How can startups win in this AI-driven venture landscape?
The venture capital landscape for startups has shifted over the past 12-18 months, driven largely by the explosive rise of native AI companies – startups built from the ground up around AI as a core capability rather than an add-on.
In a recent conversation with Startups Magazine, Jason Bennett, VP, Worldwide Startups and Venture Capital at AWS, shared how these changes are reshaping what investors look for, how startups can stand out, and where the biggest opportunities lie.
The rise of native AI and targeted use cases
According to Bennett, one of the most important trends is the growth of native AI companies that are either building foundational models or creating agentic AI systems.
“The biggest trends that we’ve seen … that is the growth of native AI companies … from a VC perspective, you’ll see that that’s where a great deal of the investment is going today.”
What’s changed more recently is where this investment is flowing. Early in the AI wave, capital gravitated toward broad foundational models. Now, investors are favouring highly targeted applications – for example, startups focused on genomic or protein analysis, like Latent Labs, or those building AI agents for specific workflows, such as Lovable.
This shift signals a clear message to startups: investors want focus, depth, and domain-specific value, not just generic AI capabilities.
Generative AI as a runway extender
For early-stage companies, perhaps the most compelling benefit of AI is its impact on speed and efficiency. Bennett emphasised that Generative and agentic AI are becoming powerful levers to extend runway and compress product timelines.
“It’s allowing them to actually extend their runway and to be able to deliver much faster … the faster that they can build to MVP, the sooner they can get context and insight back from their customers.”
By using developer tools like AWS’ Kiro, startups can behave like much larger organisations – shipping features faster, iterating more quickly, and learning from customers earlier.
Founders need to realise that AI is not just a product feature – it’s an operational advantage. Those who embed AI deeply into their development, customer support, and internal workflows can do more with less, which matters enormously in capital-constrained environments.
What investors want now
While growth has always been a key metric, the speed and scale now achievable with AI have fundamentally changed investor expectations.
“You have some companies that are able to get to hundreds of millions or even billions of dollars [in ARR] incredibly quick … Cursor did it in 18 months,” notes Bennett.
However, he stressed that “growth at all costs” is no longer the default mentality. Because many AI-native startups rely on foundational models – whether building their own or leveraging services like Amazon Bedrock – they must be acutely aware of unit economics: “If you’re leveraging a foundational model as an underpinning of your business … suddenly you’re a bit more tuned to thinking about the economics of what you’re building, and growth at all costs is maybe not necessarily what you want.”
Investors are looking for startups that can:
- Scale extremely quickly
- Maintain sustainable economics, not just impressive top-line growth
- Demonstrate defensibility, often through proprietary data, domain expertise, or vertical focus
Bennett points to companies like Layton Labs as examples of defensibility through industry-specific data and insight: “They’re very targeted in being able to support protein and drug development … they’re building that defensibility because they have a set of insight and perspective that isn’t going to be known by just your frontier labs.”
How startups can stand out in a crowded AI market
With so many AI startups launching, differentiation is critical. Bennett highlights one foundational discipline: knowing your Ideal Customer Profile (ICP) deeply.
“The first thing I would say is understanding your ICP very effectively and understanding how that’s going to shift over a period of time.”
Many AI products, especially those based on GPT-style models, are broadly applicable by nature. The startups that win are those that narrow their focus, deeply understand specific customer segments, and tailor product, messaging, and go-to-market accordingly.
Here, AWS’s ecosystem becomes a strategic advantage. With strong enterprise relationships across geographies and industries, AWS helps startups:
- Refine their go-to-market strategies
- Navigate how to sell into enterprises in different regions
- Connect with the right customer cohorts faster
For founders, that means partnering wisely – whether with Cloud providers, accelerators, or industry-focused platforms – to gain distribution and credibility.
The road ahead
Looking 12-18 months out, Bennett expects several themes to persist and intensify.
First, many of the biggest barriers to adoption for startups selling into enterprises are not technical, but cultural and organisational: “It’s often not their solution or the technology that is, in fact, the gap or the challenge. It is navigating a large enterprise company … working with procurement and working with all these different teams.”
Second, the pace of AI adoption in real-world applications is accelerating across verticals. Healthcare is a telling example, especially in ambient notetaking, where AI systems help clinicians focus more on patients and less on documentation. Because every stakeholder benefits – patients, clinicians, and health systems – adoption is happening faster than in many traditional healthcare innovations.
Bennett also highlights the emerging wave of physical AI and robotics, enabled by advances in real-world models, cheaper components, and massive training capacity (including AWS’s own chips like Trainium and Inferentia): “I sort of feel like this is the moment for physical AI and robotics … you now have this moment in time where you can start to see the uptick for physical AI and robotics.”
From manufacturing and dangerous environments to logistics and warehousing, the opportunity is “uncapped,” as labour-intensive or hazardous tasks become increasingly automatable.
The UK’s role in global AI innovation
For founders in the UK, Bennett’s view is encouraging. He describes the UK as having an “incredibly vibrant startup community”, backed by active venture capital and continued AWS investment in local infrastructure and teams.
“As startups who build here, they can continue to operate and grow effectively here.”
UK-based startups are not on the periphery – they are integral to the global AI innovation landscape.
