Elena Sakach is on a roll.
A partner at GV (Google Ventures), Sakach has helped lead the firm’s investments in high-profile startups such as Humans&, Ramp, Stripe, Tennr and Basis.
Unsurprisingly, considering her involvement in so many significant fintech deals, Sakach followed a fairly traditional path into finance. She started in the technology, media and telecom investment banking group at Goldman Sachs before moving into investing roles.
Sakach began her investing career at TPG, focusing mainly on software and fintech businesses across buyouts and minority investments. Over time, she transitioned more toward growth and venture investing, joining Coatue in 2021.
In May 2024, Sakach landed at GV, where she now focuses on growth-stage companies, and in her view, her fintech background gives her an introspective lens to examine different verticals.
“Across my investments, the common thread is solving large structural problems with technology and data advantages,” she said.
Crunchbase News recently spoke with Sakach to find out more about her investment thesis, her thoughts on what defines winning fintech and AI companies, how AI is affecting traditional software businesses, and how she determines what truly is a large opportunity.
This interview was edited for brevity and clarity.
Crunchbase News: Do you consider yourself a fintech investor or more of a generalist?
Sakach: I consider myself an investor first. Some venture investors define themselves by sector, but I’ve always wanted to be the best investor possible, regardless of category.
I’ve worked across stages and strategies — from banking to buyouts to growth equity to venture.
Those experiences are interconnected. For example, banking exposes you to companies at every lifecycle stage, buyouts focus on mature businesses, and venture focuses on emerging leaders.
At GV, we invest in hyper-scaling businesses early in their lifecycle that we believe could become public companies.
What does it take to build a successful fintech company today?
I think a lot about compounding businesses — companies that naturally grow in value as customers use them over time.
The best fintech companies share several characteristics: trust-based customer relationships because once customers trust a financial platform switching becomes difficult; expansion economics because over time, companies can upsell and cross-sell additional products; and a core infrastructure role, which allows them to become embedded in essential financial workflows.
For example, Monzo compounds through customer engagement and product expansion. Stripe continues to grow as a core infrastructure provider for global payments.
Even today, modern payment service providers still handle a minority of global payment volume, which highlights how much growth opportunity remains.
How do you evaluate fintech opportunities now compared to a few years ago?
Today, companies tend to fall into two categories: very early, highly novel ideas, often AI-driven, or late-stage compounding businesses with strong retention and expansion dynamics.
Execution quality is critical. Many fintech successes come from doing the fundamentals exceptionally well.
There’s also a large opportunity in automation within financial institutions — AI-driven efficiency improvements inside banks and financial operations.
How is AI affecting traditional software businesses?
AI has reduced technology as a durable moat. Many software products can now be rebuilt quickly. As a result, defensibility is shifting toward proprietary data, distribution channels, customer relationships and talent and research capabilities.
Companies that succeed will preserve or expand their distribution advantage, rebuild their product stack for an AI-native world, and learn from proprietary usage data faster than competitors.
The dividing line is roughly pre- and post-ChatGPT. Companies built before must replatform. Companies built after must start with the right architecture.
What excites you most about AI’s long-term impact?
I think about two categories of impact: cost reduction and expansion of possibilities. The most exciting outcomes come from expanding what’s possible, not just reducing costs. AI can increase access, scale services, and grow total output. For example, healthcare automation doesn’t just reduce expenses — it enables providers to serve more patients.
I focus on opportunities that expand outcomes dramatically rather than simply making existing processes cheaper.
Are current AI valuations sustainable?
The key difference between today and 2021 is the presence of a true platform shift. In 2021, capital surged and there was no comparable technological shift. Today, AI represents a foundational technology transition. So, capital is flowing toward transformative opportunities.
Another major change is structural. Venture capital has grown dramatically as an asset class.
Large funds must deploy capital, which increases competition and deal sizes. The critical question is not valuation alone. It’s whether investors are backing category-defining opportunities.
How do you determine what qualifies as a truly large opportunity?
You cannot make a small idea large simply by investing more capital. Investors evaluate things like market scale, structural tailwinds, timing (asking “why now?”), team capability and potential for industrywide change.
We’re looking for ideas that can reshape entire systems if they succeed. Those opportunities are relatively rare, which is why selectivity matters so much.
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Illustration: Dom Guzman
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