GenAI/AI News Jul 7, 25: Even Swiss Telecoms are Using GenAI, And Your Next Consultant Will be A Robot...
$4T+ In Services Will Be Reinvented With Service as Software, and More...
I have had the good fortune of doing some business in Switzerland and I have found my clients there to be true to the stereotype: smart, precise, demanding. If Swisscom is using GenAI in their network management that is quite an endorsement! More interestingly, the robots are coming to services. My friends in consulting see a future where they are much, much more leveraged with AI. The old “pyramid” of junior to senior staff is likely to become a “diamond” with fewer folks at the bottom — a robust middle and few at the top — with the other parts of the pyramid filled in by robots.
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Today there were 3 Important and 3 Optional articles.
Transforming network operations with AI: How Swisscom built a network assistant using Amazon Bedrock
Rating: Important
Rationale: Swisscom’s implementation of a Bedrock‑based, agent‑powered network assistant automates network data retrieval and KPI analysis—saving analysts ~200 hours/year. Our analysts appreciated the detailed architecture and iterative deployment approach as a practical template for enterprise AI implementation and maturity.
The $4.6T Services‑as‑Software opportunity: Lessons from year one
Rating: Important
Rationale: Foundation Capital highlights how AI-native startups are transforming enterprise software into outcome‑driven "services‑as‑software," not just tools—with differentiation coming from deep integration and implementation, not features alone. Our analysts noted that with the collapse of software primitives, embedding language models into workflows—blurring pre‑ and post‑sales—and outcome‑based pricing are critical markers for real traction.
DeepSeek Debrief: >128 Days Later
Rating: Important
Rationale: SemiAnalysis breaks down DeepSeek’s tokenomics, latency, and context window trade‑offs—showing why its R1 model has lost market share to low‑latency alternatives. Our analysts flagged the article as important for understanding model economics, vendor dynamics, and the strategic implications of delivery latency and model adoption.
Sakana AI’s TreeQuest: Deploy multi‑model teams that outperform individual LLMs by 30%
Rating: Optional
Rationale: Sakana AI open sourcing TreeQuest—a Monte Carlo Tree Search technique that dynamically coordinates multiple LLMs during inference, boosting performance on ARC‑AGI‑2 by ~30%. Our analysts found the approach intellectually compelling and aligned with next‑gen model design—though still research‑oriented and not immediately actionable.
On the Operational Risks and Liquidity Management of GPU Clouds
Rating: Optional
Rationale: This in-depth critique highlights how GPU leasing presents significant financial and operational risks, using methods from risk modeling to assess elasticity and supplier strategy. While insightful for infrastructure or finance leaders, our analysts deemed it outside the core purview of most AI decision-makers.
Strategic Intelligence in Large Language Models: Evidence from evolutionary Game Theory
Rating: Optional
Rationale: This research shows that LLMs demonstrate varied strategic behavior in game-theoretic simulations—such as Gemini being ruthless, OpenAI cooperative, and Claude forgiving—suggesting emergent model personalities. Our analysts found this intellectually engaging but noted its limited immediate relevance for AI practitioners.
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and Adam Rappaport.