in advance.

The fancy name for this technique is probabilistic ensemble forecasting. It’s already the gold standard in traditional forecast systems. However, GenCast is taking things up a notch. The system can spit out predictions in far less time: 8 minutes, compared to hours for traditional models.

That’s because models like ENS run on massive supercomputers that have to crunch through millions of equations to make a prediction. By contrast, GenCast runs on a single Google Cloud TPU, a chip designed for machine learning. That’s because the AI has been trained, it’s “learnt” the data — it doesn’t have to go through it every single time it needs to make a forecast.

GenCast improves upon Deepmind’s GraphCast model unveiled last year. Other tech firms are also developing their own AI weather forecasters. Nvidia released FourCastNet in 2022, while Huawei launched its Pangu-Weather model in 2023.

So will AI replace traditional forecasting soon? Probably not. Models like GenCast still rely on data from traditional weather systems and models to train and calibrate their predictions. However, AI can certainly enhance current methods.

“The greatest value comes from a hybrid approach, combining human assessment, traditional physics-based models and AI-based weather forecasting,” Steven Ramsdale, chief forecaster at the UK’s Met Office, told the Financial Times.

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It’s been a great year for the Dutch startup ecosystem.

Venture capitalists have, so far, invested $ into Netherlands-based early-stage companie...

Netherlands strikes deal with Nvidia for AI supercomputing hub

Netherlands strikes deal with Nvidia for AI supercomputing hub

The Dutch government has reached a deal with Nvidia to supply hardware and expertise for the construction of an AI facility in the Netherlands.

The facility will centre around an AI supercomputer aimed at accelerating research and development, as the country pushes to further digitalise its economy. .

The Dutch Minister for Economic Affairs Dirk Beljaarts is currently in Silicon Valley, where he is meeting with executives at Nvidia. The world’s second most valuable enterprise, Nvidia is a world leader in graphics processing units (GPUs) for artificial intelligence applications.

“Before a shovel can go into the ground, we need to be sure that the required knowledge and. Hardware is available,” stated Minister Beljaarts. “Today the Netherlands has taken an key step together with Nvidia. This brings the construction of a Dutch AI facility a lot closer.”.

The ministry didn’t provide a timeline or specific details for the AI facility. Nvidia declined our request for comment.

The Netherlands, which ranked in the top 10 most digitised countries globally last year. Is pushing to position itself as a leader in AI. In January last year, the government ringfenced over €200mn to boost local investment in the technology.

“Asia and the US have taken the lead [in AI] and Europe will have to catch up,” mentioned the former economic affairs minister Micky Adriaansens at the time.

Key priorities of the government’s strategy include fostering AI talent, building infrastructure. Ensuring safe applications, and enabling cooperation through entities like the Dutch AI Coalition. Last year, the Benelux nation also embarked on building its own “safer” large language model (LLM), called GPT-NL.

To execute its plans. The Netherlands is going to need more computing power to train AI machines. Aside from Nvidia, Beljaarts is also meeting with chipmaker AMD. The Minister is also in California to “strengthen relations” between Dutch and American technology companies more broadly, the government noted.

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Market Impact Analysis

Market Growth Trend

2018201920202021202220232024
23.1%27.8%29.2%32.4%34.2%35.2%35.6%
23.1%27.8%29.2%32.4%34.2%35.2%35.6% 2018201920202021202220232024

Quarterly Growth Rate

Q1 2024 Q2 2024 Q3 2024 Q4 2024
32.5% 34.8% 36.2% 35.6%
32.5% Q1 34.8% Q2 36.2% Q3 35.6% Q4

Market Segments and Growth Drivers

Segment Market Share Growth Rate
Machine Learning29%38.4%
Computer Vision18%35.7%
Natural Language Processing24%41.5%
Robotics15%22.3%
Other AI Technologies14%31.8%
Machine Learning29.0%Computer Vision18.0%Natural Language Processing24.0%Robotics15.0%Other AI Technologies14.0%

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity:

Innovation Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity AI/ML Blockchain VR/AR Cloud Mobile

Competitive Landscape Analysis

Company Market Share
Google AI18.3%
Microsoft AI15.7%
IBM Watson11.2%
Amazon AI9.8%
OpenAI8.4%

