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Author: Jenn Yarnold

Futurum Signal: AI-Powered Market Intelligence for DevOps and Platform Engineering
If you’re building tools and platforms for the DevOps and platform engineering community—or you’re on the buying side, trying to decide which ones to adopt—you’ve probably noticed a frustrating truth: most market analyst reports don’t keep up with the pace of the software delivery world.

We’re in an era where a new CI/CD integration, a Kubernetes operator, or an AI-driven deployment pipeline can drop this week and materially shift a vendor’s value proposition. Yet, the analyst industry is still largely built on static Magic Quadrants, Waves, and similar reports—snapshots frozen in time.

Futurum Signal changes that. It’s a real-time, AI-powered, predictive evaluation platform designed to track markets as they actually move—making it highly relevant for fast-changing technology segments like DevOps, platform engineering, and the tools ecosystem.

Why This Matters for DevOps Vendors

If you’re a DevOps or platform engineering vendor, you already know visibility in the right market context can be the difference between winning a deal and being overlooked.

Futurum Signal can help you:
- Get credit faster when you release new features, integrations, or capabilities—without waiting for the next quarterly analyst report.
- Show dynamic proof of execution in real time, thanks to AI agents monitoring public announcements, product documentation, and ecosystem activity.
- Leverage customer voice data from Futurum’s partnership with G2, providing authentic adoption and sentiment insights.
- Earn the “Future Proof Badge”—a vendor marketing asset awarded to solutions that hit “Elite” rankings across all five evaluation dimensions.
This means your product narrative isn’t trapped in a static report—it’s evolving live, alongside your roadmap.

Why This Matters for DevOps Buyers

If you’re on the other side of the equation—an enterprise or team leader deciding which tools, platforms, or cloud services to invest in—Signal can be your decision radar.

It can help you:
- Spot emerging leaders before they hit everyone’s short list.
- Understand vendor execution probability—how likely they are to deliver on their roadmap.
- See the real market impact of toolchain updates, acquisitions, and integrations as they happen.
- Compare vendors visually with Future Proof Radar, Heat Maps, and Comparison Bubbles that reflect the current state of the market—not last quarter’s.
For DevOps leaders facing tighter budgets and higher expectations, this kind of real-time intelligence can prevent costly mistakes and uncover high-value bets.

How Futurum Signal Works

Signal uses agentic AI workflows to continuously ingest and analyze:
- Publicly available market and product data
- Vendor announcements and release notes
- Technical documentation and integration activity
- Voice-of-the-customer insights from G2
- Proprietary Futurum research datasets
This data flows through RAG pipelines and Google Gemini models, producing scoring across five categories that matter in fast-moving tech segments:
1. Business Value Index
2. Product Innovation & Technology Capabilities
3. Strategic Vision
4. Go-To-Market Execution
5. Ecosystem Alignment
Human analysts validate every score and flag anomalies before updates go live—so you get the speed and scale of AI with the judgment of experienced industry experts.

Built for Fast-Moving Markets Like DevOps

DevOps and platform engineering don’t stand still. A new Terraform module, a game-changing API for GitOps, or a fresh security scanning capability in your pipeline can instantly shift market perception. Futurum Signal is built to capture that movement.

Instead of relying on an outdated report cycle, you get dynamic, predictive insights that evolve with your ecosystem. It’s not just a reflection of the market—it’s a living map.

First Up: Intelligent Data Platforms—Then DevOps and Cloud

The first Futurum Signal report drops August 20, covering Intelligent Data Platforms. That may sound a little upstream from the DevOps toolchain, but it’s directly relevant to platform teams wrestling with data integration, observability pipelines, and AI-ready infrastructure.

Coming this fall: Signals on Agentic Platforms, Cybersecurity Platforms, and Cloud Vendors—all highly relevant for DevOps and platform engineering decision-makers. And given how central cloud-native security, AI integration, and developer productivity are to our space, I expect these Signals will quickly become essential reference points.

My Take

Whether you’re building the next must-have DevOps tool or trying to future-proof your platform engineering stack, you need intelligence that moves at the speed of the market. Futurum Signal delivers that—AI-powered, analyst-validated, and grounded in real customer experience.

