Sinewave Ventures

For most of its fifteen-year history, FedRAMP operated as an assurance regime built on narrative attestation and a perceived gate for vendors. A vendor produced a System Security Plan, often several hundred pages, describing how it satisfied the controls

For the past few years, Silicon Valley has been caught in a fever dream of conspicuous consumption known as "token maxing." Under pressure to show "AI productivity," enterprises gamified LLM usage-shoveling massive datasets into frontier model context win

Front model token prices have fallen sharply over the past two years, yet most enterprises are watching their AI bills climb, not shrink. The reason is structural, not pricing: agentic AI doesn't just answer a question, it plans, calls tools, retries fail

"Investing in venture is a return-free risk." -Roelof Botha, Sequoia Capital "No. Investing in bad venture is a return-free risk." -Yanev Suissa, SineWave Ventures Where is Roelof correct? What is happening in the industry? How do you succeed given those

Last month, a court in California temporarily blocked the Department of War's blacklisting of Anthropic, and we are waiting on another decision from the D.C. Circuit. Rarely do arguments over government contracts hit the headlines across the country, but

The U.S. Federal Government is the world's largest single IT market, spending over $100 billion annually on Information Technology. This spending level exceeds that of any other single entity. Furthermore, the Federal Government acts as a "market maker,"

Across industries, the scale at which AI systems now operate is expanding dramatically. Advanced platforms are not simply analyzing datasets, they are continuously ingesting streams of information from applications, networks, sensors, and digital transact

We're excited to share a recent conversation between Yanev Suissa and Alpha Partners. The discussion highlights clear, founder-relevant frameworks for turning government into a scalable growth channel, along with Yanev's honest perspective on AI, convicti

As software systems increasingly rely on machine learning, AI, and automated decision making, organizations often hear two terms used interchangeably: observability and explainability. While closely related, they serve distinct purposes: observability hel

As business environments become more dynamic, companies are discovering that their carefully engineered deterministic systems are constraining growth and innovation. The backbone of today's enterprise operations consists of deterministic data workflows, i