Future Trends in Platform Engineering

One of the boldest takes from AI Engineer World’s Fair 2025:  𝗪𝗲’𝗿𝗲 𝗵𝗲𝗮𝗱𝗲𝗱 𝘁𝗼 𝗮𝗻 “𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝘁𝗮𝗰𝗸 𝘄𝗵𝗲𝗿𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗴𝗿𝗮𝗽𝗵𝘀 + 𝘁𝗼𝗼𝗹 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗳𝗶𝘅𝗲𝗱 𝗨𝗜𝘀; 𝗵𝗮𝗿𝗱-𝗰𝗼𝗱𝗲𝗱 𝗨𝗫 𝗮𝗻𝗱 ‘𝗔𝗣𝗜-𝘄𝗿𝗮𝗽𝗽𝗲𝗿 𝘀𝘆𝗻𝗱𝗿𝗼𝗺𝗲’ 𝘄𝗼𝗻’𝘁 𝗹𝗮𝘀𝘁.” This resonates deeply with what I’m seeing across software delivery, especially in the bigger enterprises. My take is that we’re witnessing 𝗮 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗵𝗶𝗳𝘁 𝗮𝘄𝗮𝘆 𝗳𝗿𝗼𝗺 𝗿𝗶𝗴𝗶𝗱 𝗨𝗜𝘀 𝘁𝗼𝘄𝗮𝗿𝗱 𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗮𝘄𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 that dynamically compose workflows based on intent and available tools. The developer of the future won’t want to navigate predetermined menus and forms. They’ll express intent (“deploy this service with these requirements”) and have the system intelligently orchestrate the right tools and workflows, dynamically. Some shifts I believe should happen in engineering teams is: • Internal developer platforms need to evolve from static portals to intelligent orchestration layers  • Software delivery toolchains must become composable and discoverable, not just integrated  • Teams investing heavily in hard-coded workflow tools may find themselves rebuilding sooner than expected The question isn’t whether this shift will happen - it’s how quickly organizations will adapt their delivery infrastructure to support truly flexible, agentic workflows. What’s your take? Are we ready to move beyond the comfort of predictable UIs toward more adaptive systems?

Does AI Kill Platform Engineering? AI is disrupting almost every layer of software. Code, testing, security, support, product management. It is reshaping how systems are built and operated. So it is fair to ask what it means for platform engineering. Two questions keep coming up in conversations with enterprise leaders, platform teams and investors: 1. If AI can operate infrastructure, why do we need platform engineering at all? 2. As AI infrastructure becomes dominant, do cloud-era platforms still matter? Let’s start with the first. The original case for platform engineering was productivity. Self-service. Golden paths. Reducing cognitive load. But if AI becomes the interface, that argument weakens. So what’s left? Control. Enterprises do not optimize purely for capability. They optimize for accountability. Someone still owns the cloud bill, the compliance audit, data residency, security posture, and the blast radius of failure. An AI agent can provision infrastructure. It cannot assume responsibility. As AI increases velocity, governance becomes more important, not less.And this is where declarative (intent based) APIs matter. Agents need structured, stable, idempotent interfaces. They need to declare intent, not execute fragile imperative steps. They need policy enforcement and reconciliation built in. Platform engineering becomes less about productivity tooling for humans and more about defining the declarative control plane that agents operate against. Now the second question. AI workloads introduce GPUs, accelerators, model registries, inference endpoints. But underneath, it is still compute, networking, storage, identity, policy, and cost. The workload changes. The hardware shifts. The need for a governed substrate does not. If anything, AI increases heterogeneity, cost volatility, and regulatory scrutiny. What I’m seeing in Fortune 500 companies: Platform teams are not shrinking. They are being asked to support traditional workloads plus AI infrastructure, across more clouds, at higher velocity, under stricter compliance. The scope is expanding. The real debate isn’t whether AI kills platform engineering. It’s whether enterprises still want sovereignty and policy control over infrastructure in an AI-driven world. From what I’m seeing, they clearly do. Curious what others are experiencing. Is AI shrinking your platform scope, or redefining it? #PlatformEngineering #AIInfrastructure #CloudNative #Crossplane #EnterpriseIT

