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Illustration of cool blue water drop calmly patting a fiery drop that looks angry

I conceived of this blog in 2023 as a skeptic’s outpost, demystifying AI hype and taking it down whenever necessary. I had no interest in fueling a speculative bubble, but as a technologist at heart, I’m always interested in seeing what’s coming down the road. This is my way of saying that this post is not about poking holes in the current hype cycle, but rather taking a look at what I see developing in the application development world. Because I see major changes afoot, and I’m not sure folks are ready for it.

No, we’re not all losing our jobs

This is one of those zombie lies that won’t die. Turns out, sprinkling AI sauce into your IT and build environments makes humans in the loop even more important than before. Humans who understand how to build things; humans who understand how to communicate; humans who know how to write effectively; and humans who can conceive of a bigger picture and break it down into lanes of delivery. In other words, the big winners in an AI world are full-stack humans (I stole this from Michelle Yi. I hope she doesn’t mind) – a career in tech has never been more accessible to a humanities degree holder than now.

The big losers are the code monkeys who crank out indecipherable code and respond in monosyllabic grunts to anyone who deigns to ask – those “10x” engineers that everyone was lauding just 3 years ago. We already knew source code wasn’t that valuable, and now it’s even less valuable than ever.

AI and DevOps

I had thought, until recently, that as AI infiltrated more and more application development, that the day would come when developers would need to integrate their model development into standard tools and platforms commonplace in our devops environments. Eventually, all AI development would succumb to the established platforms and tools that we’ve grown to know and love that make up our build, test, release, and monitor application lifecycle. I assumed there would be a great convergence. I still believe that, but I think I had the direction wrong. AI isn’t getting shoehorned into DevOps, it’s DevOps that is being shoehorned into AI. The tools we use today for infrastructure as code, continuous integration, testing, and releasing are not going to suddenly gain relevance in the AI developers world. A new class of AI-native tools are going to grow and obliterate (mostly) the tools that came before. These tools will both use trained models to be better at the build-test-release application development lifecycle as well as deploy apps that use models and agents as central features. It will be models all the way down, from the applications that are developed to the infrastructure that will be used to deploy, monitor, and improve it.

Ask yourself a question: why do I need a human to write Terraform modules? They’re just rule sets with logic that defines guardrails for how infrastructure gets deployed and in what sequence. But let’s take that one step further: if I train my models and agents to interact with my deployment environments directly – K8s, EC2, et al – why do I need Terraform at all? Training a model to interact directly with the deployment environments will give it the means to master any number of rulesets for deployments. Same thing with CI tools. Training models to manage the build and release processes can proceed without the need for CI platforms. The model orchestrators will be the CI. A Langchain-based product is a lot better at this than Circle CI or Jenkins. The eye-opener for me has been the rise of standards like MCP, A2A, and the like. Now that we are actively defining the interfaces between models, agents, and each other, it’s a short hop, skip, and a jump to AI-native composite apps that fill our clouds and data centers, combined with AI-native composite platforms that build, monitor, and tweak the infrastructure that hosts them.

AI Native Tools are Coming

Once you fully understand the potential power of model-based development and agent-based delivery and fulfillment, you begin to realize just how much of the IT and devops world is about to be flipped on its head. Model management platforms, model orchestrators, and the like become a lot more prominent in this world, and the winners in the new infrastructure arms race will be those tools that take the most advantage of these feature sets. Moreover, when you consider the general lifespan of platforms and the longevity of the current set of tools most prevalent in today’s infrastructure, you get the impression that the time for the next shift has begun. Hardly any of today’s most popular tools were in use prior to 2010.

