AI Makes It Easier Than Ever to Look Successful. That’s Exactly the Problem
By Henrik Landgren, CPTO and Co-founder of Gilion
The rise of AI agents has brought about an exceptional opportunity: to rebuild products in ways that were not previously possible, and will inevitably transform almost every industry. However, it is also fundamentally changing how the next generation of companies are built and backed. Agentic technology has pushed the productivity frontier outward, while also creating the biggest evaluation crisis that financial markets have ever faced. Companies can now achieve and posture early success metrics, and investors are facing greater difficulty in separating the wheat from the chaff.
Businesses are now able to do more with less, producing vast AI-led output with far fewer staff and resources. Phenomenal early-stage customer acquisition and growth metrics are easier to achieve – and can now be generated in the short term by a competent developer using AI. But they are no longer the same kind of standalone proxy indicators of the quality or product-market fit of a future high-growth business. The traditional signals investors have gotten comfortable relying on have become outdated, outperformed tools.
At Spotify, where I led analytics during our growth to 20M subscribers, we needed hundreds of engineers to build what just a handful of people can now create in weeks with AI. We’re now witnessing the emergence of Super-ICs (individual contributors): employees with agentic AI in their toolboxes who can match the output of a team of people just a few years ago. Multi-agent workflows, functional tooling, and orchestration support have raised the bar and transformed what’s expected from top talent across every function.
I’ve been coding since I was 6 and experimenting with neural networks since the 1990s. The technical capabilities we have today would have seemed like science fiction back then. An agentic system can now orchestrate hundreds of specialised agents, each handling tasks that once required dedicated attention from a human expert. But this same power that enables a small team to build world-class products also enables others to generate world-class metrics without necessarily having the same underlying substance.
Traditional evaluation methods, benchmarks, rules of thumbs, and investor napkins were previously founded in the historical success of high-performance companies. But in the new agentic age, all these are becoming obsolete.
When an individual or small team can produce previously unparalleled output across sales, marketing, and operations, attracting first-time customers and the revenue they bring doesn’t necessarily correlate with a company’s long-term value. These metrics are still important, but the benchmarks are now higher, and alone they are insufficient.
Yet financial institutions are still too often making multi-billion dollar decisions with tunnel vision. Banks analyse dated balance sheets, and VCs look to headcount growth and surface-level financial models. These organisations are not seeing the full picture when it comes to company valuations. For all their proclamations that AI has changed the world, investors are still making bets using the same old playbook.
Financial markets need a new framework for the agentic age, one that can better see patterns as they emerge. The technology exists to leverage fundamental data with high accuracy to determine what a company’s revenue will be in the coming months and years. To have agents monitor deep metrics that unearth problems and deviations months before they would show up otherwise. The development of agentic AI will only accelerate, and the gap between company valuations and tangible value will widen until the market learns these new evaluation methods.
The last tech downturn created, and was arguably brought about by, a massive correction for companies with ‘false value’ – whose eye-watering valuations bubbled up out of control. Those with strong unit economics and sustainable growth survived, while several of those chasing flashy numbers are no longer around. At Gilion, we saw this coming in our data. The real-time analysis of billions of live data points per company – every payment transaction, user click, accounting record, and marketing interaction – provides a continuous, anatomical view on whether resilience exists in its DNA.
Smart investors are already adapting. They are deepening their toolboxes and gaining a competitive advantage, using the hundreds of specialised AI agents available for due diligence. They check if the superior product advantage still holds in the agentic age, if the team still has the capabilities to execute, and if market dynamics have shifted. We will soon see the first batch of agentic investors with outsized portfolio performance, paving the way for the mass market.
Founders might argue this feels a little daunting. Opening your books to highly analytical investors initially feels less preferable to the AI ‘hype cycle’ that has generated big rounds and household-name businesses. But if a founder wants to build a long-term growth business, they too should want to be aware of detailed customer engagement trends, the real-time impact of their bets, whether they can increase scaling speed, or hold back until their growth covers their costs. And most of all, that the investors around their cap table are ones that do their due diligence. Those chasing a bumper valuation without the underlying numbers risk flying too close to the sun – facing potential layoffs and down-rounds if, and often when, revenue slows.
The greatest concern isn’t missing the next unicorn. It’s funding companies with impressive AI-generated metrics and bolted-on integrations, but no real business underneath. Ironically, the same tools that enable false signals also enable genuine innovation at scales previously thought impossible. Those companies that survive now will be the ones solving real problems for real customers.
Henrik Landgren, Co-founder and CPTO of Gilion: Henrik Landgren is Co-founder and CPTO of Gilion, the Stockholm-headquartered, AI-powered funding platform. Henrik was previously Spotify’s first VP of Analytics, leading data strategy under Daniel Ek throughout the company’s explosive growth to over 20 million subscribers and 2,000 employees. He then built EQT Ventures’ Funds I and II from launch to over 100 investments, while simultaneously creating Motherbrain, an AI-driven investment platform that revolutionised data-driven deal sourcing and decision-making. At Gilion, he leads development on the firm’s AI-powered investment platform, which is transforming investment and business growth intelligence.
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