Ashu Garg specializes in backing pre-revenue, deeply technical founders building AI-first companies with the ambition to become the next Databricks or potential $100B+ companies, focusing on the zero-to-one phase from initial idea to first product and customers. He believes his superpower is quickly identifying people's unique strengths and hidden opportunities within 15-20 minutes, then connecting exceptional people to each other and to the right opportunities, while advising AI startups to assume rapid model improvement and focus on owning proprietary data and high-value workflows that model providers can't easily replicate.

• A strong investment thesis focused on context graphs as the next essential layer of the tech stack. This involves building software that understands the relationships between data, infrastructure, and user behavior rather than just processing isolated inputs. For example, he highlights portfolio companies like PlayerZero that build this deep context by automating support tasks where code, configuration, and customer actions all intersect.
• Belief that the new competitive moat for AI startups will be decision traces rather than just training data. He argues that the real value lies in capturing and searching the history of how an AI agent executed a workflow—specifically how it pulled context, applied rules, and resolved conflicts—information that traditional systems usually discard once a task is done.
• Skepticism toward legacy systems of record, viewing them as at risk of becoming commoditized backends. He prefers investing in orchestration-layer startups that sit at the point of decision-making. The idea is that value shifts to the interface where work is actually executed and coordinated across different platforms, rather than the database where the results are stored.
• Prediction that chat interfaces have hit a ceiling and will be replaced by AI embedded directly into workflows. He anticipates that the "Cursor for X" model—where intelligence lives inside the work environment rather than in a side panel—will become the default standard for knowledge work in the coming years.
• Observation on the changing nature of engineering talent and productivity. He notes that AI tools are helping top-tier developers increase their output by a hundredfold, while average developers see only marginal gains, suggesting that startups should focus on small teams of high-performers rather than volume hiring.
• Confidence that software is far from dead, despite narratives about AI replacing it. He posits that incumbents must pivot to AI-native products to survive, and specifically looks for opportunities where software previously failed to bridge gaps between departments, citing Revenue Operations as a role that only exists because sales and finance software couldn't talk to each other.