{"slug": "why-open-infrastructure-will-define-the-ai-era", "title": "Why open infrastructure will define the AI era", "summary": "JetBrains Research found 74% of developers use AI tools, with Claude Code becoming the most popular coding assistant since May 2025, according to Jellyfish's 2026 report. Industry leaders warn that reliance on proprietary AI platforms creates vendor lock-in, advocating for open-source infrastructure to maintain control and avoid perpetual rental costs.", "body_md": "A new form of vendor lock-in is here. And it’s not proprietary languages or rigid enterprise software suites — it’s something more fundamental. It’s the very thing that writes the code.\n\n[JetBrains Research](https://blog.jetbrains.com/research/2026/04/which-ai-coding-tools-do-developers-actually-use-at-work/) found that 74% of developers worldwide use AI tools. [Claude Code](https://www.infoworld.com/article/4136718/claude-code-is-blowing-me-away.html), available only since May 2025, is now the most popular AI coding tool, followed by [Gemini Code Assist](https://www.infoworld.com/article/3829347/review-gemini-code-assist-is-good-at-coding.html) and [GitHub Copilot](https://www.infoworld.com/article/3609013/github-copilot-everything-you-need-to-know.html), according to Jellyfish’s 2026 [State of Engineering Management Report](https://jellyfish.co/resources/2026-state-of-engineering-management-report/).\n\nThe latter study also found that 91% of developers say their productivity has increased in the past 12 months. As coding output [expectations are rewritten daily](https://leaddev.com/ai/openai-says-there-are-easily-1000x-engineers-now), the engineering world is becoming heavily reliant on paid external AI services.\n\n[Gartner predicts](https://www.linkedin.com/posts/markwoneill_how-to-optimize-token-consumption-for-ai-activity-7458329480994992128-AT9m?utm_source=share&utm_medium=member_desktop&rcm=ACoAAA-8zTABlsmtYe-zC-Uf5z3oD5nm6qXDVVo) that by 2028 spending on AI coding tokens could exceed developer salaries. Yet, [tokenmaxxing](https://www.infoworld.com/article/4183060/the-tokenmaxxing-backlash-is-coming.html) while [vibe coding](https://www.infoworld.com/article/4166817/vibe-coding-or-spec-driven-development-how-to-choose.html) through a vendor’s cloud-based API feels like a far cry from the open foundations of free programming languages and open models, which many of today’s AI platforms now abstract.\n\n“Open infrastructure will be the backbone of the AI era,” says [Peter Farkas](https://www.linkedin.com/in/farkasp/), CEO of [Percona](https://www.percona.com/), a provider of open-source database solutions. “Right now, too many companies are building their entire AI strategy on top of proprietary platforms because the convenience is seductive.”\n\n“It’s ‘three clicks’ to stand up a database or an AI service in a hyperscaler, and that convenience blinds people to the lock-in they’re signing up for,” he adds. “As AI workloads mature, organizations will realize that depending on one vendor for their data, models, runtime, and pricing is not a strategy.”\n\n[AI-assisted coding](https://www.infoworld.com/article/3973969/knowing-when-to-use-ai-coding-assistants.html) is democratizing software engineering for non-engineers and [accelerating top performers](https://leaddev.com/ai/ai-doesnt-create-great-developers-it-amplifies-them). But if teams are always working within the confines of how one platform thinks the world should work, it could create locked-in toolsets at scale. And as [AI platform costs rise](https://techcrunch.com/2025/12/29/2025-was-the-year-ai-got-a-vibe-check/), a fundamental question arises: will software developers consume AI on their own terms, or on someone else’s?\n\nThere’s a strong case that the long-term winners in tech will be built on open-source standards and foundations, similar to the history of cloud-native computing and the internet itself.\n\n“Open always wins,” says [Brian Alvey](https://www.linkedin.com/in/brianalvey/), CTO at [WordPress VIP](https://wpvip.com/), a managed WordPress hosting platform. “Not because it’s a fancy ideology, but because it gives you total freedom to adapt, evolve, and stay in control.”\n\nOpen infrastructure avoids a future where developers perpetually rent. “For AI to be useful to people at large, it can’t be something you’re paying rent for the rest of your life,” says [Manik Surtani](https://www.linkedin.com/in/maniksurtani/), CTO and co-founder of the [Agentic AI Foundation](https://aaif.io/) (AAIF), a vendor-neutral home for open-source agentic AI technologies. “And it can’t be concentrated in one particular corporation or a small handful of corporations, because we know how that goes.”\n\nAI development today is traveling two parallel paths. On one path, [open-source AI](https://leaddev.com/technical-direction/be-careful-open-source-ai) is thriving and fueling tremendous growth in the number and variety of AI models and tools. Just take the thousands of open-weight models on [HuggingFace](https://huggingface.co/), the community around the [OpenClaw](https://openclaw.ai/) AI agent, or the many academic institutions publishing [new breakthroughs](https://thenewstack.io/llms-can-now-trace-their-outputs-to-specific-training-data/).