This week, we’re talking about the evolution of the large language models and how businesses must evolve in order to capitalize on both the speed of innovation and performance advancements. As language models continue to evolve, they are no longer novelty but rather becoming core infrastructure. In the past few weeks, we’ve seen major developments across enterprise adoption, creative tooling, interoperability standards, and competitive models in Europe. From MCP’s plug-and-play tooling to Veo 3’s audio-visual realism to small language models (SLMs) that outperform at scale, the opportunities (and competitive pressure) are real.
Mistral Launches “Magistral”
Mistral’s new model prioritizes multilingual and region-specific reasoning, challenging US incumbents. Backed by French industry leaders like TotalEnergies and CMA CGM, it signals serious momentum in sovereign AI. For your business: consider piloting regionally compliant LLMs for legal, CX, and internal use cases, especially if operating in the EU. Read More →
From Hype to Production: AI Budgets Are Shifting
New a16z data shows only 7% of enterprise AI budgets are still in “innovation mode” – the rest are in deployment or scaling phases. What does this mean for you? If you’re still testing, accelerate. Start defining LLM governance, budget structure, and organization-wide deployment plans. Read More →
Retrieval-Augmented Generation (RAG) Pipelines Go Mainstream
RAG, or Retrieval-Augmented Generation, is now a top strategy for contextual LLM performance, powered by structured internal data storage and indexing systems. As you think about design decisions for knowledge and data management systems, consider investing in searchable internet capabilities. Your LLM is only as good as the data it can access. Read More →
Small Language Models (SLMs) Are the Future of Agentic AI
Not only are SLMs, or Small Language Models, more efficient than the general purpose LLMs that we know and use today (think chatGPT), but they are also “sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems,” so a team at NVIDIA Research posits. Read More →
Model Context Protocol (MCP) Standardizes AI Tooling
Perhaps not burning news, but certainly newsworthy enough to put on repeat. OpenAI, Microsoft, and Google have all adopted Anthropic’s MCP standard, a newish open protocol for connecting LLMs with real-world tools like Slack, GitHub, and Stripe. The great BetaMax/VCR debate hopefully has been laid to rest – businesses can now move forward with building interoperability into your LLM stack to reduce vendor lock-in and boost deployment speed. Read More →
AI Generated Video Slop Fest Hopefully Upgraded By Adobe
Everyone was raging on about Veo 3’s 8-second video AI capabilities last week, but Adobe’s Firefly AI allows users to choose between the latest models from Google, Pika, Luma, Runway, and Ideogram. Hopefully, this is us witnessing beginning of the end for the AI generated slop fest with a $2,000 NBA Finals ad (probably not, but one can dream). Read More →
The Enterprise Struggles with AI Inference Costs
Enterprises are starting to hit budget ceilings – unpredictable inference costs in cloud deployments threaten large-scale AI adoption. As performance improves, cost predictability matters more. Consider focusing on a mix of on-prem, cloud, and optimized inference engines to stay “cost-aware” while allowing for AI adoption at-scale. Read More →
Your Takeaway This Week: We’ve moved on from which model is best – it’s now about how fast your organization can implement the right combination of models, data systems, and integrations.
What’s emerging now is a playbook for modern enterprise AI:
- Pick fit-for-purpose models (SLMs for operations, LLMs for depth and complexity)
- Build governed data backbones
- Standardize with MCP
- Deploy modular, testable AI agents with real-world outputs (video, chat, docs)
- Test and swap regularly
The winners won’t be those with the biggest models – they’ll be the ones who deploy faster, govern smarter, and build effectively.
Annie Tsai 
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