General Partner
Andreessen Horowitz (a16z)Check size: $1M-$100M+ (from a16z's $5.2B AI fund, largest sector allocation in 2026)
Leads a16z's AI investments from the $5.2B AI allocation — the firm's largest sector bet ever. Anjney came to AI from an unusual path: he previously led a16z's crypto gaming investments, giving him deep understanding of platform transitions, virtual economies, and consumer behavior in digital worlds. His AI thesis is built on the belief that AI is a platform shift as significant as mobile or cloud — and that the biggest opportunities are at both the infrastructure layer (foundation models, training, inference) and the application layer (consumer AI, AI agents, vertical applications). Particularly bullish on consumer AI as a social phenomenon — Character.ai embodies this, where AI characters become companions and social interactions, not just tools. Believes open-weight models (Mistral, Meta's Llama) will create an ecosystem similar to open-source software, where the most innovative applications are built on open foundations. Views gaming as a leading indicator for AI adoption — games were the first consumer products to adopt AI-generated content, AI NPCs, and AI-powered user experiences.
Technical founders building at the frontier of AI capability. Companies with genuine technical differentiation in models, training, or inference — not just prompt engineering. AI applications that couldn't exist without recent model capabilities. Teams with deep ML research backgrounds. Consumer AI products that create new behaviors (not just AI-augmented existing behaviors). Open-weight ecosystem plays that benefit from network effects.
AI wrappers without technical depth. Companies competing solely on prompt engineering. AI products without data or model moats. Teams without deep ML expertise building 'AI companies.' Applications that could be trivially replicated when the next model improvement ships. AI companies that are just a UI on top of an API with no proprietary advantage.
Show technical depth. He invests from the largest AI fund in VC — he sees hundreds of AI pitches. Differentiate on technical capability, not just application. If you're building models, show benchmarks and explain your technical approach. If you're building applications, show why your approach requires frontier capabilities and can't be trivially replicated. Show your team's ML credentials — papers, research backgrounds, prior work at AI labs. If you're in consumer AI, show engagement metrics and explain why users come back. If you're in infrastructure, show performance benchmarks and cost advantages. Understand the competitive landscape deeply — who else is building this and why you'll win. Having an opinion on open vs. closed models is table stakes. If gaming or social dynamics are relevant to your product, lean into that — it's Anjney's original domain.
AI is a platform shift comparable to mobile (2007-2012) or cloud (2006-2015). During platform shifts, entirely new categories of companies emerge. The winners aren't the ones who retrofit existing products — they're the ones who build natively for the new platform. a16z is allocating accordingly with $5.2B.
The AI stack has two major investable layers: infrastructure (foundation models, training, inference, tooling) and applications (products built on top of models). Both can capture significant value. Unlike mobile (where Apple/Google captured most infrastructure value), AI's value distribution is still unsettled.
The most engaging consumer AI products will be social, not solo tools. AI characters, companions, and social interactions create engagement patterns more like social media than like productivity software. Character.ai proved this with massive engagement metrics.
Open-weight models (Mistral, Meta's Llama) will create a vibrant ecosystem similar to open-source software. Companies building on or contributing to open models benefit from community innovation, lower switching costs for customers, and transparency. This ecosystem competes with closed models (OpenAI, Anthropic) on different dimensions.
Games are always the first consumer products to adopt new technologies at scale. Gaming adopted AI-generated content, AI NPCs, and AI-powered experiences before any other consumer category. Understanding gaming AI adoption patterns predicts how AI will spread to other consumer categories.
Analysis of value distribution in the AI stack — where does value accrue? At the model layer, the infrastructure layer, or the application layer? Argues that the AI platform is still early and the value chain isn't settled. Both infrastructure and applications can capture significant value, unlike previous platform shifts where one layer dominated.
AI compute costs are the defining constraint for AI companies. Successful AI companies need strategies for managing compute costs — whether through more efficient models, inference optimization, or novel architectures. The compute cost curve will determine which business models are viable.
Framework for how AI companies should think about building, deploying, and scaling AI products. Covers model selection, fine-tuning strategy, evaluation, deployment, and business model considerations.
Analysis of consumer AI engagement patterns. Some of the most successful consumer AI products are deliberately constrained — doing one thing exceptionally well rather than trying to be everything. Character.ai's focused approach to AI conversation is an example.
“AI is a platform shift, not a feature. The companies that win won't be the ones that add AI to existing products — they'll be the ones that build entirely new products that couldn't exist without AI.”
— Interviews and a16z talks
“Consumer AI is going to be social, not solo. The most engaging AI products won't be productivity tools — they'll be social experiences where AI is a participant.”
— Character.ai investment thesis
“Open-weight models will create an ecosystem as vibrant as open-source software. The most innovative applications will be built on open foundations.”
— Mistral investment thesis / talks
“Gaming has always been the first industry to adopt new technology at scale. AI is no different — games will show us what consumer AI looks like before anyone else.”
— Gaming and AI talks
“The question isn't whether AI infrastructure or AI applications will be more valuable. Both layers will produce massive companies. The question is which companies at each layer will win.”
— Who Owns the Generative AI Platform essay
Studied at Stanford University (Computer Science). Joined a16z relatively young. Initially focused on crypto gaming investments — led deals at the intersection of blockchain, gaming, and virtual economies. This gave him deep understanding of platform transitions, digital native consumer behavior, and how new technologies create entirely new categories of interaction. Transitioned to lead a16z's AI practice as AI emerged as the firm's biggest bet. His gaming background is actually a unique advantage in AI investing — he sees consumer AI through the lens of engagement, retention, and social dynamics rather than purely through a technology or enterprise lens. This is why he was early to Character.ai (AI as social interaction) while many AI investors focused exclusively on enterprise productivity tools. Now manages a16z's largest-ever sector allocation at $5.2B in AI.