This is market analysis built for the continuous delivery age. If you’re serious about understanding where the market is headed—not just where it’s been—go check it out. And as always, tell them Shimmy sent you.
August 13, 2025
Stack Overflow Survey Shows AI Adoption for Devs

We’re in the middle of a developer renaissance powered by AI — yet a large portion of the community remains cautious, especially when it comes to the next frontier: Agentic AI. The just‑released 2025 Stack Overflow Developer Survey paints this nuanced portrait vividly: 84% of respondents use or plan to use AI tools in their workflows, yet less than a third trust their accuracy — just 29% this year, down from much higher levels in prior surveys.

A Tale of Two AI Worlds: Generative vs. Agentic

On one hand, generative AI tools — ChatGPT, GitHub Copilot and others — have become near-ubiquitous. OpenAI’s GPT models are used by 81–82% of developers, followed by Claude Sonnet (~43–45%) and Gemini Flash (~35%). And more than half of professional developers (50.6%) use AI tools daily. It’s mainstream.

But agentic AI — AI that takes multiple-step, autonomous actions — remains emergent. Only 31% are currently using AI agents, 17% plan to, and a significant 38% plan not to use them at all. That aligns with the Reddit‑driven takeaway that nearly 40% aren’t on board with agents — at least for now.

Is that believable? Yes — and instructive. Developers are pragmatists. They’ll use tools that reliably boost productivity — like autocomplete, code suggestions and troubleshooting helpers — but they’re not yet ready to delegate multi-step tasks to AI agents. The maturity, transparency and accuracy simply aren’t there yet.

Warming Up to AI — But with Side-Eyed Caution

The broad adoption of generative AI is undeniable, but trust is eroding. Favorable sentiment toward AI tools overall has dropped from over 70% in prior years to around 60% in 2025. About 46% of developers say they don’t trust the accuracy of AI outputs, up sharply from 31% last year.

Why the distrust? “AI outputs are almost right — but not quite,” says 45% of developers; 66% spend more time debugging AI-generated code than expected. And when things get complex, they still turn to humans: 75% report they’ll still ask another person for help when they don’t trust AI answers.

This is the developer four‑step pattern with AI: Use it, verify it, understand it and only delegate if it’s proven.

Claude Leads (But Narrowly), ChatGPT Reigns

Stack Overflow’s survey shows Claude Sonnet is the most admired AI model, though OpenAI’s GPT remains the most used. Specifically, Claude garners admiration (61.2%) vs GPT (67.5%) and is the second most desired. For usage, GPT leads (~82%), Claude Sonnet is in the mid‑40s, and Gemini Flash is around 35%.

The takeaway? Developers are exploring beyond ChatGPT even as it holds the share lead, but admiration and curiosity swirl around newer players like Claude.

The State of Transition

All the signs point to a community in transition. Generative AI is widely adopted and embedded across workflows, from writing code to learning new frameworks. Yet agentic AI remains cautious territory, held back by trust, reliability and real-world complexity concerns. Sentiment toward AI has cooled, trust has declined and developers are consciously balancing automation with oversight.

But this is not pessimism — it’s maturity. The script is no longer “AI will save us all.” The script is “AI helps us, but it’s still a tool, not a trusted partner — yet.”

Final Word: Developers in Flux, AI Accelerating Wisely

The 2025 Stack Overflow survey tells a compelling story: Developers are rapidly adopting AI, favoring trusted generative models while keeping agentic AI at arm’s length. Satisfaction may have dropped, but usage remains high. Favorites like ChatGPT dominate, but admiration for Claude signals emerging shifts in preference.

We’re not at the agentic AI tipping point — yet. But the foundations are being laid. As tools mature, trust evolves and real-world utility proves itself, developers will increasingly invite agentic AI into their toolchains — but on their terms, with oversight and only when it earns its keep.

That’s not hesitancy. That’s sensibility. It’s the hallmark of an ecosystem moving smartly, not blindly, toward the future.

August 12, 2025
AI-Driven Observability: Fast, Context-Rich MCP Servers

Christine Yen—developer-turned-CEO—explains why Honeycomb built an MCP server and why every observability vendor may follow. MCP, short for Model Context Protocol, acts like a concierge for AI agents: It makes a product’s API, telemetry schema and helper tools easily discoverable so large-language models can ask precise questions instead of guessing. Yen sees it as table stakes for letting bots troubleshoot production just as humans do.

Speed is the first requirement. Engineers won’t wait minutes for an LLM to fetch a log slice or distribution graph. Honeycomb’s mantra—fast is a feature—now pays off when an agent fires off dozens of sub-queries during root-cause analysis. Yen jokes that no one wants to brew coffee while a model thinks, so the platform’s near-instant queries translate directly into snappier conversational answers.