DevOps tools have followed a general pattern over the past 30 years, starting with the beginning of what I’ll call the “web era”:

Timeline showing the progress of machine automation in the web era, starting from 1995 until today. The diagram shows "custom scripts on bare metal" lasting from 1995 - 2010, "Automated IaC, CI 'cloud native'" from 2010 - 2025, and "MCP, agentic automation 'AI Native'" starting in 2025 going until ????
Progression of automation in the web era

The current crop of automation tools are now becoming “legacy” and their survival now rests on how well they acclimate to an AI Native application development world. Even what we call “MLOps” was mostly running the AI playbook in a CI “cloud native” DevOps world. Either MLOps platforms adapt and move to an AI native context, or they will be relegated to legacy status (my prediction). I don’t think we yet know what AI native tools will look like in 5 years, but if the speed of MCP adoption is any indicator, I think the transition will happen much more quickly than we anticipated. This is perhaps due to the possibility that well-designed agentic systems can be used to help architect these new AI Native systems.

I also don’t think this will be any great glorious era of amazing transformation. Any migration to new systems will bring about a host of new challenges and obstacles, especially in the security space. I shudder to think of all the new threat vectors that are emerging as we speak to take advantage of these automated interfaces to core infrastructure. But that’s ok, we’ll design agentic security systems that will work 24/7 to thwart these threats! What could possibly go wrong??? And then there are all the other problems that have already been discussed by the founders of DAIR: bias, surveillance, deep fakes, the proliferation of misinformation, et al. We cannot count on AI Native systems to design inclusive human interfaces or prevent malicious ones. In fact, without proper human governance, AI native systems will accelerate and maximize these problems.

In part 2, I’ll examine the impact of AI Native on development ecosystems, open source, and our (already poor) systems of governance for technology.

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Woman in head covering inserting a daisy into the shield of a riot police officer. She stands between a line of riot police and protestors

I have been struggling recently with where to direct my focus and what I could write about that would add something material to the ongoing debates on “AI”, technology, and politics. Thanks to my friend Randy Bias for this post that inspired me to follow up:

Randy Bias' LinkedIn Post with the following text: "I notice that a lot of the open source world gets uncomfortable when I start talking about how geopolitics is now creating challenges for open source. I don't understand this. It's provably true. Even things at the margins, like the Llama 4 release, which is technically not 'open' has a restriction against EU usage.We *must* talk about the geopolitical realities and look for solutions rather than letting us be driven by realtime political trends. The open source world is uniquely positioned to operate across borders, but if we can't talk about the geopolitical challenges right in front of us it's just a "hear no evil, see no evil" type mentality. 🙈 Open source world, stop trying to shut me and others like me down simply because the topic is unpleasant."
Randy Bias’ LinkedIn post about open source and geopolitics

This post triggered a few thoughts I’ve been having on the subject. Namely, that open source was born at a time that coincided with the apex of neoliberal thought, corresponding with free trade, borderless communication and collaboration, and other naive ideologies stemming from the old adage “information wants to be free”. Open source, along with its immediate forbear free software, carried with it a techno-libertarian streak that proliferated throughout the movement. Within the open source umbrella, there was a wide array of diverse factions: the original free software political movement, libertarian entrepreneurs and investors, anarcho-capitalists, political liberals and progressives, and a hodgepodge of many others who came around to see the value of faster collaboration enabled by the internet. There was significant overlap amongst the factions, and the coalition held while each shared mutual goals.

From 1998, when the term “open source” was coined, until the early 2010’s, this coalition held strong, accomplishing much with robust collaboration between large tech companies, startup entrepreneurs, investors, independent developers, general purpose computer owners, and non-profit software foundations. This was the time when organizations like the Linux Foundation, the Apache Software Foundation, and the Eclipse Foundation, found their footing and began organizing increasingly larger swaths of the industry around open source communities. The coalition started to fray in the early 2010s for a number of reasons, including the rise of cloud computing and smart phones, and the overall decline of free trade as a guiding principle shared by most mainstream political factions.

