How DeepSeek Rewrote the Rules Venture Capitalists

Between 2015 and 2024, venture capital funding for AI startups grew rapidly. In 2024, global VC funding for AI companies reached $131.5 billion, a 52% increase from the previous year. In contrast, funding for other startups dropped by 10% during the same period.

In the fourth quarter of 2024, AI startups received 50.8% of total global VC funding, nearly doubling their share compared to the same period in 2023.

This was caused by large investments, including OpenAI’s $6.6 billion funding round in October 2024, which valued the company at $157 billion. attracted by advances in machine learning, automation, and data tools.

Early investors often backed startups with smart researchers and exciting tech demos, even if their plans to make money were unclear.

Companies like Mistral and Anthropic, known for building powerful language models, grew quickly by focusing on innovation first and real-world uses later.

In January 2025, DeepSeek broke through. This AI platform worked faster, cheaper, and at a larger scale than earlier tools, saving businesses up to 40% on costs. Almost overnight, VCs stopped funding startups with vague promises.

They started asking: Can your AI beat DeepSeek? How will you protect user data? Who will actually pay for this? Big investors like Sequoia and Andreessen Horowitz shifted focus to startups that solved clear problems, not just those with flashy tech.

This article breaks down how VC priorities changed before and after DeepSeek’s 2025 breakthrough. We’ll explain what investors ignored in the past (like ethics or practical uses) and what they care about now.

Using real examples, like Mistral moving from open-source experiments to business solutions, we’ll share practical steps for founders to build AI companies that win trust (and funding).

The Pre-DeepSeek Era: What VCs Used to Look for in AI Startups

Technical Expertise and Innovation

VCs bet on startups with cutting-edge algorithms and unique technology that competitors couldn’t copy.

Teams stacked with PhDs or engineers from giants like Google or OpenAI grabbed attention. For example, Mistral’s early funding came from its team’s reputation in AI research—even before they had a clear product.

Market Potential and Use Cases

Investors wanted AI solutions that could scale massively and solve real problems. Startups promising to “transform healthcare” or “fix supply chains” got funded if they showed a big addressable market. Few asked for proof of customer demand—VCs assumed “build it, and they will come.”

Team Composition and Execution Capability

VCs looked for founders who blended tech skills with business savvy. A Stanford AI researcher paired with a ex-McKinsey operator was the golden ticket. Past wins mattered: founders who sold previous startups or shipped products at FAANG companies had an edge.

Early Traction and Proof of Concept

Startups needed just enough traction to hint at future success. A pilot with a Fortune 500 company or 10,000 free users often sealed the deal. Metrics like revenue or profit took a backseat to hype—growth charts pointing “up and to the right” were enough.

Competitive Landscape

VCs checked if a startup’s AI was truly unique. For instance, Anthropic stood out by focusing on AI safety while others chased performance. Investors also asked: “Can others copy this easily?” Startups with patents or complex tech stacks (like custom AI chips) won more funding.

The Post-DeepSeek Era: How VC Criteria Have Changed

Shift from Hype to Practicality

VCs no longer care about buzzwords like “AI” or “machine learning.” Today, they ask: Does your product save time or money for real users?

For example, a startup automating hospital billing gets funded faster than one hyping “AI-powered healthcare.” Investors demand proof of ROI, like a 30% drop in customer support costs, before writing checks.

Increased Emphasis on Ethical AI and Safety

After scandals like biased hiring algorithms, VCs now audit startups for fairness and transparency.

They ask: How do you prevent your AI from harming users? Can regulators shut you down? Startups like Anthropic now highlight “ethical guardrails” in their pitches, not just technical specs.

Data as a Core Asset

VCs treat data like gold. They ask: Is your data unique, clean, and legal? A startup analyzing satellite images for crop yields needs millions of geotagged photos, not just clever algorithms. Investors also check if startups comply with laws like GDPR or face fines, like the $10M penalty slapped on a facial recognition firm in 2024.

Business Model Viability

“Cool tech” isn’t enough. VCs ask: Who pays you, and how much? Startups must show plans to monetize, like charging per API call or subscription fees.

For example, Mistral pivoted from free open-source models to selling custom LLMs to banks, proving they could turn a profit.

The Role of DeepSeek in Shaping New Benchmarks

DeepSeek’s 2025 breakthrough forced startups to aim higher. Investors now ask: Is your AI faster/cheaper than DeepSeek? Can you scale globally? 

