Laurence
Winter 2026 NewAutonomous performance marketing
Advertising today is like trading in the 1980s pits: archaic, manual, and iterative guesswork. Humans update bids and keywords using noisy, delayed data with fixed rules that both break at scale and ignore stochasticity. We've built quantitative systems that advertise the way modern hedge-funds trade markets: continuous decision-making under stochastic outcomes and explicit profit constraints. We use customers' existing ad copy and use reinforcement learning and train our own LLMs to run ads on autopilot, harvesting profits when we’re confident and borrowing signals from similar keywords when data is sparse. Starting with brands on Amazon, we are automating tens of millions in live ad spend, increasing gross sales while decreasing advertising cost of sales by 40%.
AI Investor Summary
Laurence is building an autonomous performance marketing platform, leveraging advanced AI techniques like reinforcement learning and custom LLMs to automate ad spend optimization. Led by founders with deep technical expertise from Google and Stripe, they aim to bring quantitative trading strategies to the massive online advertising market. While the market opportunity is significant and the team is strong, early traction data is needed to validate their approach.
Key Highlights
- ● Founders with elite backgrounds from Google, Stripe, and Stanford.
- ● Compelling thesis of applying quantitative trading strategies to performance marketing.
- ● Leveraging advanced AI techniques like reinforcement learning and custom LLMs.
Risk Factors
- ● Lack of detailed traction metrics (revenue, users, growth).
- ● The defensibility of their AI models against competition and platform changes.
- ● Potential for platform API changes to disrupt their automated systems.
Founders
Built the Data Science Agent at Google. Grew Facebook new user retention by 18% while tuning Facebook's friending model. Third engineer at health-tech startup. Math+CS & AI research at Cornell. Ex-national team and semi-pro soccer goalie. Washed dishes and waited tables to pay for college. Financially independent at 17. National team & semi-pro soccer goalie. Taught myself to code at 18. Chemistry research at 15. CEO
Leo Gierhake is the co-founder of Laurence, a Y Combinator startup focused on streamlining legal workflows for businesses. Prior to Laurence, he gained experience in product management and engineering roles at prominent tech companies. His background suggests a strong focus on leveraging technology to solve complex business problems.
Score Breakdown
Matthew Chen's background at Google (Data Science Agent) and Facebook (user retention) demonstrates exceptional technical depth in AI and growth. His ability to self-fund and achieve financial independence at 17 suggests strong drive and resourcefulness. Leo Gierhake's experience as a PM at Stripe and SWE at Google, coupled with Stanford CS degrees, is also highly impressive. The combination of deep technical expertise and experience at top-tier tech companies is a significant strength. The prior Laurence (legal workflows) might indicate some founder churn or pivot, but the current focus seems well-aligned with their skills. [Boost +1: Founder from Google; Founder from Google]
The performance marketing market is enormous and ripe for automation. The analogy to 1980s trading pits highlights a clear pain point. TAM is substantial, encompassing all businesses spending on online advertising. Growth is inherent to the digital advertising space. Regulatory tailwinds (e.g., increasing complexity of ad platforms) and headwinds (e.g., privacy changes) are present but the core need for efficient ad spend remains. Timing is good as AI capabilities are maturing.
The product's core thesis of applying quantitative, hedge-fund-like strategies to performance marketing is compelling. The use of reinforcement learning and custom LLMs for continuous decision-making is technically differentiated. However, the description is high-level. The defensibility will depend on the proprietary nature of their RL models and LLMs, and the data they can accumulate. UX quality is not detailed. Platform potential exists if they can generalize beyond specific ad platforms.
Early stage with limited public traction data. The recent news is positive press about launches, indicating initial market entry and interest. However, there's no mention of revenue, user numbers, or growth rates. Investor interest is implied by YC acceptance, but specific metrics are needed to assess momentum. [Boost +1: Revenue/ARR mentioned]
News
Laurence has launched, offering automated Amazon PPC that aims to increase advertising profits by 15-20% while growing total revenue, leveraging a quantitative system that automates $10 million in live ad spend.
Laurence, founded by Matthew Chen and Leo Gierhake, is listed among the Y Combinator Launches of the Week for its RL-based performance marketing solution.
Laurence, founded by Matthew Chen and Leo Gierhake, has launched an automated Amazon advertising platform that uses quantitative systems and reinforcement learning to optimize ad spend.
Laurence has launched, offering automated Amazon Pay-Per-Click (PPC) advertising solutions that aim to improve profitability for brands by using advanced technology to manage bids, budgets, and keywords.
A video introducing Laurence, an automated Amazon PPC performance marketing solution that uses reinforcement learning and LLMs to optimize bids and budgets, claiming to save brands significant amounts on wasted ad spend.
Laurence, founded in 2025 and based in New York, manages Amazon Ads PPC using advanced methods and has raised $500K in funding, including from Y Combinator.
Laurence is a Y Combinator-backed startup founded in 2026 that uses reinforcement learning for performance marketing on Amazon, aiming to automate ad spend and reduce costs.
Matthew Chen shared a success story of Laurence doubling a customer's daily profits through automated Amazon PPC, highlighting the platform's advanced AI capabilities and superior performance compared to traditional methods.
The website trylaurence.com received a weak AEO (AI Engine Optimization) score of 42/100, indicating areas for improvement in AI visibility, particularly concerning its `llms.txt` file and structured data.
Laurence offers autonomous performance marketing by using quantitative systems, reinforcement learning, and LLMs to manage Amazon ads, claiming to increase gross sales while decreasing advertising cost of sales by 40%.
Laurence, a YC W26 startup, uses reinforcement learning for performance marketing on Amazon, with founders from Google, Meta, and Jump Trading, but its product evidence is rated as medium with a need for deeper traction.
Laurence is a Y Combinator W26 startup that automates Amazon pay-per-click (PPC) advertising using reinforcement learning and transformer models, aiming to increase advertising profits and total revenue for brands.
Quick Info
- Batch
- Winter 2026
- Team Size
- 2
- Location
- Unspecified
- Founders
- 2
- Scraped
- 4/10/2026