Future Outlook and Predictions

The Dutch Startups Raised landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:

Year-by-Year Technology Evolution

Based on current trajectory and expert analyses, we can project the following development timeline:

2024Early adopters begin implementing specialized solutions with measurable results
2025Industry standards emerging to facilitate broader adoption and integration
2026Mainstream adoption begins as technical barriers are addressed
2027Integration with adjacent technologies creates new capabilities
2028Business models transform as capabilities mature
2029Technology becomes embedded in core infrastructure and processes
2030New paradigms emerge as the technology reaches full maturity

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:

Time / Development Stage Adoption / Maturity Innovation Early Adoption Growth Maturity Decline/Legacy Emerging Tech Current Focus Established Tech Mature Solutions (Interactive diagram available in full report)

Innovation Trigger

  • Generative AI for specialized domains
  • Blockchain for supply chain verification

Peak of Inflated Expectations

  • Digital twins for business processes
  • Quantum-resistant cryptography

Trough of Disillusionment

  • Consumer AR/VR applications
  • General-purpose blockchain

Slope of Enlightenment

  • AI-driven analytics
  • Edge computing

Plateau of Productivity

  • Cloud infrastructure
  • Mobile applications

Technology Evolution Timeline

1-2 Years
  • Improved generative models
  • specialized AI applications
3-5 Years
  • AI-human collaboration systems
  • multimodal AI platforms
5+ Years
  • General AI capabilities
  • AI-driven scientific breakthroughs

Expert Perspectives

Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:

"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."

— AI Researcher

"Organizations that develop effective AI governance frameworks will gain competitive advantage."

— Industry Analyst

"The AI talent gap remains a critical barrier to implementation for most enterprises."

— Chief AI Officer

Areas of Expert Consensus

  • Acceleration of Innovation: The pace of technological evolution will continue to increase
  • Practical Integration: Focus will shift from proof-of-concept to operational deployment
  • Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
  • Regulatory Influence: Regulatory frameworks will increasingly shape technology development

Short-Term Outlook (1-2 Years)

In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:

  • Improved generative models
  • specialized AI applications
  • enhanced AI ethics frameworks

These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.

Mid-Term Outlook (3-5 Years)

As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:

  • AI-human collaboration systems
  • multimodal AI platforms
  • democratized AI development

This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.

Long-Term Outlook (5+ Years)

Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:

  • General AI capabilities
  • AI-driven scientific breakthroughs
  • new computing paradigms

These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.

Key Risk Factors and Uncertainties

Several critical factors could significantly impact the trajectory of ai tech evolution:

Ethical concerns about AI decision-making
Data privacy regulations
Algorithm bias

Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.

Alternative Future Scenarios

The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:

Optimistic Scenario

Responsible AI driving innovation while minimizing societal disruption

Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.

Probability: 25-30%

Base Case Scenario

Incremental adoption with mixed societal impacts and ongoing ethical challenges

Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.

Probability: 50-60%

Conservative Scenario

Technical and ethical barriers creating significant implementation challenges

Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.

Probability: 15-20%

Scenario Comparison Matrix

FactorOptimisticBase CaseConservative
Implementation TimelineAcceleratedSteadyDelayed
Market AdoptionWidespreadSelectiveLimited
Technology EvolutionRapidProgressiveIncremental
Regulatory EnvironmentSupportiveBalancedRestrictive
Business ImpactTransformativeSignificantModest

Transformational Impact

Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.

The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.

Implementation Challenges

Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.

Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.

Key Innovations to Watch

Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.

Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.

Technical Glossary

Key technical terms and definitions to help understand the technologies discussed in this article.

Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.

Filter by difficulty:

generative AI intermediate

algorithm

computer vision intermediate

interface

API beginner

platform APIs serve as the connective tissue in modern software architectures, enabling different applications and services to communicate and share data according to defined protocols and data formats.
API concept visualizationHow APIs enable communication between different software systems
Example: Cloud service providers like AWS, Google Cloud, and Azure offer extensive APIs that allow organizations to programmatically provision and manage infrastructure and services.

platform intermediate

encryption Platforms provide standardized environments that reduce development complexity and enable ecosystem growth through shared functionality and integration capabilities.

large language model intermediate

API

machine learning intermediate

cloud computing