Open source grew in importance along with the world wide web, which was the other grand manifestation of the apex of neoliberal thought and the free trade era. These co-evolving movements, open source and the advocacy for the world wide web, were fueled by the belief, now debunked, that giving groups of people unfettered access to each other would result in a more educated public, greater understanding between groups, and a decline in conflicts and perhaps even war. The nation state, some thought, was starting to outlive its purpose and would soon slide into the dustbin of history. (side note: you have not lived until an open source community member unironically labels you a “statist”)

For a long time, open source participants happily continued down the path of borderless collaboration, falsely believing that the political earthquake that started in the mid-2010s woud somehow leave them untouched. This naivety ignored several simultaneous trends that spelled the end of an era: Russian influence peddling; brexit; the election of Trump; Chinese censorship, surveillance and state-sponsored hacking; and a global resurgence of illiberal, authoritarian governments. But even if one could ignore all of those geopolitical trends and movements, the technology industry alone should have signaled the end of an era. The proliferation of cryptocurrency, the growth of “AI”, and the use of open source tools to build data exploitation schemes should have been obvious clues that the geopolitical world was crashing our party. This blithe ignorance came to a screeching halt when a Microsoft employee discovered that state-sponsored hackers had infiltrated an open source project, XZ utils, installing a targeted backdoor 3 years after assumgin the ownership of a project.

One cannot overstate the impact of this event. For the first time, we had to actively monitor the threats from nation states wanting to exploit our open source communities to achieve geopolitical goals. The reactions were varied. After some time, the Linux Foundation finally admitted that it could no longer ignore the origins of its contributors, demoting the status of some Russian contributors. At the other end of the spectrum is Amanda Brock, who prefers to stay ensconced in her neoliberal bubble, unperturbed by the realities of our modern political landscape.

Screenshot from ComputerWeekly article with the text: "Amanda Brock, CEO of OpenUK, described the decision to remove Russian developers from patching the Linux kernel as “alarming”. In a LinkedIn post, she said: “At its heart, open source allows anyone to participate for any purpose. But as we have seen adoption of open source at scale in recent years, to the point where over 90% of the active codebases used by companies have dependencies on open source software, it’s understandable that concerns about risk have been raised by governments.”
Screenshot of ComputerWeekly article quoting Amanda Brock.

One thing must be clear by now: we find ourselves knee-deep in a global conflict with fascist regimes who are united in their attempts to undermine free republics and democracies. As we speak, these regimes are looking to use open source communities and projects to accomplish their aims. They’ve done it with blockchains and cryptocurrencies. They’ve done it with malware. They’ve done it with the erosion of privacy and the unholy alliance of surveillance capitalism and state-sponsored surveillance. And they’re continuing to do it with the growth of the TESCREAL movement and the implementation of bias and bigotry through the mass adoption of AI tools. This is part and parcel of a plan to upend free thought and subjugate millions of people through the implementation of a techno oligarchy. I don’t doubt the utility of many of these tools – I myself use some of them. But I also cannot ignore how these data sets and tools have become beachheads for the world’s worst people. When Meta, Google, Microsoft or other large tech companies announce their support of fascism and simultaneously release new AI models that don’t disclose their data sets or data origins, we cannot know for sure what biases have been embedded. The only way we could know for sure is if we could inspect the raw data sources themselves, as well as the training scripts that were run on those data sets. The fact that we don’t have that information for any of these popular AI models means that we find ourselves vulnerable to the aims of global conglomerates and the governments they are working in tandem with. This is not where we want to be.

From where I stand, the way forward is clear: we must demand complete transparency of all data sources we use. We must demand complete transparency in how the models were trained on this data. To that end, I have been disappointed by almost every organization responsible for governing open source and AI ecosystems, from the Linux Foundation to the Open Source Initiative. None of them seem to truly understand the moment we are in, and none of them seem to be prepared for the consequences of inaction. While I do applaud the Linux Foundation’s application of scrutiny to core committers to its projects, they do seem to have missed the boat on the global fascist movement that threatens our very existence.

We have to demand that the organizations that represent us do better. We must demand that they recognize and meet the moment, because so far they have not.

