Ankur Goyal

Founder of Braintrust, former AI lead at Figma, and advocate for evaluation-centric AI development.

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Key positions and views

The single most important and primary activity in AI product development is building and iterating on evaluations ('evals').

The latest generation of AI models has crossed a critical threshold, becoming capable of evaluating and improving their own work, which enables a new paradigm of self-improvement.

Incumbent companies face an existential choice: either fundamentally rebuild their products around AI or they will fail.

The choice of a specific AI model is transient and unreliable for long-term planning; therefore, tooling and infrastructure must be model-agnostic.

Founders cannot hire go-to-market leaders to solve fundamental product or strategy problems; these must be figured out by the core team first.

Podcast consensus on Goyal

Points of consensus

▶Ankur Goyal has a consistent career trajectory focused on data and AI, from dropping out of Carnegie Mellon to join MemSQL, to founding the AI document extraction company Empira, leading Figma's AI team post-acquisition, and now founding Braintrust.May 2026

▶Braintrust's product is fundamentally centered on the concept of 'evals', with core features designed to connect production logs to evaluation datasets and code in GitHub to create a tight feedback loop for AI development.May 2026

▶Goyal's leadership philosophy at Braintrust emphasizes a unique company culture, characterized by a flat organizational structure with minimal hierarchy and an engineering practice that prioritizes immediate response to customer issues over rigid sprint plans.May 2026

Points of debate

▶Goyal presents a stark contrast between Braintrust and observability tools like Datadog, arguing that customers use Braintrust to achieve product 'quality' whereas they use Datadog for 'uptime', positioning them as solving fundamentally different business problems.May 2026

▶He posits an existential dilemma for incumbent companies, stating they must either completely rebuild their products around AI or they will fail, a viewpoint that frames the current technological shift as a make-or-break moment for established businesses.

▶Goyal challenges a common startup practice by asserting that founders cannot hire go-to-market leaders to fix underlying product or strategy issues, arguing these problems must be solved by the founders first.May 2026

▶He presents a focused, almost singular, view on AI development, claiming that focusing on evaluations ('evals') is the only activity of value, which contrasts with other potential priorities like data preparation, model training, or prompt engineering.May 2026

Key themes

▶The Primacy of 'Evals' in AI DevelopmentMay 2026

Goyal argues that the most critical and primary activity for building AI products is creating and refining evaluations ('evals'). This philosophy is embedded in his company's product, which connects production logs to evaluation datasets linked directly to code and prompts, creating a continuous improvement loop.

This focus on evaluation as the core development loop suggests a paradigm shift from traditional software development, highlighting a key area of tooling and infrastructure investment for AI-native companies that prioritize quality and reliability.

▶AI-Driven Organizational and Product PhilosophyMay 2026

Goyal's management and product strategy are heavily influenced by AI principles and observations from other tech leaders. He advocates for a flat organizational structure inspired by NVIDIA's CEO and believes Braintrust's internal AI agent, Loop, will simplify the product by reducing the need for new features.

This indicates a belief that AI will not just be a feature but will fundamentally reshape how companies are structured and how products evolve, potentially leading to leaner organizations and simpler, agent-driven user experiences.

▶The Unpredictable and Opaque Nature of LLMsMay 2026

Goyal emphasizes the uncertainty in the AI space, stating that practitioners generally agree that no one fully understands how LLMs work. He also predicts that the model a developer chooses today is highly unlikely to be the one they use exclusively in the future, underscoring the field's volatility.

For investors and analysts, this highlights the risk of betting on specific models and the corresponding opportunity for companies like Braintrust that provide model-agnostic infrastructure and tooling that abstract away model-specific dependencies.

▶Pragmatic Go-to-Market and Engineering CultureMay 2026

Goyal details a highly focused go-to-market strategy that involved being selective about initial customers to ensure their success. This is paired with an engineering culture that drops everything to fix customer-reported issues, prioritizing real-world user problems over pre-planned sprints.

This reveals a founder-led, product-centric approach that prioritizes deep customer value and reliability over premature scaling, a potentially durable model for other deep-tech startups navigating nascent markets.

Source episodes

Sentiment over time

Not enough data for timeline

Changes over time

Early Career

Dropped out of the computer science program at Carnegie Mellon University to become the second employee at the database company MemSQL.

First Founding

Founded his first company, Empira, which focused on using AI for document extraction.

Post-Acquisition

After Empira was acquired by Figma, Goyal transitioned to lead the AI team at Figma.

Braintrust Founding

Founded Braintrust, implementing a go-to-market strategy focused on being highly selective with initial customers to ensure their success.

Braintrust Technical Evolution

After Braintrust's initial open-source logging infrastructure failed to scale, the company built its own purpose-built data system, Brainstore, to handle complex LLM workloads.

Braintrust Product Maturation

Braintrust launched a native integration with OpenAI's real-time API ahead of OpenAI's own tooling and introduced an internal AI agent named 'Loop' to simplify the product.

Suggested prompts

How does Ankur Goyal's philosophy on 'evals' as the core of AI development challenge traditional software engineering methodologies? &nearr;What are the potential risks and rewards of Braintrust's strategy to use an internal AI agent ('Loop') to simplify its product and potentially remove features? &nearr;Given Goyal's view that founders must solve product problems before hiring GTM leaders, how might this affect Braintrust's ability to scale compared to competitors with more aggressive, sales-led growth models? &nearr;How does Goyal's assertion that LLMs are not fully understood and that model choices are temporary influence Braintrust's long-term product roadmap and competitive positioning? &nearr;

Key concepts

Braintrust 1 ep AI Evaluations ('Evals') 1 ep AI Agents 1 ep Large Language Models (LLMs) 1 ep Company Culture 1 ep Go-to-Market Strategy 1 ep Product Development Philosophy 1 ep Empira 1 ep Figma 1 ep

Notable quotes

“if you're building an AI product, there's no point doing anything other than evals.”

Ankur Goyal · Braintrust's Ankur Goyal on Why Evals Are the Core of AI Development

“as of the most recent vintage of models, we hit a threshold change or a step function change where models are now sufficiently good at looking at their own work that they can self-improve.”

Ankur Goyal · Braintrust's Ankur Goyal on Why Evals Are the Core of AI Development

“it's either figure out how to rebuild our product around AI or die.”

Ankur Goyal · Braintrust's Ankur Goyal on Why Evals Are the Core of AI Development

“the only thing that you can be certain of is that whatever model you pick today is super unlikely to be the model that you're going to use solely tomorrow.”

Ankur Goyal · Braintrust's Ankur Goyal on Why Evals Are the Core of AI Development

Report last updated: May 5, 2026

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