Venture capitalists are at the forefront of many technological revolutions as they scour and support startups working on cutting edge products and services. The AI Innovator recently caught up with Dean Mai, a partner at Myriad Venture Partners, to talk about the AI startup landscape and what’s hot right now.
The following is an edited transcript of that interview.
The AI Innovator: Tell me about Myriad Venture Partners. What makes you unique from other VCs?
Dean Mai: Myriad Venture Partners is an early-stage venture capital firm shaping the future of business solutions. We focus on investing in future-forward industries like AI, clean technology, and B2B software — where we see a strong interplay between technological progress and real-world impact.
The Myriad Model, our distinct investment approach, draws on decades of sector insight from our leadership team and an extensive network of corporate and financial partners we have cultivated over the years. Supported by the expertise and connections of our executive advisory board, we go beyond providing capital.
At its heart, the Myriad Model is about building relationships that enable founders to tackle immediate strategic hurdles, whether forming the right industry partnerships or adapting business models to evolving market conditions. By actively connecting entrepreneurs, industry stakeholders, and co-investors, we aim to create an open, collaborative ecosystem that fosters sustainable growth.
What are your main criteria for choosing an AI startup to invest in? Tell me about your approach. Does gut feeling play into your choice as well?
At Myriad, we start by considering whether the team has a unique insight and grasp of the technical fundamentals as well as a clear roadmap to build and scale something genuinely transformative. We want to see a path from core technology to a tangible solution that customers need. Additionally, we pay attention to how quickly and thoughtfully they iterate: Are they adaptable enough to stay ahead of a fast-moving field? Do they have the resilience and ingenuity to navigate complexity?
As for gut feeling — it plays a role, perhaps as a final tiebreaker rather than a starting point. Ultimately, we use rigorous analysis and human judgment to back the right teams and ideas. As our decisions are based on data and market potential, our open model and proximity to seasoned industry operators at every level allow us to leverage the expertise and insights in real-time when assessing a startup’s ability to scale and make a meaningful impact.
How do you evaluate the founders of a startup? What do you look for? What are the red flags?
I look for founders who deeply understand the technology they’re using to build their products. Do they grasp what’s possible now and clearly understand what it will take to get where they want to go? I’m also looking at how they think about scaling a company and product, whether they have a plan that acknowledges real constraints, and whether they have spoken to customers instead of believing the technology will find its audience.
Another crucial factor is how well they handle uncertainty and potential setbacks and their willingness to iterate. AI projects often run into unexpected hurdles, so I’m interested in founders who have thought through different scenarios and can balance enthusiasm with critical thinking.
Red flags show up when founders gloss over the product’s technical and scaling complexity or make unrealistic claims without evidence or a defined roadmap. If they’re dismissive of practical challenges, lack a clear vision of what it takes to deliver, or think a good idea and funding will solve it, I’m wary.
Do you focus on a particular area of AI? Or the startup stage?
I’m drawn to companies leveraging AI to improve business processes fundamentally — streamlining repetitive workflows, cutting down unnecessary complexity, and ultimately boosting operational efficiency. We’re talking about startups that don’t just automate tasks but rethink the workflow, identifying where value is lost and introducing smarter decision-support tools or more adaptive process flows.
Following our model, Myriad doesn’t limit itself strictly to the startup stage; early ventures with a compelling plan and a credible path to execution can be just as interesting as later-stage companies already delivering measurable efficiency gains. The common thread is that they’re using AI to move beyond small, incremental gains and towards meaningful changes in how businesses operate — reducing resource waste, optimizing team output, and unlocking new growth opportunities. That’s where we see the real potential.
What are the most exciting areas of AI right now as you survey the startup landscape?
There’s a clear shift right now toward building AI agents that can handle complex business processes. Rather than just experimenting with large language models, startups are developing systems that can integrate with existing software, reason about workflows, and take meaningful actions. These agents aren’t just chatbots — they’re becoming operational tools that handle tasks like lead qualification, invoice processing, or compliance checks, often by chaining multiple models together or leveraging APIs to shape context and responses.
In parallel, there’s growing interest in AI infrastructure and developer tooling. Companies are providing platforms and frameworks that help teams build, deploy, and monitor AI systems at scale, as well as layer in controls for security and compliance. This involves a range of solutions — everything from improved model management and versioning to integrated workflows that fit directly into the enterprise stack. It’s a response to the practical demands of shipping reliable AI-enabled products.
Lastly, we’re seeing more experimentation at the intersection of LLMs and emerging modalities, incorporating structured databases, simulation environments, or geospatial data. Rather than treating language models as standalone systems, startups are combining them with other data sources or integrating them into new interfaces and interactive applications. These combinations aim to transform how people solve problems by providing richer, more context-aware AI solutions that go beyond text and can support decision-making in more nuanced ways.
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