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I’ve written a number of articles over the years about open source software supply chains, and some of the issues confronting open source sustainability. The ultimate thrust of my supply chain advocacy culminated in this article imploring users to take control of their supply chains. I naively thought that by bringing attention to supply chain issues, more companies would step up to maintain the parts that were important to them. When I first started brining attention to this matter, it was November 2014, when I keynoted for the first time at a Linux Foundation event. Over the next 3 years, I continued to evolve my view of supply chains, settling on this view of supply chain “funnels”:

Diagram of a typical open source supply chain funnel, where upstream comments are pulled into a distribution, packaged for widespread consumption and finally made into a product
Diagram of open source supply chian funnel

So, what has happened since I last published this work? On the plus side, lots of people are talking about open source supply chains! On the downside, no one is drawing the obvious conclusion: we need companies to step up on the maintenance of said software. In truth, this has always been the missing link. Unfortunately, what has happened instead is that we now have a number of security vendors generating lots of reports that show thousands of red lights flashing “danger! danger!” to instill fear in any CISO that open source software is going to be their undoing at any given moment. Instead of creating solutions to the supply chain problem, vendors have instead stepped in to scare the living daylights out of those assigned the thankless task of protecting their IT enterprises.

Securing Open Source Supply Chains: Hopeless?

Originally, Linux distributions signed on for the role of open source maintainers, but the world has evolved towards systems that embrace language ecosystems with their ever-changing world of libraries, runtimes, and frameworks. Providing secure, reliable distributions that also tracked and incorporated the changes of overlaid language-specific package management proved to be a challenge that distribution vendors have yet to adequately meet. The uneasy solution has been for distribution vendors to provide the platform, and then everyone re-invents (poorly) different parts of the wheel for package management overlays specific to different languages. In short, it’s a mess without an obvious solution. It’s especially frustrating because the only way to solve the issue in the current environment would be for a single vendor to take over the commercial open source world and enforce by fiat a single package management system. But that’s frankly way too much power to entrust to a single organization. The organizations designed to provide neutral venues for open source communities, foundations, have also not stepped in to solve the core issues of sustainability or the lack of package management standardization. There have been some efforts that are noteworthy and have made a positive impact, but not the extent that is needed. Everyone is still wondering why certain critical components are not adequately maintained and funded, and everyone is still trying to undertand how to make language-specific package ecosystems more resilient and able to withstand attacks from bad-faith users and developers. (note: sometimes the call *is* coming from inside the house)

But is the supply chain situation hopeless? Not at all. Despite the inability to solve the larger problems, the fact is that every milestone of progress brings us a step closer to more secure ecosystems and supply chains. Steps taken by multiple languages to institute MFA requirements for package maintainers, to use but one example, result in substantial positive impacts. These simple, relatively low-cost actions cover the basics that have long been missing in the mission to secure supply chains. But that brings us to a fundamental issue yet to be addressed: whose job is it to make supply chains more secure and resilient?

I Am Not Your Open Source Supply Chain

One of the better essays on this subject was written by Thomas Depierre titled “I Am Not a Supplier“. While the title is a bit cheeky and “clickbait-y” (I mean, you are a supplier, whether you like it or not) he does make a very pertinent – and often overlooked – point: developers who decide to release code have absolutely no relationship with commercial users or technology vendors, especially if they offer no commercial support of that software. As Depierre notes, the software is provided “as is” with no warranty.

Which brings us back to the fundamental question: if not the maintainers, whose responsibility is open source supply chains?

