Google Cloud AI-First Development Trends

#AI-First Engineering is becoming the number 1 initial topic of discussion amongst Tech Leaders. It's no longer a matter of "if" or "when". It's happening now and BairesDev is right there in front.

Cloud-native solved a real problem. It gave engineering teams speed, portability, and scale they didn't have before. AI-first is a different kind of shift. The question isn't whether to use AI. It's whether you're building it into how your teams actually work or just adding another tool to the stack. Inside Google Cloud right now, infrastructure, data, and AI are converging. Developer workflows are getting AI-assisted. Platforms are becoming full development ecosystems. That's not a product announcement. That's a structural change in how software gets built. The teams winning in this environment are the ones treating AI as a workflow question, not a feature question. On April 8, a group of industry leaders are getting into what this actually looks like in practice. Worth your time if you're thinking seriously about what engineering leadership looks like in 2026. Details below.

Heading to Google Cloud Next '26 this week. The conversation has clearly moved beyond GenAI. What’s emerging now is something more structural: systems that don’t just generate, but act. But here’s what I am more interested in: Not the agents themselves. The systems they depend on. Because once you move from “assistive AI” to “autonomous execution”: 🔹 Data isn’t just consumed, it drives decisions in real time 🔹 Pipelines aren’t just transformations, they become part of execution flows 🔹 Governance isn’t about access anymore, it’s about controlling actions And this is where things get harder. Most platforms today weren’t designed for this level of coordination: - fragmented data foundations - inconsistent execution patterns - governance models built for users and not autonomous systems That changes the problem entirely. It’s no longer: “Do we have the right models?” It’s: “Do we have a platform that can support autonomous systems, without introducing new failure modes at enterprise scale?” Curious how others are thinking about this shift. #GoogleCloudNext #DataPlatform #AIPlatform #PlatformEngineering #AgenticAI

I just came across a great new Google Cloud paper shared by Richard Seroter on what to think about when preparing for agent-native infrastructure. It’s a useful reminder that the infrastructure assumptions we built for cloud-native systems do not automatically carry over to agentic systems. As we move into this new era, the questions change: How do we think about autonomy, orchestration, state, observability, governance, and control when software is no longer just executing deterministic workflows? That shift matters. Cloud-native helped us build for scale, resilience, and portability. Agent-native will push us to design for reasoning, interaction, and safe execution in more dynamic environments. Worth a read if you are building with AI agents or thinking about what infrastructure needs to look like next. Direct Link: https://lnkd.in/d7_6Ykeg #AI #GoogleCloud #CloudNative #AgenticAI #Infrastructure #PlatformEngineering #MLOps #GenAI

Looking forward to Google Cloud Next ’26 in Vegas this week with Miracle Software Systems. This year’s focus on agentic AI, modern infrastructure, and real-world AI adoption is especially relevant — from modernizing apps and databases to operationalizing data and AI, and turning these capabilities into repeatable accelerators that drive faster time-to-value. At Miracle, we’re actively building App, DB, Data, and AI accelerators to help enterprises move faster from strategy to execution. If you’re attending, stop by Booth #3111 and drop me a message — let’s connect! #GoogleCloudNext #GenAI #AgenticAI #Cloud #Data #AI #Innovation Miracle Software Systems, Inc

AI isn’t just about asking questions anymore. It’s about building systems that take action. I just wrapped up the Google Cloud Technical Series: AI in Action, and the shift in the landscape is unmistakable. We are moving rapidly from static chat interfaces to dynamic Agentic CX. Over two days of intensive sessions and hands-on labs, I focused on: • Deploying Gemini Enterprise for production environments. • Building speech and voice agents that actually feel human. • Bridging the gap between Data Engineering and low-code App Development. The most important takeaway? The "Magic" of AI is becoming a "Utility." The real value now lies in how we integrate these agents into existing workflows to solve complex customer problems. It was a privilege to dive deep into the technical weeds with the Google Cloud team. Now, it’s time to put these tools to work. How is your team moving from "AI experimentation" to "Agentic execution" this year? Let's swap notes in the comments. ⬇️ #GoogleCloud #GenerativeAI #Gemini #AIAgents #CloudComputing #ContinuousLearning

𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗡𝗲𝘅𝘁 𝗶𝘀 𝗯𝗿𝗶𝗻𝗴𝗶𝗻𝗴 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝘁 𝗮 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁 𝗳𝗼𝗿 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜. AI is moving from experimentation to becoming a production layer of the enterprise. Many organizations struggle to make this happen quickly enough. 𝗔𝘀 𝗮 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗣𝗿𝗲𝗺𝗶𝗲𝗿 𝗣𝗮𝗿𝘁𝗻𝗲𝗿, 𝘄𝗲 𝗯𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗿𝗲𝗮𝗹: • Embedding AI into software delivery • Scaling agentic workflows with governance • Modernizing platforms to support AI at scale Our team will be on the ground in Vegas connecting with leaders working through these challenges. 📍 Google Cloud Next 📅 April 22–24 📌 Las Vegas, NV 𝗜𝗳 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗮𝘁𝘁𝗲𝗻𝗱𝗶𝗻𝗴, 𝘀𝗲𝗰𝘂𝗿𝗲 𝘀𝗼𝗺𝗲 𝘁𝗶𝗺𝗲 𝘄𝗶𝘁𝗵 𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗼𝗻𝘀𝗶𝘁𝗲: https://okt.to/mn4tZ2 #GoogleCloudNext #AIinProduction #CloudAI #EnterpriseAI #Xebia

The "AI in Production" concept is more than just the models—it's about data and security foundations to make Agentic AI systems work at scale. I'll be heading to #GoogleCloudNext to dive into these updates, specifically looking at how we can build robust, secure data systems for agentic workflows. Who else is going to be in Vegas? I’d love to meet up, share notes on the latest in Agentic AI data, security, and infrastructure, and talk shop. Let me know if you'd like to meet up.

Enterprise AI is reaching a pivotal moment—transitioning from trial phases to significant production results. The focus has shifted from whether to implement AI to how swiftly it can be scaled efficiently. Excited for the discussions at Google Cloud Next. 👇 #GoogleCloudNext #EnterpriseAI #AIInProduction #CloudAI #GenerativeAI #AIAtScale #DigitalTransformation #Xebia

Uber’s move to adopt AWS Trainium and Graviton is easy to read as a cloud migration. It’s actually a systems architecture decision. Modern AI infrastructure is no longer a single layer. It is a tightly coupled stack: -Compute → custom silicon (Trainium for training, Inferentia for inference, Graviton for general-purpose workloads) -Accelerators → specialized tensor/matrix units optimized for deep learning -Networking → ultra-low latency, high-bandwidth interconnects (EFA, Nitro) enabling distributed training -Storage → high IOPS (input/output operations per second), parallel file systems for large-scale datasets -Orchestration → managed systems for training, fine-tuning, and serving models On top of this sits a distinct layer: The model layer. Foundation models such as GPT, Claude, and Gemini represent the intelligence but they are increasingly decoupled from the infrastructure they run on. This is where AWS is making a differentiated move. Through Bedrock, AWS is not just providing infrastructure; it is introducing a model access layer that supports multiple foundation models (Anthropic, open models, and others) on the same underlying stack. This creates two important properties: • Infrastructure optimization (cost, performance, scalability) independent of model choice • Model optionality-the ability to select or switch models based on use case Other cloud providers are building similar capabilities, but with different architectural philosophies: Azure is developing its own silicon (Maia, Cobalt) while tightly integrating OpenAI models, creating a more coupled, application-first ecosystem. Google Cloud combines TPUs (Tensor Processing Units) with its own models like Gemini, emphasizing vertical integration across hardware and intelligence. So Uber’s decision is not just about performance. It is about choosing an architecture where infrastructure and intelligence can evolve independently, while still operating as a coherent system. Because at scale, the constraint is no longer model capability. It is how efficiently, reliably, and flexibly that capability can be deployed. #AI #AWS #Azure #GoogleCloud #Infrastructure #DistributedSystems #GenAI