Depth of data is the second pillar. Because customers pipe richly labeled events (“checkout_latency_ms”, “user_tier”) instead of fixed dashboards, even off-the-shelf models can map plain-English prompts to meaningful fields. Yen argues this beats the “narrow metric lists” many teams still rely on; a model that understands business context can pinpoint anomalies without endless prompt engineering.

Yen admits she no longer remembers every field in Honeycomb’s own dog-food cluster—but the MCP server does. She expects newcomers to treat agents as default copilots, while power users toggle between raw queries and natural language. The challenge for vendors, she says, is balancing both paths without forcing engineers into one camp.

Yen’s closing advice: start experimenting now, map where your context lives and demand that every tool expose it through open protocols. The future observability stack won’t just collect data; it will serve it—fast and in the right context—to whatever human or agent asks first.

August 4, 2025
Daddy, Where Do Software Engineers Come From?
There’s one question every parent dreads hearing from their child: “Daddy, where do babies come from?”

We’ve all heard the responses over the years—storks, God, mommy’s belly, “ask your mother,” you name it. But beneath the awkwardness lies a fundamental question: how do we make more of us?

Oddly enough, the tech world is now grappling with its own version of this question: Where do software engineers come from?

And lately, the answers we’re getting aren’t much better than “the stork.”

The Vanishing Junior Developer

Over the last year or so, we’ve seen a troubling trend take root. Across LinkedIn, Reddit, public layoff announcements and even private chats, junior software developers—bright-eyed, bootcamp-graduated, degree-holding or not—are becoming a bit of an endangered species:
- Offers rescinded.
- Layoffs hitting only the least experienced.
- Job postings asking for “junior engineers” with 5 years of experience and Kubernetes mastery.
- Companies saying they have put a hold on hiring junior developers
Meanwhile, hiring managers say: “We’re only looking for top-tier, senior-level talent who can hit the ground running… preferably with AI superpowers.”

I get it. In today’s hyper-competitive, AI-infused economy, the temptation to lean on experienced developers—and increasingly, AI tools—is strong. Everyone wants 10x output yesterday. But this mindset has a blind spot.

The Software Talent Pipeline Is Drying Up

We all love to talk about pipelines in tech. CI/CD pipelines. Security pipelines. Data pipelines. But there’s another pipeline we rarely talk about: the human pipeline.

If you don’t hire junior devs today, you won’t have senior devs tomorrow.

There is no Hogwarts School of Senior Software Engineering. Developers don’t just emerge from a secret lab fully formed with a GitHub repo of 10,000 commits. They start small. They mess up staging. They crash prod. They learn.

And yes, some of that early-stage work—debugging, boilerplate, test writing—is now within AI’s grasp. But that’s no excuse to stop growing new talent. The danger is clear: we’re optimizing ourselves out of a future.

Are We AI-ing Ourselves Into Mediocrity?

Let’s play this out. If junior roles disappear, then over time, fewer devs learn the fundamentals. That means fewer senior engineers down the line. Will AI replace them all?

Maybe. But do we really want to bet the future of software—and by extension, the economy—on that?

Even AI needs human oversight. Someone has to prompt it well. Review the outputs. Understand the systems it’s building. And as AI gets better, the bar for good human oversight gets higher, not lower.

We risk a future where the few remaining engineers are glorified AI babysitters with just enough understanding to catch hallucinated nonsense—but not enough to innovate, secure, or scale. That’s not a future I want to live in.

So What Do We Do About It?

Glad you asked. Here are 4 steps we can start taking right now to make sure tomorrow’s developers exist—and thrive—in a world where AI plays a bigger role:

1. Mentorship-First Engineering Cultures

Don’t just assign junior devs Jira tickets. Pair them with mentors. Give them structured feedback. Celebrate learning, not just delivery. If your senior engineers don’t have time to mentor, you don’t have a team—you have a support group for burnout.

2. AI-Human Apprenticeships

Why not flip the script? Let junior devs learn by prompting. Have them use GitHub Copilot, CodeWhisperer, or open-source models as learning tools—not replacements. Teach them to critically evaluate AI output. Think of it as co-piloting their way into expertise.

3. Hire for Potential, Not Perfection

Companies love to say “we hire smart people and train them”—until it comes to engineering. Create entry-level pathways. Host internal bootcamps. Offer returnships. Today’s “junior” can be tomorrow’s technical leader if you give them the chance.

