Forward Future Live | 02.06.26 | Guests from Modular, Emergence Capital, & Axiom

2026-02-07 · Source: Matthew Berman · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, extended

Summary

Forward Future Live on February 6, 2026, featured discussions on recent AI model updates, the "SaaS apocalypse," and the future of AI infrastructure and mathematical reasoning. Anthropic released Opus 4.6, boasting a 1 million token context window and enhanced agentic tool use, while OpenAI countered with GBT 5.3 Codecs, emphasizing coding performance and self-creation capabilities. The show also covered the ongoing competition between Anthropic and OpenAI, including Super Bowl ads and pricing strategies. Guests included Tim Davis from Modular AI, discussing open and portable AI infrastructure to combat hardware lock-in, and Joseph Floyd from Emergence Capital, analyzing the impact of AI on SaaS valuations and the firm's evolving investment strategy. Karina Hong, founder and CEO of Axiom, presented her company's work on a self-improving AI mathematician, which recently achieved a perfect score on the Putnam exam, highlighting the potential for AI in formal math and code verification.

Key takeaway

For CTOs and VPs of Engineering navigating the rapidly evolving AI landscape, recognize that the "software is dead" paradigm necessitates a shift towards AI-native solutions and hardware-agnostic infrastructure. Your teams should prioritize adopting flexible AI deployment platforms like Modular AI to avoid vendor lock-in and explore verified AI generation for mission-critical applications, as demonstrated by Axiom's advancements in formal mathematics, to ensure reliability and accelerate development in high-stakes domains.

Key insights

AI models are rapidly advancing in context, agentic capabilities, and self-improvement, intensifying competition and reshaping industry landscapes.

Principles

• AI inference scales with user base, not just research teams.
• Hardware abstraction is key for flexible AI deployment.
• Verifiable rewards enhance AI system training and reliability.

Method

Axiom's approach to AI mathematics involves translating natural language math into formal Lean code, leveraging the Curry-Howard correspondence for verifiable generation and self-improvement.

In practice

• Use local embedding models to reduce cloud token usage by up to 30x.
• Orchestrate multiple AI models (e.g., Claude, Codecs, Gemini) for collective benefit.
• Explore formal math for mission-critical code verification in hardware/software.

Topics

• Frontier AI Models
• AI Infrastructure
• AI in Mathematics
• Enterprise AI
• Agentic AI

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, Investor, Entrepreneur

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Editorial summary, takeaway, and curation by AIssential. Original article published by Matthew Berman.