Silicon Valley Leadership Summit
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1. The acceleration of AI impact
As we move into 2025, we're witnessing a step-change in the impact of Generative AI. Foundation models have not only reached a critical threshold - becoming good enough for many enterprise use cases - but the emerging ecosystem of AI tools and scaffolding is now unlocking truly impactful applications. The contrast is stark: while the first wave of co-pilot solutions delivered modest 10-20% efficiency gains, early agentic tools are now demonstrating impressive 40-60+% improvements for some select use cases.
“Over the next couple of years, every single engineer is going to be able to do 10x more than what they did one or two years ago.”
Scott Wu, Co-founder and CEO, Cognition Labs
“What I'm hearing from enterprises that I talk to is a 25% uplift of productivity of developers while they code. If they're using a coding model, the overall uplift is 12% or so…for a couple of hundred dollars, getting a 12% uplift on a $100,000 a year developer is an incredibly good deal. The ROI case is very simple.”
Guido Appenzeller, Partner at Andreessen Horowitz
2. Operational opportunity - from R&D to GTM
R&D is leading the charge, with Hg pilots consistently demonstrating 25%+ efficiencies in key use cases, building towards 80%+ where agentic engineers excel. Customer Support and Operations are following closely behind, with AI solutions rapidly transforming everything from ticket triage to issue resolution. GTM, while slower to evolve initially, is now emerging as a significant opportunity space.
“Ninety percent of our developers who use AI say that they feel more productive, 20 percent say they feel much more productive. I think this shows that there is this pack of early adopters and pioneers who take the time to figure things out, teaching themselves how to be most valuable. And then you have the majority, who we need to invest in, who we need to inspire and motivate, and give them the chance to catch up,”
Alexander Lystad, Visma CTO.
“The next five years are going to be like when I built Instagram, at the time when mobile all of a sudden became a platform. As much as there's been a lot of excitement in AI so far, I think in the next five years we’ll be taking that capability and making it actually useful to people.”
Mike Krieger, Anthropic CPO
3. Overcoming viscosity - the race to product-market fit
The impact on product presents a more complex picture. While 'co-pilot wrappers' are already delivering pricing uplifts and NPS gains through enhanced user experiences, the timeline for mass adoption of transformative solutions of ‘killer apps’ (like those demonstrated by Harvey and Vic.AI) is less clear. Unlocking this next wave of value will require intelligent tailoring of agents and models to specific, vertical use cases, overcoming real-world viscosity to find product-market fit. This creates both opportunities and challenges for product teams navigating this evolving landscape.
“Every AI company that wants your market is built by a small team of 20-somethings working all day every day. I meet them all the time, I invest in these guys. This is not hyperbole. This is the reality.”
Eoghan McCabe, CEO, Intercom
4. The incumbent advantage - and the rising challenge
Established companies find themselves in a unique position to leverage their competitive advantages. By combining their deep repositories of industry-specific data (their 'frontier data' – the specialized, real-world transaction and interaction data that only incumbents possess) with cutting-edge AI models, incumbents can create powerful solutions that newer entrants cannot easily replicate. Their domain expertise and extensive customer distribution networks further amplify this advantage. However, incumbents cannot afford complacency - substantial talent and venture capital are actively flowing into Vertical AI challengers. These new entrants are building AI-native solutions that target industry-specific pain points, presenting a growing competitive presence. Therefore the window for incumbents to capitalize on their advantage is now.
“It’s not just about the usage of our software but also the domain. If you develop that further, the support agents will gradually become expert agents…Expert agents can probably be something that you can monetise in the future.”
Øystein Moan, Executive Chairman, Visma
5. The product engine imperative
We're witnessing the start of accelerating product development. As LLMs mature and R&D costs decline, new use cases are emerging to compress traditional product development cycles. In this environment, the strength and agility of a company's product engine becomes the critical differentiator. Organizations must build robust processes for rapidly identifying, testing, and scaling AI-enabled solutions. Those who can't keep pace risk being overtaken by more agile competitors. This isn't just about having a strong product team – it's about transforming the entire organization to operate at AI speed, where months-long development cycles must be compressed into weeks or even days.
“What technology could disrupt your existing business model? Imagine it and build it. Ask: ‘how do I disrupt myself…and how do I make the economics work for me in a different way?”
Lindsay Sparks, Serial Chair, CINC Systems
6. Leadership in the AI Era
The next few years offer transformative gains, but demand a fundamental shift in leadership approach. While traditional SaaS playbooks have served well until now, the GenAI era requires Product-focused Chairs and CEOs capable of orchestrating these GenAI-first transformations.
Success demands a two-pronged strategy: First, leaders must drive change from the top, fundamentally reimagining customer journeys and operating models with GenAI at their core – not merely as an add-on. Second, they must cultivate an environment that encourages bottom-up experimentation and rapid innovation. This often requires strong leadership to overcome organizational inertia, navigate compliance and legal hurdles, and drive broad adoption throughout the organization. The companies that will succeed are those whose leaders can balance this AI-driven step-change while keeping momentum in BAU.
For businesses that seek to change how work gets done in the economy through the application of technology, the rapid maturation of GenAI is ushering in exciting opportunities. The very best of these companies - supported by a strong Hg ecosystem supplying talent, tools, and IP - are ready to take on the transformation challenge and capture these emerging but transformative upsides.
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