· 5 min read
As funding becomes more selective and successful exits harder to come by, the venture capital landscape is entering a phase of recalibration. Both startups and investors are rethinking priorities, moving away from the era of growth-at-all-costs and towards a more grounded focus on operational excellence. In this new environment, it's no longer enough to promise disruption: what matters now is the ability to solve real-world problems with precision, purpose, and a clear path to profitability.
In conversations with investors like Gianluca Dettori (Chairman, Primo Capital), Stephanie Dorsey (Founding Partner, esquared VC), Renat Heuberger (CEO and Co-Founder, Terra Impact Ventures), and Nate Kinch (Socioethnology Ethisist and previous impact investor, Trustworthy by Design), a new thesis emerges: the winners will be focused, data-driven, and impact-native—especially in how they leverage AI.
Practical AI is taking over
Investors are shifting away from hyped AI trends, now focusing on practical AI tools that address immediate operational challenges in sectors like agriculture, logistics, and healthcare.
“The most promising solutions leverage large language models to decode agricultural data inputs and agentic AI systems capable of independent execution,” says Gianluca Dettori, Chairman and General Partner at Primo Capital.
This reflects AI's shift from supportive tools to autonomous systems that make decisions, adapt to context, and reduce the need for human oversight
Stephanie Dorsey, Founding Partner at esquared VC, sees AI as a transformative force that goes beyond optimisation only:
“By leveraging AI, startups can redefine industries, unlock unprecedented efficiencies, and drive innovation that was previously unimaginable. This isn't just about doing things better; it's about doing things differently—creating new markets, revolutionising customer experiences, and addressing global challenges in ways that fundamentally change the game.”
That’s a market signal. From farm automation to health diagnostics, AI is moving from novelty to scalable, value-generating infrastructure.
Discipline is the new differentiator
The era of fast fundraising and “growth at all costs” is over. Capital is more selective, and investors are more demanding. Due diligence is longer and expectations higher. Only the most focused startups are making it through.
“Forget trying to be everything to everyone. Nail one thing, and nail it well,” says Gianluca Dettori.
This is pushing startups to go deep rather than wide, solving a specific problem for a specific customer in a specific market. That’s where trust, product-market fit, and competitive advantage are born.
Even within this tougher environment, there’s room for optimism. Startups that adapt (through leaner models, smart partnerships, and mission clarity) are building stronger, more resilient companies.
Circular thinking is the competitive edge
“By focusing on circular economy solutions, we can address resource scarcity and create sustainable business models,” says Renat Heuberger, CEO and Co-Founder of Terra Impact Ventures.
Circularity is becoming a key investment lens. Startups that focus on eliminating waste, minimising the use of virgin inputs, and implementing closed-loop systems are unlocking significant resource efficiencies while driving down capital costs. This approach is particularly impactful in industries like manufacturing, packaging, and infrastructure, where the potential for optimisation and long-term cost savings is substantial.
Combined with AI, circular models are becoming powerful platforms for systemic efficiency and climate resilience.
Ethics in AI: A startup imperative
The rise of autonomous AI demands a new level of responsibility.
“As we advance AI technologies, it’s crucial to ensure they are developed and deployed responsibly - with transparency, accountability, and fairness,” says Nate Kinch, impact investor now socio-technology ethicist at Trustworthy by Design.
By integrating ethical AI practices, startups can build trust with customers, regulators, and investors through several key strategies:
• Data privacy: Beyond compliance, prioritising data privacy builds customer trust and encourages more meaningful data sharing, which can enhance AI performance and personalisation.
• Explainability: Transparent AI decision-making processes empower users, fostering trust and loyalty. This transparency can also preempt regulatory scrutiny and public backlash.
• Fairness: Ensuring AI systems are fair and unbiased broadens market appeal and mitigates the risk of legal challenges and reputational damage. It also aligns with growing investor focus on ESG criteria.
By embedding ethical AI practices into their core operations, startups can achieve competitive differentiation, attracting discerning customers and investors who value responsible innovation.
Proactive ethical practices also ensure regulatory readiness, anticipating and aligning with evolving regulations to reduce future non-compliance risks and costs. Additionally, a commitment to ethical AI can attract top talent seeking socially responsible employers, ultimately creating a foundation of trust that drives sustainable growth, enhances reputation, and unlocks new opportunities in a world where technology and ethics are increasingly intertwined.
Case studies in practical AI
• A crop intelligence solution has increased farm yields by 30% through autonomous monitoring and predictive analysis (e.g. Taranis)
• An AI-driven supply chain tool reduced stockouts by 25% for major retailers, boosting sales and inventory efficiency (e.g. Llamasoft)
• In healthcare, AI systems are cutting diagnosis time and personalising treatments - improving outcomes while reducing costs (e.g. Tempus)
These are the tools getting funded: purpose-built, transformative, and metrics-driven.
What to know now
If you’re building in this market, here’s the playbook:
• Solve a real problem, and solve it well: Go narrow, go deep. Build for one customer type. Prove your value. Scale from strength
• Make AI work, not just impress: Focus on AI that drives measurable outcomes: saved time, reduced waste, increased margins. Show, don’t tell
• Design for circularity and efficiency: Think inputs, lifecycle, and waste from day one. Your unit economics and environmental footprint are now connected
• Lead with data and ethics: Track everything. Own your impact. Build trust through transparent AI governance and clear reporting
The road ahead: Focus wins
Looking into 2025, the ecosystem is leaner but stronger. Startups that combine execution discipline with technology that matters are raising money and building real businesses. Investors are doubling down on substance on startups that can prove impact, efficiency, and staying power.
“The best ideas will always find funding,” says Dettori. “It’s just about clarity of value, of market, of mission.”
In a world of complexity, the winners are doing one thing better than anyone else, using AI, ethics, and sustainability to get there faster.
illuminem Voices is a democratic space presenting the thoughts and opinions of leading Sustainability & Energy writers, their opinions do not necessarily represent those of illuminem.
