Featured

October 2026

Investing in Northstar: The Context Layer for Agentic Security

Northstar raises a Wing-led seed round to build a Security Context Graph that gives security teams and AI agents accurate, repeatable answers.

Firm News

October 2026

Wing Welcomes Tyson Mao

Wing VC names Tyson Mao, Tobiko Data co-founder and SQLMesh creator, as Head of AI to apply AI across sourcing and portfolio support.

Firm News

October 2026

Congratulations to Classiq on Its Acquisition by Intel

Intel acquires Classiq, the Tel Aviv–based leader in quantum software that turns high-level designs into optimized quantum circuits, in a major step for the quantum ecosystem.

Perspectives

October 2026

Cheap Money Is the New Moat in AI Infrastructure

AI infrastructure debt is splitting in two: big tech-backed bonds face rising rates, while smaller borrowers face default risk. The gap is becoming a competitive advantage.

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Funding Announcements

August 2026

Investing in Synthefy: Building Foundation Models for the World’s Numbers

Wing led Synthefy’s $6.5M seed for structured data foundation models, building tabular AI that reads enterprise numbers the way LLMs read language.

Perspectives

August 2026

The Simulation Stack in Robotics

Physics-based simulation engines and neural network world models offer complementary approaches to generating synthetic training data at scale for robot learning, though both face sim-to-real gaps that startups are addressing through domain randomization, asset generation, and real-to-sim reconstruction technologies.

Perspectives

August 2026

The AI Memory Stack

AI's memory problem isn't buying more HBM. It's managing a full hierarchy — GPU cache to cold storage — with a different winner at every tier.

Perspectives

July 2026

How AI Tokens Are Made

Inside the five scheduling, memory, and routing problems that turn GPU compute into output tokens — and why neoclouds keep buying the companies that solved them.

Perspectives

July 2026

The Data Pyramid in Robotics

The robot data pyramid framework combines seven types of training data—from internet video and human demonstrations to simulation and real-world deployments—to address the critical data bottleneck limiting the development of physical AI and autonomous robotics systems.