AI strategy decisions land on your desk — make them with context
Build or buy? Which models to bet on? How to restructure teams around AI? Get a weekly brief that gives you the strategic context to decide.
Curated from 20+ industry labs and publications
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Board and CEO want an AI strategy
You're expected to have a clear AI roadmap, but the landscape changes every month. You need a continuously updated view.
Build vs. buy is harder than ever
Open source models improve monthly, API prices keep dropping, and every vendor is adding AI. The calculus shifts constantly.
Competitive pressure to ship AI features
Your competitors are announcing AI capabilities. You need to know what's real, what's vaporware, and where to invest.
AI news through the CTOs & VPs of Engineering lens
Executive-level synthesis
We distill technical AI developments into strategic implications — not implementation details, but decision context.
Competitive intelligence
What your peers and competitors are building, buying, and betting on — sourced from launches, partnerships, and hiring signals.
Investment-grade context
Model cost trends, capability trajectories, and vendor lock-in risks to inform your technical strategy.
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From 20+ AI labs, publications, and research outlets
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Filtering for CTOs & VPs of Engineering, Build-vs-buy strategy, AI infrastructure costs
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Surfacing only what matters to your role and priorities
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News Relevant to You
OpenAI's o1 Model Release Shifts Build-vs-Buy Calculus for Enterprise Reasoning Tasks
OpenAI released o1, a reasoning-focused model that outperforms GPT-4 on complex problem-solving, forcing engineering leaders to reassess whether to build custom reasoning layers or adopt pre-built solutions. Early benchmarks show 92% improvement on AIME math problems, with pricing at $15/1M input tokens.
Why this matters to you: This changes your build-vs-buy strategy calculations for reasoning-heavy features—understanding o1's cost-to-capability ratio is critical before greenlit any internal R&D on similar capabilities.
Google Introduces Gemini 2.0 with Native Multimodal Planning; Microsoft Expands Copilot Stack for Engineering Teams
Google's Gemini 2.0 adds native multimodal planning abilities, while Microsoft deepens its Copilot offerings for software development with tighter GitHub integration. Both moves signal where the AI infrastructure wars are heading and what your competitors are already deploying.
Why this matters to you: Your competitive AI moves tracking should include how these platform shifts affect your org design patterns—knowing what Gemini 2.0 enables helps you decide whether to standardize on one platform or multi-vendor approach.
What To Test This Week
Run a 2-week pilot comparing in-house fine-tuned model costs vs. OpenAI o1 API for your top 3 reasoning workflows
Select your most expensive or frequent reasoning tasks (e.g., code review synthesis, architecture decision analysis, bug root-cause analysis), measure current latency and cost, then test o1's performance and per-request spend. Track total cost of ownership including prompt engineering time.
Why this matters to you: This hands-on test directly informs your build-vs-buy strategy and AI infrastructure costs—you'll have real data to present to your board on whether to invest in internal capability or shift budget to API consumption.
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Topics we watch for you include
- 🔍Strategic AI moves by major tech companies and competitors
- 🔍Build-vs-buy analysis for common AI capabilities
- 🔍Model cost and capability trend analysis
- 🔍Org design patterns for AI-forward engineering teams
- 🔍Weekly executive summary for board and leadership conversations
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