The 10% Rule

I would propose the following solution: If you depend on open source software, you have an obligation to contribute to its sustainability. That means if you sell any product that uses open source software, and if your enterprise depends on the use of open source software, then you have signed on to maintain that software. This is the missing link. If you use, you’re responsible. In all, I would suggest replacing 10% of your engineering spend with upstream open source maintenance, and I’ll show how it won’t break the budget. There are a number of ways to do this in a sustainable way that leads to higher productivity and better software:

  • Hire a maintainer for software you depend on – this is a brute force method, but it would be valuable for a particularly critical piece of software
  • Fund projects dedicated to open source sustainability. There are a number of them, many run out of larger software foundations, eg. The Linux Foundation, the ASF, Eclipse, the Python Software Foundation, and others.
  • Pay technology vendors who responsibly contribute to upstream projects. If your vendors don’t seem to support the upstream sources for their software, you may want to rethink your procurement strategies
  • Add a sustainability clause to your Software Bills of Materials (SBOM) requirements. Similar to the bullet above, if you start requiring your vendors to disclose their SBOMs, add a requirement that they contribute to the sustainability of the projects they build into their products.

There is, of course, still a need to coordinate and maximize the impact. Every critical piece of software infrastructure should be accounted for on a sustainability metric. Ideally, software foundations will step up as the coordinators, and I see some progress through the Alpha and Omega project. It doesn’t quite reach the scale needed, but it is a step in the right direction.

If you work for a company that uses a lot of open source software (and chances are that you do) you may want to start asking questions about whether your employers are doing their part. If you do the job well of sustaining open source software and hardening your supply chains, you can spend a lot less on “security” software and services that generate reports that show thousands of problems. By coordinating with communities and ecosystems at large, you can help solve the problem at the source and stop paying ambulance chasers that capitalize on the fear. That’s why spending 10% of your IT budget on open source sustainability will be budget neutral for the first 2 years and deliver cost savings beyond that. Additionally, your developers will learn how to maintain open source software and collaborate upstream, yielding qualitative benefits in the form of greater technology innovation.

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Cory Doctorow published an excellent essay in Locus about the AI bubble and what will happen when (not if) it goes “bloop” as bubbles are wont to do. Namely, the money in the AI ecosystem is only sustainable if it allows programs to replace people, and due to the prevalence of high risk applications, that seems highly unlikely. I think he’s absolutely right – read that first.

Ok, done? Cool…

Reading Cory’s essay jogged my memory about some experiences I’ve had over my tech career. The first thought that came to mind was, haven’t we been through this before? Yes, we have. Several times. And each time we learn the same lesson the hard way: paradigm shifting tech transformations do not, in fact, result in large reductions of workers. Sometimes there may be displacement and reallocation, but never reductions. No, large reductions happen when businesses decide it’s time to trim across the board or exit certain businesses altogether.

One particular moment from my career came to mind. I was a product manager at large storage vendor. We had a assembled a small group of large company CTOs and were telling them about our latest roadmap for storage management automation. We had launched an automation product 3 years prior, and we wanted to assure them that we were committed to continuing our investment (spoiler alert: we were not, in fact, committed to that). So we went through the song and dance about all the great new things we were bringing to the product suite, about how it would solve problems and help our customers be more productive.

I’ll never forget one exchange with a particular CTO that is forever seared into my memory. He began by carefully choosing his words, mindful of their impact, but he finally said what was really on his mind, and likely for the rest of the group as well: “Will this let me fire some guys?” I was unprepared for this question. We had just spent the last 2 hours talking about increased productivity and efficiency from automation, so he drew what seemed to him to be a very logical conclusion from that. That is, if the product is as efficient and productive as we claimed, then surely he would be able to reduce staff. We hemmed and hawed and finally admitted that, no, we could not guarantee that it would, in his words, let him “fire some guys.” It was as if the air completely left the room. Whatever we said after that didn’t really matter, because it wouldn’t be the magic bullet that let everyone fire a bunch of staff.