By 2030, we won’t deploy to the cloud — we’ll negotiate with machine economies. Platform engineering is evolving faster than most people realize. The “infrastructure” part is getting eaten by AI agents that handle observability, SLOs, and half the toil that keeps us in pager hell today. The real shift? Autonomous compute markets. Workloads won’t live in one cloud. They’ll move — continuously — between on-prem, hyperscalers, and open GPU exchanges based on cost, latency, privacy, and carbon footprint. AI brokers will make millions of micro-decisions a day to rebalance that tradeoff. Hybrid will be the default. Ownership will make a comeback. “On-prem” will mean sovereignty, not legacy. Under the hood, WebAssembly, eBPF, confidential computing, and data fabrics will tie it all together — a universal substrate where applications, infrastructure, and AI speak the same language. The next generation of platform engineers won’t be button-pushers. They’ll be architects, economists, and philosophers — building living systems that can reason, adapt, and trade. We’re not just running workloads anymore. We’re shaping the culture of compute.

Last week, publicly traded software companies lost roughly $1 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗶𝗻 𝗺𝗮𝗿𝗸𝗲𝘁 𝘃𝗮𝗹𝘂𝗲. Over the past 20+ years I’ve had the privilege of building software and investing across several major platform shifts: from local software distributed on floppy disks with one-time license fees, to cloud-based SaaS with seat pricing, to fully cross-platform, real-time synchronized apps. What we are seeing happening right now is another giant platform shift 𝗳𝗿𝗼𝗺 𝗦𝗮𝗮𝗦 𝘁𝗼 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲. Historically, building and scaling successful software companies required millions in capital, large engineering teams, complex architectures, and years of operational execution. Defensibility largely came from execution complexity, software was hard. Well, that’s over. With the arrival of advanced models such as Codex 5.3, Opus 4.6 and other frontier systems, we are reaching a point where developers manage coding agents instead of writing code themselves, and entire products can be built with dramatically smaller teams. The barrier to creating high-quality software is collapsing faster than expected. At first glance, this shift feels existential for the software industry. If anyone can build software, is software itself becoming commoditized? Will defensibility disappear? Will successful products be instantly replicated by AI? 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 — 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝘂𝗶𝗹𝗱𝗲𝗿𝘀, 𝗳𝗼𝘂𝗻𝗱𝗲𝗿𝘀, 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀, 𝗮𝗻𝗱 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗶𝗻 𝗺𝗼𝗱𝗲𝗿𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗱𝗮𝗽𝘁? For decades, software was something humans logged into and operated. The next generation — 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 — will still be configured by humans, but will increasingly run continuously in the background, operating workflows 24/7: generating invoices, writing and deploying code, running marketing campaigns, analyzing data, coordinating operations across systems, managing machines, and handling customer support. Instead of selling tools used by employees, many companies will effectively deliver 𝗲𝗻𝘁𝗶𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝘀 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 — faster, cheaper, and continuously improving. Pricing models will gradually shift from seat-based subscriptions toward usage- and outcome-based economics, while the teams building these products become dramatically smaller and more productive than ever before. Every major platform transition reshapes how companies are built, how products are priced, and where value accrues. The shift toward agentic software may be the most profound platform transition the industry has experienced yet. PS: I’m deeply concerned about what this transition means for entry-level jobs. Our top priority should be to fund large-scale initiatives focusing on understanding how our societies, education systems, and economic models must adapt if a significant share of knowledge work can and will be automated by AI.

We just released the State of Platform Engineering Vol. 4, and it’s easily the most practical report we’ve published so far. This isn’t a “platform engineering is important” piece. It’s a snapshot of where teams actually are right now. What they’re measuring. Where adoption breaks. How security, AI, FinOps, and observability are colliding with platform work in very real, very messy ways. Some highlights that stood out to me: A surprising number of teams still don’t measure success at all. Adoption is still more push than pull in many orgs. And while almost everyone talks about platforms as products, only a fraction have the structures and incentives to support that claim. The gap between intention and execution is still huge. What’s different this year is how clearly platform engineering is bleeding into adjacent domains. Data, security, AI, cost, reliability. The report shows that the teams making progress aren’t treating these as separate initiatives anymore. They’re converging them through the platform layer. And AI? It didn’t make platform engineering obsolete. It made the cracks impossible to ignore. Teams trying to scale AI without strong platform foundations are feeling it fast. If you’re leading a platform team, building an IDP, or trying to justify the next phase of your initiative, this report gives you data points, language, and benchmarks you can actually use. Not theory. Not hype. Worth a read if you want to sanity check where you stand and where the industry is really heading. https://lnkd.in/eCh4HV9e

𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗜𝘀 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗛𝗼𝘄 𝗪𝗲 𝗥𝘂𝗻 𝘁𝗵𝗲 𝗖𝗹𝗼𝘂𝗱 The more time I spend leading infrastructure teams, the more convinced I am that Platform Engineering is becoming the backbone of modern cloud operations. It solves a problem every enterprise feels: developers want speed, while infrastructure teams need governance, security, and cost control. For years, those goals felt at odds. Platform Engineering finally aligns them. Golden paths and self service platforms are at the heart of this shift. 𝗚𝗼𝗹𝗱𝗲𝗻 𝗽𝗮𝘁𝗵𝘀 give teams a clear, pre approved way to build - secure, compliant, and cost efficient by default. They remove the guesswork and eliminate the “choose your own adventure” chaos that leads to cloud sprawl. 𝗦𝗲𝗹𝗳 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 take it further by giving developers the ability to provision what they need instantly, without waiting on tickets or navigating complex cloud policies. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹: faster delivery, fewer misconfigurations, predictable spend, and a dramatically reduced cognitive load for engineering teams. As AI workloads surge and cloud environments grow more complex, this 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹. A strong platform layer isn’t just an operational improvement - it’s a 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲. Platform Engineering is reshaping cloud resource management, and I’m excited for what this next chapter unlocks for teams, products, and the business.

Are we asking too much of our developers with "Shift Left"? 🛑 The industry trend has been to push more responsibility onto developers. But Google Cloud's latest guide to Platform Engineering suggests a "Shift Down" strategy—embedding complexity into the platform rather than the person. The guide outlines 6 key principles for building platforms that scale: 💼 Work Backwards from the Business Model Don't build in a vacuum. Align your platform investment and evolution directly with your organization's margins, risk tolerance, and quality requirements. 🛡️ Focus on Quality Attributes (NFRs) Reliability, security, and efficiency shouldn't just be goals; they are emergent properties of the system. "Shift down" by embedding these directly into the platform infrastructure. 🧩 Master Abstractions and Coupling Use abstractions to encapsulate complexity and control costs. Manage "coupling" intentionally—the right degree of interconnectedness allows the platform to enforce quality standards automatically. 🤝 Leverage Social Tools Tech isn't enough. You need shared responsibility, active education, and explicit policies (like "secure-by-design" APIs) to foster a culture that supports the platform. 🗺️ Use a Map Supporting diverse teams is complex. Use an "Ecosystem Model" to visualize how well your current controls match your business risks. Avoid over-constraining low-risk areas or under-protecting high-risk ones. 🏗️ Divide the Problem Space One platform doesn't fit all. Identify different ecosystem types—from "AdHoc" (flexible) to "Assured" (highly integrated/Type 4)—and apply the right level of oversight to each. The takeaway? Make active choices. Tailor your engineering to your business needs to maximize velocity without sacrificing quality. Read the full deep dive here: https://lnkd.in/eA72_DFR

Every decade removes a layer. This one removes the engineer. Token cost is falling - LLM inference costs are collapsing at a historic rate, roughly 10x per year for equivalent performance. In three years, GPT-3-level capability became about 1,000x cheaper, outpacing the cost declines of the PC and internet eras. This is the predictable result of better hardware, smaller models, improved training, and open competition. When a foundational input gets this cheap, new product categories become inevitable. This is how platform shifts begin. Here are the signs: 1. Coding models are getting better - SWE-bench scores increased from 12-15% in March 2024 to 65%+ by mid-2025. The benchmark tests production codebase resolution: models clone repos, parse issues, generate fixes, and pass existing tests. Top models now resolve real-world GitHub issues at rates matching junior engineer output. Performance doubled in 15 months across GPT-5 series, Claude 4 Sonnet, and reasoning models. 2. Engineering time allocation inverted: three days on implementation architecture, three hours on code execution. Founders now write technical specifications in the morning, review AI-generated production code by lunch. The traditional bottleneck of code generation collapsed. Engineer value shifted from implementation speed to system design accuracy. Development cycles that took weeks now complete in days. 3. What are frontier teams doing - SF teams pull support chat queries directly into technical specifications, converting customer complaints into feature requirements before touching code. Senior engineers allocate entire sprints to bottleneck analysis before Claude Code writes a line. Startups draft complete feature context from raw customer data, then hand it to agents. By nature of distillation, practices adopted in SF today become nationwide in six months, worldwide in twelve. 4. Requirements, Design, and DevOps - AI-generated specifications frequently hallucinate use cases: features built for workflows customers never requested. Tribal knowledge remains locked in 10-year veterans who remember why systems were architected in specific ways. DevOps engineers shifted from builders to gatekeepers, answering a single question: will this survive production load. The bottleneck moved from implementation capacity to architectural correctness. Teams no longer ask “can we build it” but rather “should we build it, and will it scale.” Now, Designers output in Figma, engineers in Cursor, product in Notion, DevOps in Terraform, sales in Salesforce. Context dies at every handoff point between these tools. Feature archaeology became standard practice: CEOs ask why features exist, nobody has documentation, original builders left the company. The infrastructure for persistent, collaborative technical context doesn’t exist. Whoever builds the coordination layer between customer requirements and system capabilities captures the software development platform for the next decade.