Startups like Glean (a DeepSeek competitor) won funding by cutting energy costs by 50% while matching its performance. VCs want Olympic-level athletes, not hobbyists.

How VCs Ensure Safety After Investing in AI Startups

Investing in AI startups comes with both high potential rewards and significant risks. With the rapid growth of AI, VCs need strong safeguards to protect their investments and ensure long-term success. Here’s how they can do that:

Rigorous Due Diligence

Before investing, VCs conduct detailed technical audits to assess the AI model’s reliability, scalability, and potential risks. They examine:

• Algorithm performance to ensure accuracy and efficiency.
• Bias and fairness to avoid ethical or legal challenges.
• Data security to prevent privacy violations.
• Scalability to confirm the startup can handle growth.

Some VCs also hire third-party AI experts to verify the startup’s claims and check for potential weaknesses.

Governance and Oversight

Once they invest, VCs demand clear governance structures to manage risks. Many insist on:

• Board representation to oversee strategy and compliance.
• Regular progress reports on key performance metrics.
• Risk management policies to prevent misuse or failures.

Some investors require startups to follow AI safety frameworks, ensuring they comply with industry regulations and ethical guidelines.

Alignment with Founders on Long-Term Goals

Strong investor-founder relationships are key to startup success. VCs ensure:

• Founders share their vision for responsible AI growth rather than chasing short-term profits.
• Clear milestone-based funding keeps teams accountable for progress.
• Transparent communication helps investors stay informed and engaged.

By aligning on long-term success over rapid expansion, both parties reduce risks and improve decision-making.

Diversification of Investments

VCs rarely put all their money into a single AI company. Instead, they:

• Spread investments across multiple AI startups to balance risk.
• Invest in both application-layer and infrastructure-layer AI to ensure exposure to different parts of the AI value chain.
• Look for partnerships and collaborations between startups to build stronger AI ecosystems.

How Founders Can Ensure VC Money Is Not Wasted

When VCs invest in AI startups, they expect efficient use of funds and a clear path to growth and profitability. Founders need to use resources wisely, build a strong business model, and keep investors updated. Here’s how they can ensure that VC money is not wasted:

Clear Roadmaps and Milestones

Founders must have a well-defined plan for how they will use funding. They should:

• Set clear goals for product development, scaling, and revenue generation.
• Break down milestones into short-term and long-term targets.
• Regularly update investors on progress and challenges.

A structured roadmap helps track progress and ensures investors stay confident in the startup’s vision.

Focus on Unit Economics and Profitability

AI startups should prioritize financial sustainability instead of only chasing growth. To do this, they must:

• Track key metrics like customer acquisition cost (CAC) and lifetime value (LTV).
• Optimize resources to reduce unnecessary spending.
• Develop a clear pricing model that ensures profitability in the long run.

VCs look for startups that understand their costs and have a plan to generate revenue.

Building a Strong Team and Culture

A company’s success depends on its people. Founders must:

• Hire skilled professionals who can drive innovation and execution.
• Create a culture of accountability, where team members take ownership of their roles.
• Retain top talent by offering career growth, equity, and a positive work environment.

A strong team increases investor confidence and ensures AI products are built and scaled effectively.

Customer-Centric Approach

A successful AI startup must build products that solve real customer problems. Founders should:

• Engage with customers regularly to understand their needs.
• Collect and analyze feedback to improve their AI models.
• Validate the product-market fit before scaling aggressively.

By focusing on customer value, startups can increase adoption rates and generate sustainable revenue, proving to investors that their money is well spent.

The Evolution of Conversational AI: Mistral, Anthropic, and Other LLM Players

AI companies have changed the way they present their innovations to investors and the public. Before DeepSeek, startups like Mistral and Anthropic focused on breakthrough technology and promised to transform industries. Now, the focus has shifted toward practical applications, ethical concerns, and real-world impact.

Pre-DeepSeek Narratives

Before DeepSeek, AI companies mainly emphasized:

• Cutting-edge technology – Startups highlighted their advanced AI models and algorithms.
• Theoretical potential – AI leaders spoke about long-term benefits rather than immediate impact.
• Industry transformation – Many companies claimed they would revolutionize multiple industries without clear short-term applications.

Investors were excited about AI’s potential, but many startups struggled to deliver tangible results quickly.