\n\n“Open-source models and tooling are hot on the heels of state-of-the-art, with interesting and boundary-pushing work being shared by labs and researchers across the world,” says Austin Parker, director of AI strategy at [Honeycomb](https://www.honeycomb.io/), an observability platform provider, citing frontier open-source models like [Mistral](https://mistral.ai/), [DeepSeek](https://github.com/deepseek-ai/deepseek-v3), and [Ai2’s OLMo](https://allenai.org/olmo2) as examples.\n\nOthers agree. “There’s unprecedented openness at the model and tooling layer, with open-source models, frameworks, and orchestration advancing at remarkable speed,” says [Mark Collier](https://www.linkedin.com/in/markcollier/), general manager of AI and infrastructure at the [Linux Foundation](https://www.linuxfoundation.org/).\n\nOn the other path, we’re seeing heavy reliance on proprietary AI systems controlled by Anthropic, Cursor, Google, Microsoft, OpenAI, and others. As Collier says, “Many platforms are wrapping those open components in closed, opinionated interfaces that trade short-term speed for long-term constraints.”\n\n[Open source](https://www.infoworld.com/article/2262355/what-is-open-source-software-open-source-and-foss-explained.html) and the AI tooling market don’t always mix well. LangChain’s [Open Agent Platform](https://github.com/langchain-ai/open-agent-platform), for instance, was open-sourced to much fanfare in 2025, but by 2026 had been deprecated, with the repository now recommending fully managed alternatives.\n\nFor [Roman Shaposhnik](https://www.linkedin.com/in/shaposhnik/), co-founder and CTO of [Ainekko](https://nekko.ai/), provider of an open-source, composable AI stack, the current AI platform landscape is reminiscent of [low-code and no-code platforms](https://devops.com/demystifying-the-low-code-category/), which promised democratization of software development but often [failed to deliver](https://www.infoworld.com/article/3958483/7-reasons-low-code-and-no-code-tools-fail-to-deliver.html), becoming synonymous with platform lock-in and inflexibility.\n\n“Honestly, it feels familiar,” Shaposhnik says. “We have incredibly powerful AI tools right now, but most of them come bundled as tightly controlled platforms.” This is a risk for AI, he says, because the infrastructure, models, and hardware are tightly coupled. “If those layers are closed, you lose flexibility fast.”\n\nSome abstractions that sit on top of models, like routing and agent frameworks, tend to be tightly coupled and optimized for certain models. Other platforms take the walled garden concept quite literally. Anthropic, for instance, has repeatedly made headlines for blocking access to its Claude models over [vague policy violations](https://www.tomshardware.com/tech-industry/artificial-intelligence/anthropic-nuked-a-companys-access-to-claude-stopping-60-employees-dead-in-their-tracks-support-via-google-form-is-the-only-recourse-for-vague-usage-policy-violation). The company recently shut off [competitor xAI’s use](https://venturebeat.com/technology/anthropic-cracks-down-on-unauthorized-claude-usage-by-third-party-harnesses) and [stonewalled OpenCode](https://thenewstack.io/anthropic-agent-sdk-confusion/), drawing community backlash.\n\nMoves toward increasingly closed systems don’t bode well for an AI economy already built on shaky economics. As [Vikram Srivats](https://www.linkedin.com/in/vikramsrivats/), head of product experience at [WaveMaker](https://www.wavemaker.com/), provider of an agentic application development platform, adds, “Given the unit economics of AI tooling and pace of accelerated change to keep up, it seems obvious that some will evolve to more of a closed system to be able to monetize and gain ROI.”\n\nReliance on proprietary AI platforms can create long-term operational dependencies. As systems become less interoperable, organizations may be forced to standardize on a single stack across data pipelines, models, and decision logic, says the Linux Foundation’s Collier.\n\n“As infrastructure consolidates, enterprises become more exposed when platforms change direction, raise prices, or fall behind technically,” he says. “If you can’t change platforms without re-architecting your AI systems, you’ve already given up too much control.”\n\n“When you build on someone else’s platform, you have to live by their rules and those rules always change,” adds WordPress VIP’s Alvey. “We’ve all seen this before, businesses wasting time and money building to serve Google, Facebook, YouTube, and the App Store, instead of building to serve their customers.”\n\n[Platform lock-in](https://www.infoworld.com/article/2337012/get-used-to-cloud-vendor-lock-in.html) can also create direct business risk. As Ainekko’s Shaposhnik says, “It usually shows up as higher costs, fragile systems, and growing risk when it’s time to change direction.”