4. Establish an Industry Pledge

Let’s take a page from the DEI playbook and build a Tech Talent Sustainability Pledge. Imagine a coalition of DevOps orgs, vendors, and communities committing to hire and develop a minimum percentage of junior engineers. Call it ESG for talent.

Don’t Let the Stork Handle Your Hiring

At the end of the day, we have a choice.

We can tell ourselves comforting myths—that senior devs will always be around, that AI will save us, that someone else is training the next generation. Or we can act like grown-ups and take responsibility for nurturing the future.

Because if we keep cutting the bottom out of the software talent ladder, don’t be surprised when no one’s left to climb it.

Final Thought

Software is the backbone of modern civilization. But without the human minds to build, challenge, and evolve it—even with all the AI in the world—we’re just one stork story away from collapse.
July 28, 2025
AI‑First DevOps is Becoming the Default
If there’s one transformational trend reshaping DevOps today, it’s this: AI‑First DevOps has moved well past the tipping point. What was once hype is now becoming the operational baseline. DevOps teams are no longer experimenting with piecemeal AI features — they are deploying tools enhanced with AI as a standard part of daily work.

AI’s Quiet Revolution in DevOps Tooling

In observability, deployment pipelines, testing, security scanning — AI has already embedded itself. Platforms like Dynatrace leverage proprietary AI (“Davis”) for anomaly detection, root‑cause analysis, predictive analytics and even auto‑remediation across cloud‑native environments. It discovers microservices, traces app performance and helps teams fix issues before customers notice.

Meanwhile, industry commentary and toolchain reports consistently cite AI‑powered anomaly detection, intelligent alerting, predictive scaling and code analysis as core productivity boosters in modern DevOps toolchains (DEV Community, about.gitlab.com, Spacelift, Monterail, SmartDev). These features reduce noise, speed mean time to detect and respond (MTTD/MTTR), and shift teams from reactive firefighting to proactive optimization.

I’ve been writing for years about how DevOps would evolve into something far more intelligent than just CI/CD pipelines. And today, predictive analytics and AI‑based incident resolution are not future promises — they’re operational realities across high‑velocity ops teams.

DevOps Meets MLOps: The Merge is in Full Swing

As DevOps becomes AI‑first, the next logical evolution is the convergence with MLOps. Recent market developments show JFrog, long known for package management and DevSecOps tools, has launched JFrog ML — a unified platform that brings DevOps, DevSecOps and MLOps together under one umbrella (SmartDev, TechRadar, JFrog). JFrog ML enables teams to treat ML models as first‑class artifacts — versioned, secured, bundled and released the same way software binaries are — streamlining collaboration between data science, development and operations teams (TechRadar).

That same integration philosophy is echoed across the industry; as one TechRadar piece recently noted, siloed DevOps and MLOps workflows are one of the main reasons 85% of ML models fail to make it into production. The answer: Treat ML models like code artifacts in a unified software supply chain (TechRadar).

The data bears this out. A mixed‑method study of ML DevOps adoption — surveying over 150 professionals — finds that organizations embracing this convergence achieve higher deployment frequency, lower error rates, improved scalability, stronger compliance and reduced costs (arXiv).

Automated Testing: AI’s Biggest Early Impact

If there’s one area where AI has made an immediate and undeniable impact in DevOps, it’s automated testing. Tools like CloudBees Smart Tests — born from CloudBees’ acquisition of Launchable — use AI to prioritize and optimize test suites, dramatically reducing test times and catching regression patterns before code hits production. Backed with awards like Newsweek’s AI Impact Award for “Best Outcomes in Engineering,” Smart Tests illustrate how model‑driven testing is no longer niche — it’s becoming a default capability (Wikipedia).

This is where speed meets quality. As I wrote previously in “From software factories to intelligence engines,” traditional “software factories”—pipelines that just push binaries — are giving way to intelligence engines. AI is now optimizing when and what to test, even dynamically adjusting based on code and historical risk.

Yes, We’re Early — but Acceleration Is Coming

Let me be clear: We’re still early in this AI‑DevOps evolution. Many organizations have only scratched the surface with AI‑enhanced observability or test planning. Full-blown AI‑driven code generation, pipeline orchestration, LLM‑driven configuration (see research like LADs, which uses LLM chains to optimize cloud infrastructure), or autonomous rollout systems are still emerging in labs and pilot programs (arXiv).