This is a lesson that we keep learning and unlearning, over and over again. Remember cloud? Remember how that spelled the end of sysadmins and half of IT staff? Yeah, they’re still here, but their job titles have changed. Just because you moved things to the cloud doesn’t mean you can be hands off – you still need people to manage things. Remember Uber? None of these gazillion dollar swallowing enterprises or sub-industries of tech have generated anywhere near the original perceived value. And don’t even get me started on crypto, which never had any actual value. Cory’s point is the same: do you really think hospitals are going to fire their radiologists and put all patient screening and lab results in the hands of a machine learning (ahem: advanced pattern recognition) bot? Of course not. And so, a hospital administrator will ask, what’s the point? Do you really believe that hospitals are going to add tens or even hundreds of thousands of dollars to their annual budget to have both bots AND people? Don’t be absurd. They’ll be happy to make use of some free database provided by bots, but the humans in the loop will remain. Cory’s other example was self-driving cars. Do you think taxi or other transportation companies are going to pay both drivers (remote or otherwise) and bots for transit services? Be serious. And yet, that’s the only logical outcome, because there is no universe where humans will be taken out of this very high risk loop.

The problem is that this is no justification for the billions of dollars being invested in this space. End user companies will happily make use of free tools, keep their humans, and spend as little as possible on tech. That part will not change. So who, then, is going to justify the scope of current investments? No one. That’s why it’s a bubble. Cory’s right. The only thing that remains to be seen is who gets harmed in the aftermath and how badly.

The intended buyers of this technology are going to ask the same question as that CTO from years ago: will it let me fire some guys? The answer is no. It is always no.

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Those of us who have been around the block in the high tech space can point to a number of moments where the hype went way beyond the actual value. The worst example of this was probably crypto and NFTs, which are slot machines built on a casino where the house definitely has the upper hand. The world of AI is the successor to crypto, with one very important difference: the tools that have been lumped under “AI” are actually useful, or potentially useful. But that is also part of the problem: because there are some well-known use cases, there’s a tendency to exaggerate the usefulness of the technology. There’s also a tendency to exaggerate the possibilities of the technology to the point of delusion.

Let’s start with the first problem: the term itself, “Artificial Intelligence”. It is neither “artificial” nor “intelligent”. What it actually is is advanced pattern recognition and language automation. For that insight, I credit Dr. Emily M. Bender, professor of linguistics and computational linguistics at the University of Washington. Labeling language automation tools as “AI” brings about the worst comparisons to dystopian sci-fi, but it also is, frankly, just wrong. No large language model is remotely sentient. None of the language automation tools are paving the way to Artificial General Intelligence (AGI) – the type of technology that “wakes up” and… makes us breakfast? provides tips on the betterment of humanity? decides humans have had their day and builds skynet? All of these scenarios are a bit silly, and the hype beasts concern trolling over implausible outcomes has become most wearisome.

While we were distracted by the dystopia vs utopia non-debate, real harms have been perpetrated against real humans with these tools. And with the increasing compute power behind these language models, the degree of potential harm grows with each passing day. Real harms in the form of disinformation, bias, devaluing of creative works, and a growing inability to retract or prevent any of these harms. Add to that the growing body of research that shows LLMs are vulnerable to data poisoning and reverse engineering of its training data and it’s clear that we haven’t quite thought out the ramifications of relying on these tools.

I’ll wrap up this blog post by (hopefully) stating the obvious: LLMs are obviously here to stay and can already do a number of useful things. I know I look forward to having an LLM fulfill my more mundane, rote tasks. But it’s crucial that we don’t anthropomorphize LLMs and ascribe to them characteristics that are definitely not there, however much we might wish them to be. It’s equally important not to buy into the dystopian doomerism about rogue AI, which is its own form of egregious hype. The more we worry about implausible hypotheticals, the more we risk missing the danger that’s here today. Humans were already good at institutionalizing bias and spreading misinformation. Now, with LLMs, we can do it faster and at a much larger scale. Buckle up!

My guiding lights on this topic are the amazing people of the DAIR Institute, led by founder Dr. Timnit Gebru. Other influences are Kim Crayton and the aforementioned Dr. Bender. Read them today – don’t believe the hype.