\n\nAt Ainekko, an internal group called the [AI Plumbers](https://www.eejournal.com/article/do-you-want-to-be-an-ai-plumber/) focuses on back-end AI infrastructure like inference, scheduling, memory, and hardware integration. “Their view is simple,” says Shaposhnik. “If those layers are closed, everything above them becomes fragile.”\n\nOpen standards, interfaces, and infrastructure provide a necessary hedge against closed systems to prevent this sort of fragility. “In the AI era, open infrastructure gives enterprises control, portability, and choice at exactly the time they need it most,” says Percona’s Farkas.\n\nIt can cost upwards of $100,000 to migrate enterprise software, [according to Cloudaware](https://cloudaware.com/blog/cloud-migration-costs/), making portability a major enterprise concern. From this perspective, procuring closed systems can become a costly architectural dependency.\n\nOthers argue that openness is a critical hedge against vendor concentration risks at large, especially if AI replaces human labor en masse. “If all of that economic value is now being concentrated in the hands of one or two companies,” says the AAIF’s Surtani, “that’s an order of magnitude bigger problem than we’ve seen in any other wave of computing.”\n\nInstead, open foundations allow adaptability to evolving conditions so enterprises can swap out models, agents, data, hardware, and orchestration, as needed. “Open standards let those components change independently without breaking the system,” says Collier.\n\nOpenness can also help future-proof businesses against economic upheaval. “Open everything will help build a cushion for businesses and users to survive and thrive after the almost-certain correction in the current hype cycle,” says WaveMaker’s Srivats.\n\nAt the industry level, momentum toward open AI infrastructure is growing. The [establishment](https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation) of the [Agentic AI Foundation](https://aaif.io/author/aaif/), Anthropic’s donation of [Model Context Protocol](https://www.infoworld.com/article/4029634/what-is-model-context-protocol-how-mcp-bridges-ai-and-external-services.html) (MCP), and Block’s donation of its [Goose agent](https://aaif.io/projects/goose/) are significant ecosystem-wide moves toward openness. Other advances include the donation of [llm-d](https://thenewstack.io/llm-d-cncf-kubernetes-inference/), a [Kubernetes](https://www.infoworld.com/article/2266945/what-is-kubernetes-scalable-cloud-native-applications.html) framework for LLM inference, to the Cloud Native Computing Foundation (CNCF).\n\nFor Parker, donations like this help ensure long-term support and care. “Open standards aren’t just the foundation of the internet, they’re the foundation of the AI space,” he says. “I predict that we’ll see these practices continue, especially as enterprise adoption increases in earnest,” he adds.\n\nStill, some question whether this level of stewardship is enough for a rapidly evolving ecosystem. “The internet benefited early on from groups that helped keep vendors aligned,” says Shaposhnik. “In AI infrastructure, we don’t really have that yet.”\n\n“All of us open source veterans are hopeful,” he says, “but we also need to adapt to this new reality in what we do regarding AI infrastructure.”\n\nBeyond industry governing bodies, companies themselves are also spearheading open AI initiatives. Warp, an agentic development environment, recently [went open source](https://thenewstack.io/warp-open-source-client/) amid closed-source rivals. Arcade.dev, meanwhile, is pushing an open-source [Agent Library](https://www.arcade.dev/blog/agent-library/) for agentic memory.\n\nWhile AI infrastructure can be open in many ways, a few layers stand out as especially important. First is the openness of the model itself. “Open-source models must be the foundation of future trust and value,” says WaveMaker’s Srivats.\n\n“The forms of open infrastructure that reduce integration friction and accelerate adoption stand out,” adds [Neeraj Abhyankar](https://www.linkedin.com/in/neeraj-abhyankar-9040141/), VP of data and AI at [R Systems](https://www.rsystems.com/), a global digital solutions provider. For him, open model representation formats, open orchestration and execution layers, open agentic protocols, and open governance and metadata standards are all essential for enterprise flexibility.\n\nOthers place more value on the connective tissue between AI components. “The most important forms of open infrastructure are the ones that connect systems together,” says Collier. “That includes open APIs, metadata standards, identity and policy frameworks, and protocols for how models and agents communicate.”\n\nArguably, [MCP](https://www.infoworld.com/article/4096223/10-mcp-servers-for-devops.html) has become the connective tissue between AI agents and the [broader API ecosystem](https://thenewstack.io/how-to-prepare-your-api-for-ai-agents/). “If we get MCP right we unlock the same level of interoperability between entities on the web and models driving them as we came to enjoy during the Web 2.0 era and the API-first boom,” says Shaposhnik. “If we don’t we risk massive proprietary lock-ins.”