Quema

The Enterprise AI crown is quietly changing hands. 👑🤖

For the last three years, OpenAI was the default choice for every tech company. But the data from March 2026 shows a massive architectural and financial shift.

Anthropic’s share of enterprise AI spending has climbed to 40%, while OpenAI’s share fell to 27%. When it comes to new business deals, Anthropic is now winning approximately 70% of head-to-head matchups against OpenAI.

Why is Anthropic winning the enterprise war? It comes down to trust, security, and the developer ecosystem:

🔹 Strict Security Principles: Recently, Anthropic walked away from a potential deal with the Pentagon. They refused to allow their technology to be used for mass surveillance of US citizens or autonomous weapons. 🔹 Market Backlash: The government labeled Anthropic a "supply chain risk". However, following OpenAI's $200 million deal with the DoD, uninstalls of the ChatGPT app jumped 295% overnight.

🔹 Developer Dominance: Claude Code now holds between 42% and 54% of the code generation market. In contrast, OpenAI sits at 21% in that specific market. Engineers are also reportedly eight times more likely to leave OpenAI for Anthropic than the reverse.

🔹 Financial Stability: OpenAI is projected to lose $14 billion in 2026. Meanwhile, Anthropic is projecting positive cash flow by 2027.

In Platform Engineering and DevOps, predictability and security trump hype every single time. Enterprises don't want the most "creative" model; they want the safest and most reliable one.

Which AI models are you integrating into your internal developer platforms today? Let's discuss! 👇

Will AI replace DevOps engineers? No. But engineers using AI will replace those who don't.

The "AI-Driven DevOps" approach is gaining massive traction. We see it streamlining workflows and catching bugs that humans miss.

We’ve summarized the key benefits and strategies in our new article: 🔗 [https://quema.co/news/ai-driven-devops/]

A question for my network: What is the biggest challenge you see in adopting AI for infrastructure management today? Trust? Cost? Or complexity?

Let's discuss in the comments! 👇

Your Startup Doesn't Need GPT-4. How Small Language Models (SLMs) Are Changing the Game.

For the last few years, the AI industry has chanted a single mantra: "Bigger is Better."

We chased trillions of parameters, infinite context windows, and data centers that consume the energy of small nations. But while everyone was staring at the clouds, a quiet revolution started right on our desks.

Welcome to the era of Small Language Models (SLM) and Edge AI.

Why is the focus shifting from massive LLMs (like GPT-4 or Claude) to compact models that can run on a standard laptop or even a smartphone?

I see three key drivers shifting the Enterprise and Consumer IT markets right now:

1. Privacy is the New Luxury 🔒 Businesses are tired of fearing leaks. Sending sensitive financial reports or medical data through an API into a third-party corporation's "black box" is a massive risk. A local model (like Llama or Mistral) running inside your perimeter (on-premise) or directly on an employee’s device guarantees data sovereignty. "What happens on your device, stays on your device."

2. Token Economics vs. Hardware 💸 Paying for every API call gets expensive, especially at scale. SLMs allow us to offload the computational burden to the user (Edge). With the rise of powerful NPUs in modern processors (Apple Silicon, Intel Core Ultra, Snapdragon X Elite), we can perform inference for "free" using hardware we already own, rather than renting expensive cloud compute.

3. Latency and Autonomy ⚡ The internet isn't perfect everywhere, and a two-second latency kills real-time UX. Local AI works instantly and without a network connection. This is critical for coding assistants, autonomous agents, and smart manufacturing.

What does this mean for us? We are moving from a model of "One Giant Brain for Everything" to a "Swarm of Specialized Agents." Instead of asking GPT-4 to write an email, check code, and summarize a meeting, we will use three different micro-models, each weighing just a few gigabytes and perfectly fine-tuned for its specific task.

Developers need to get comfortable with quantization, ONNX, and local inference. And businesses need to rethink their cloud budgets.

The future of AI isn't just in massive server farms. It’s in your pocket.

👇 Colleagues, have you tried running local models (Llama 3, Phi, Gemma) yet? Or do you still trust everything to the API?

Sustainability is not charity. It’s smart engineering. 📉🌱

We used to think of "Green IT" as a nice-to-have PR move. But in 2025, Sustainable Infrastructure is actually about Efficiency.

Every idle server, every unoptimized container, and every bloated pipeline isn't just increasing your carbon footprint. It is burning your cloud budget.

At Quema, we see a direct correlation: The cleaner your infrastructure, the higher your margins.

In our latest article, we dive deep into: ✅ Why the industry is shifting towards GreenOps. ✅ How sustainable architecture reduces TCO. ✅ The real impact of data centers on global energy consumption.

Stop paying for resources you don't use.

👇 Read the full breakdown here: https://quema.co/news/why-do-we-talk-about-sustainable-it-infrastructure-in-industry/