But here’s the thing: innovation and adoption are accelerating. We’re transitioning from manual pipelines to pipelines enhanced with AI; soon, those pipelines themselves will be shaped and orchestrated by AI and ML. Building software will become more automated, almost commoditized. The competitive edge will come from an organization’s intelligence engine — how well it leverages AI/ML to continuously adapt, deliver and iterate.

What an AI‑First DevOps Future Looks Like
- Software and models treated as unified artifacts. Everything — code, containers, ML models, configuration — is versioned, scanned, signed, and promoted together.
- AI‑driven test orchestration. Smart Test selection, predictive test failure detection and even auto‑remediation suggestions.
- Intelligent pipelines. Systems that tune themselves based on performance feedback, deployment success rates, canary results and drift metrics.
- Observability that understands AI. Platforms like Dynatrace track GPU latency, inference drift, feature distribution shifts, connected to infrastructure events.
- Governance baked into AI/ML delivery. Policies-as-code extended to include ML artifacts, with audit trails and traceability across data, features, models and code.
In Closing

We are crossing a threshold. AI‑First DevOps is not a buzzword — it’s becoming the default. DevOps teams today use anomaly detection, predictive analytics, AI‑powered test optimization and increasingly integrated ML pipelines. JFrog ML’s emergence is a clear signal that the industry expects a unified DevOps‑plus‑MLOps future.

Yet even as we accelerate, we must remember that this is still an early era. The true revolution lies ahead: Where DevOps becomes intelligence engineering. Where building and deploying software is table stakes, and the real value lies in how fast and smart your organization learns and adapts.

As I’ve written before: The shift from software factories to intelligence engines isn’t just evolutionary — it’s transformative. Organizations that embrace AI‑First DevOps — and build out intelligence engines that deliver adaptability, reliability, governance and speed — will define the next decade.

That’s where we’re headed. And in that world, AI‑First isn’t optional — it’s the new norm.
July 28, 2025
An AI-enabled Supergraph: From text-to-query to conversational agents

Join Ben Newman, Principal Software Engineer at Apollo as he explores how generative AI and GraphQL APIs are better together.

January 14, 2025
4 Characteristics of a Resilient API Strategy

One of the most significant challenges Platform Architects face is balancing the need to maintain stable systems while preparing for the future. Learn four key characteristics of what makes a resilient API strategy, the difference between environmental and ecological resilience in APIs, and why Architects at over 30% of the Fortune 500 use a distributed composition layer as an integral part of their API strategy to improve collaboration, increase velocity, and complement existing services.

January 13, 2025
CIOs Guide to GenAI

Now is the time to fortify your AI ambition and build real solutions for everyday problems to drive efficiency and competitive differentiation.

This guide, sponsored by Andela, delves into Generative AI’s diverse applications, necessary resources, and the strategic considerations that CIOs must weigh for successful implementation.

Whether you’re piloting projects, optimizing workflows, or envisioning a transformative roadmap, this will guide your organization towards a future where innovation and ROI walk hand-in-hand.

Download now!

January 8, 2025
How Platform Engineering Principles Can Elevate Developer Skills

In the face of growing complexities in software development, technology leaders are striving to understand how they can simplify complex workflows to empower their developers. The answer lies in platform engineering — a revolutionary approach that builds more intuitive workflows and team structures that streamline development processes.

This whitepaper, sponsored by Andela, explores the foundational principles of platform engineering, its impact on software development efficiency, and the importance of Team Topologies, a framework for structuring teams to enhance productivity, adaptability, and innovation in complex organizations. Discover actionable insights and strategies to rethink your development team’s structure, foster a supportive environment, and position your organization at the forefront of software development efficiency.

Download Now!

January 8, 2025
Adaptive Hiring – The Digital Transformation of Talent

Did you know that 74% of tech leaders struggle to find the right talent in today’s market? The way we’ve always hired isn’t working anymore – it’s time for a new approach.

In a world where agility and innovation are key, your hiring practices must evolve.

This latest whitepaper, sponsored by Andela, reveals new strategies to attract, engage, and retain top talent in a fiercely competitive landscape – via Adaptive Hiring.

Whether you’re dealing with the rapid pace of technological change or the growing demand for specialized skills, this resource provides the insights you need to transform your hiring approach.
If you’re ready to overcome your talent challenges and future-proof your workforce, this is the guide you’ve been waiting for.

Download now!

January 8, 2025