\n\nParker agrees that open protocols will underlie future AI progress. “We’ll see continued development and progress on AI agents which will rely on protocols like MCP and ACP [[Agent Client Protocol](https://www.infoworld.com/article/4007686/a-developers-guide-to-ai-protocols-mcp-a2a-and-acp.html)] to interoperate with various clients and each other,” he says. Yet a gap remains around API conventions for models. “It would be nice if we could get a commitment from model providers to use a standard here.”\n\nFor the AAIF’s Surtani, opening up the protocol layer is the most important aspect. “I think it’s really important for interoperability, for choice,” he says. “It means you can bring your own agent, you can bring your own framework, you can bring your own harness, and pick what model you want.”\n\nOpen standards may also play a significant role within [inference architecture](https://www.infoworld.com/article/4117620/edge-ai-the-future-of-ai-inference-is-smarter-local-compute.html). “As AI expands to the edge, developers need visibility into how models run, how memory is used, and how performance scales,” says Shaposhnik. Open systems could make it easier to optimize, debug, and adapt while helping enterprises avoid observability fragmentation.\n\nLastly, [cloud-native architectural standards](https://www.infoworld.com/article/3498485/the-future-of-kubernetes-and-cloud-infrastructure.html) are a key ingredient for open AI infrastructure. “We’re seeing Kubernetes become the missing link for people who want the hyperscaler-style convenience without hyperscaler lock-in,” says Percona’s Farkas. For him, Kubernetes has become the de facto hybrid enterprise deployment option for data, workloads, and AI components.\n\nThe [2026 State of Open Source Report](https://opensource.org/blog/the-2026-state-of-open-source-report) found avoiding vendor lock-in to be the primary driver of open source adoption. But beyond being a strategic decision for a single company, open infrastructure provides a layer for entire industries to be built upon.\n\nArguably, the internet itself is evidence of this, where groups like the [IETF](https://www.ietf.org/) and the [IEEE](https://www.ieee.org/) were instrumental in defining the fundamental protocols. “Without open protocols we would’ve been in telco hell and without phenomenons like Google or Facebook,” says Shaposhnik.\n\nOr, take the [history of Linux](https://www.infoworld.com/article/2335646/thirty-two-years-of-linux-and-its-community.html) as a parallel. “Linux became the default operating system because it offered a common, vendor-neutral foundation that everyone could build on,” says Collier. “In the AI era, open infrastructure will define the layers that organizations rely on for long-term continuity.”\n\nAt the infrastructure level, open standards have repeatedly underpinned major platform shifts, from [Docker](https://www.infoworld.com/article/2253801/what-is-docker-the-spark-for-the-container-revolution.html) to [Kubernetes](https://www.infoworld.com/article/3812622/will-kubernetes-ever-get-easier.html). The question now is whether AI will develop a similarly durable standards layer.\n\nFor Parker, it’s too early to say, but the current growth of AI mirrors the early cloud. “Remember that it took many years before we saw the development and popularization of the open source cloud-native ecosystem,” he says. “I think it would be a mistake to extrapolate from the current trajectory towards a closed, proprietary future.”\n\nOthers agree the future must be rooted in openness. “I see open infrastructure becoming the foundation of enterprise AI,” says R Systems’s Abhyankar. “As systems become more distributed and agent‑driven, closed ecosystems simply won’t scale.”\n\nThe groundwork is being laid through open agentic protocols, open frameworks, and industry support intended to reduce fragmentation around proprietary standards.\n\n“Ironically, the AI movement has mostly seemed to learn from the mistakes of the past and is starting off on a more open foot,” says Parker. “Over time, I believe we’ll see innovation and openness thrive.”", "url": "https://wpnews.pro/news/why-open-infrastructure-will-define-the-ai-era", "canonical_source": "https://www.infoworld.com/article/4186382/why-open-infrastructure-will-define-the-ai-era.html", "published_at": "2026-06-22 09:00:00+00:00", "updated_at": "2026-06-22 09:12:12.992859+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-tools", "ai-ethics", "developer-tools", "ai-policy"], "entities": ["JetBrains Research", "Claude Code", "Gemini Code Assist", "GitHub Copilot", "Jellyfish", "Gartner", "Percona", "WordPress VIP"], "alternates": {"html": "https://wpnews.pro/news/why-open-infrastructure-will-define-the-ai-era", "markdown": "https://wpnews.pro/news/why-open-infrastructure-will-define-the-ai-era.md", "text": "https://wpnews.pro/news/why-open-infrastructure-will-define-the-ai-era.txt", "jsonld": "https://wpnews.pro/news/why-open-infrastructure-will-define-the-ai-era.jsonld"}}