Post-DeepSeek Narratives

After DeepSeek, AI companies started to shift their messaging to focus on:

• Practical applications – Investors now demand real-world use cases rather than just promising future impact.
• Ethical AI and transparency – Companies must show how they handle AI bias, security, and compliance.
• Measurable results – AI firms now highlight specific achievements, cost savings, and efficiency improvements rather than just technology itself.

This shift means that VCs now prioritize AI startups that show real impact and scalability.

Lessons Learned from Early LLM Players

The early days of LLMs taught investors key lessons:

• Balancing innovation with responsibility – AI startups must develop groundbreaking models while ensuring ethical AI use.
• Building sustainable business models – Companies can no longer rely on hype; they must demonstrate clear revenue streams.
• Scaling AI while maintaining quality – AI models should be reliable and adaptable, not just powerful.

Application Layer vs. Infrastructure Layer: What VCs Look For

Venture capitalists evaluate AI startups based on where they fit in the AI ecosystem. Some startups build AI applications that solve specific problems, while others focus on AI infrastructure that powers multiple applications. VCs need to understand the differences and investment potential in both layers.

Investing in Application-Layer AI Companies

Application-layer AI companies develop AI-powered solutions for businesses and consumers. These startups focus on:

• Solving real-world problems – AI is used for healthcare diagnostics, customer support automation, fraud detection, etc.
• Revenue generation – They sell AI products or services directly to businesses or users.
• User adoption and market demand – VCs assess customer acquisition cost (CAC), lifetime value (LTV), and retention rates.

Example: An AI startup providing automated legal document review must show that its tool improves efficiency and reduces legal costs for law firms.

Investing in Infrastructure-Layer AI Companies

Infrastructure-layer AI companies build platforms, tools, and frameworks that support AI development. They focus on:

• Scalability and interoperability – Their solutions need to work across multiple industries.
• Developer adoption – The success of infrastructure companies depends on how many businesses use their tools.
• Long-term impact – These companies power other AI applications, making them essential for industry growth.

Example: A startup providing AI model training infrastructure must prove that its platform helps businesses develop and deploy AI models more efficiently.

Balancing Both Layers in a Portfolio

VCs often invest in both application-layer and infrastructure-layer AI startups to reduce risk and maximize returns.

• Application-layer investments offer faster returns if the product gains early adoption.
• Infrastructure-layer investments provide long-term stability and power multiple industries.

VCs need to evaluate business models, market demand, and technological advantages carefully before investing in AI startups at any layer.

Conclusion

The AI investment landscape is evolving, and VCs must adapt to new benchmarks set by innovations like DeepSeek. In the past, Vs focused on technical expertise, early traction, and market potential, but now they prioritize practical applications, ethical AI, and sustainable business models.

To succeed, founders must ensure efficient use of VC funding by setting clear milestones, maintaining strong unit economics, and focusing on customer-driven growth. At the same time, VCs can protect their investments through rigorous due diligence, governance structures, and diversification across AI startups.

The distinction between application-layer and infrastructure-layer AI startups is also crucial for portfolio diversification. While applications generate faster returns, infrastructure provides long-term scalability.

As AI continues to evolve, collaboration between investors and startups will be key to driving responsible innovation and maximizing impact in the post-DeepSeek era.

Building a successful AI company starts with high-quality labeled data. Labellerr helps AI teams streamline data annotation with automation, AI-assisted labeling, and scalable workflows. If you're an AI startup looking to accelerate your product launch, explore Labellerr’s solutions today!

FAQs

What are the key risks VCs face when investing in AI startups?

VCs face risks such as unproven business models, high development costs, regulatory uncertainty, and AI ethics concerns. Ensuring strong governance, technical due diligence, and market validation can mitigate these risks.

How has DeepSeek changed the way VCs evaluate AI startups?

DeepSeek has shifted VC focus from pure innovation to practicality. Investors now prioritize scalability, ethical AI, and real-world applications over just technological breakthroughs. Startups must prove their market fit and long-term sustainability to attract investment

How can VCs ensure their investment is being used effectively by AI startups?

VCs should set clear milestones, demand transparency in financial and technical reporting, and maintain active involvement through board seats. Startups should show capital efficiency, customer validation, and steady progress toward profitability.

What are the biggest challenges AI startups face today, and how should VCs evaluate them?

AI startups struggle with data availability, regulatory compliance, high compute costs, and competitive market dynamics. VCs should assess how well a startup handles data privacy, cloud costs, and differentiation from larger AI players like OpenAI and DeepSeek before investing.