𝗔𝗜 𝗮𝗻𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: 𝗔 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 🤝 A thought-provoking article by Luca Galante on Platform Engineering explores the intersection of two transformative trends: Artificial Intelligence and Platform Engineering. It argues that these two fields are not only complementary but are also becoming increasingly intertwined, with platform engineering playing a crucial role in enabling and accelerating the adoption of AI. Key Takeaways: - Platform Engineering as the Foundation for AI: The article emphasizes that robust, scalable, and well-managed platforms are essential for effectively deploying and managing AI applications. Platform engineering provides the infrastructure, tools, and processes needed to support the unique demands of AI workloads. - Simplifying AI Adoption: Platform engineering can abstract away the complexity of AI infrastructure, making it easier for developers and data scientists to build, deploy, and manage AI models. This can significantly accelerate the time to value for AI initiatives. - Enabling Self-Service AI: By providing self-service tools and APIs, platform engineering can empower teams across the organization to leverage AI without needing specialized expertise. - Governance and Security for AI: Platform engineering can help ensure that AI systems are secure, compliant, and governed responsibly. This is particularly important for sensitive applications like those in healthcare or finance. - AI-Powered Platforms: The article likely also explores how AI itself can be used to improve platform engineering. For example, AI can automate platform management tasks, optimize resource allocation, and detect and resolve issues proactively. - The Future of AI and Platform Engineering: The article suggests that the collaboration between AI and platform engineering will only deepen in the future, leading to more intelligent, automated, and efficient IT systems. This article highlights the strategic importance of platform engineering in the age of AI. Organizations that invest in building robust and AI-ready platforms will be well-positioned to unlock the full potential of this transformative technology. ACL Digital, #AI #ArtificialIntelligence #PlatformEngineering #MachineLearning #Infrastructure #CloudComputing #DevOps #DigitalTransformation #Innovation #Technology

DevOps Engineer ≠ Platform Engineer (and how these roles are evolving in the AI era) Let’s clear this up ~ they’re not interchangeable roles, even though it can get confusing because the responsibilities often overlap. Also, AI-driven tooling and automation are blurring the lines faster than ever. DevOps Engineers focus on: • CI/CD pipelines and automation workflows • Infrastructure as Code (Terraform, Ansible, etc.) • Observability, monitoring, and incident response • Bridging Dev + Ops for faster releases • Managing integrations across environments and workflows Their world is about automation, delivery speed, and reliability. With AI entering the picture ~ they’re now: → Embedding LLM-based assistants into CI/CD flows → Using AI for anomaly detection in observability stacks → Automating incident triage and root cause analysis with AI-driven insights --- Platform Engineers focus on: • Building internal developer platforms (IDPs) • Abstracting infra into reusable, self-service modules • Managing multi-cluster, multi-cloud environments • Standardizing golden paths and governance • Enabling developer productivity at scale Their world is about scale, standardization, and developer experience (DevEx). And in the AI context ~ they’re now: → Integrating AI agents for platform observability and provisioning → Designing infrastructure for GPU workloads and inference pipelines → Building AI-ready platforms that support hybrid Dev + ML workflows (pretty in-demand) Here’s the key distinction: DevOps engineers operate pipelines. Platform engineers architect ecosystems. DevOps is about process. Platform is about productization. If you’re in either of these roles ~ it’s a really good time to be here. Both roles are steadily evolving toward AI-native engineering. Curious to hear your take.. what other distinctions do you see between these two roles? • • • I share weekly insights on Cloud, DevOps, and AI Infrastructure ~ Follow me (Vishakha) or check out my newsletter for more deep dives on how AI is reshaping the engineering stack.