Tried changing a flight through the @Delta app and online. Shows flights available, but when you click to change it gives you an error message. Eventually speak to customer service who then give you a different (much higher) price then you can see online. Why is this so hard?
Enterprise software is on the verge of another transformation.
First came custom software: highly differentiated, built to fit. But expensive to maintain and hard to scale.
Then SaaS took over: lower maintenance, continuous updates, greater efficiency. But also rigid cores, underused features, and roadmaps driven by vendors. We gained efficiency, but lost differentiation.
Now we’re entering a third phase: AI-native services.
AI fundamentally changes the economics of maintenance. When intelligent agents can generate, test, deploy, and continuously evolve code at a fraction of historical cost, the old trade-off breaks down.
For the first time, companies can combine true customization with subscription economics. Software designed around your workflows and data, yet continuously maintained and improved at scale. This also changes what we mean by “services.”
Services were never just manual work. They were human-scale work: revenue scaled with headcount, margins scaled with utilization. AI changes the lever.
The next generation of services is human-led and AI-accelerated, architecturally repeatable, continuously evolving, and outcome-oriented rather than hour-based.
At Globant, this shows up in AI Pods: AI-powered service units that design and continuously evolve living enterprise systems. Living software delivered as a service.
There is no SaaS apocalypse. Standard platforms will remain massive businesses and critical pieces of enterprise infrastructure. But for many use cases, a smarter alternative now exists.
Custom software gave us differentiation. SaaS gave us efficiency. AI-native services bring both together.
The services industry isn’t going away. It’s being upgraded.
people always ask why i care so much about culture… why i read about it, write about it, post about it.
it’s very simple: it’s the only layer that actually matters.
everything else… tech, markets, relationships, politics is just emergent behavior stacked on top. they’re lagging indicators of cultural mood.
you can’t build, invest, lead, or even live coherently if you don’t feel what people want, fear, love, imitate, & aspire to right now.
if you’re a ceo, vp, or vc, your only real job is reading people, zeitgeist, & timing. everything else is paperwork.
Salesforce is about to pass 1M AI agent conversations on our own https://t.co/BrfR5x8nFJ — working side-by-side with our 1M human agent conversations in Salesforce Service Cloud. AI Agents + Humans = Customer Success. ❤️🤖🤝
#Dreamforce#AI
https://t.co/ngbrccyWzw
In the Fortune 500 average annual turnover from 1995–2011 was about 39 companies, versus 29 per year in 1955–1993, and in the late 2010s–early 2020s it has been on the lower end of that range (often under 5% of the list changing yearly). I suspect starting in early to mid 2030's I suspect it will explode.
When you start to use more AI Agents that do long running work in the background, it becomes clear that software is going to look very different in the future.
Right now the vast majority of software was built to enable people to do all the work. Next, we saw a brief period where software has evolved for AI to be an occasional assistant in helping with that work. You can chat with AI in-line, it provides useful suggestions or data retrieval, and so on.
But, as Agents get more powerful, this paradigm will likely flip. At least for some important chunk of software.
As the AI Agents get more powerful, and you don’t need to go back and forth with them at every point, the software starts to become a tool for managing what AI Agents are doing for you. And software for people managing how agents work will look different from software that enables people to work.
Software becomes about reviewing the AI Agents’ work, creating task queues, seeing the status of work as it’s happening, being able to interrupt the work when necessary, checking audit trails, managing parallel threads, connecting work outputs from multiple agents, and more.
A large portion of software will likely evolve toward supporting this new approach. But a lot of software equally will need to be reinvented along the way. This is going to be both a crazy time for incumbents and a window where lots of new startup opportunities emerge.
Announcing @OpenAI Pioneers, a new program for ambitious companies building with our API.
Selected teams will partner closely with us on domain-specific evals and custom fine-tuned models to advance AI product intelligence in their verticals.
Apply to the first cohort below 👇
Enterprise software wasn’t built for humans. It was built to trap them.
And now we’re trying to duct tape AI on top of it and act surprised when nothing changes.
Every past platform shift was about how humans interacted with computers. Mainframe to desktop. Desktop to mobile. On-prem to cloud. The user was always the same: a person.
This time, the user has changed. We’re building systems for machines, specifically for agents that can reason, decide, and act. That’s not a UI shift. It’s a total reset of how enterprise software should work.
Legacy systems assume a human is always present to move a mouse, fix bad inputs, interpret context, and chase things down. Agents can’t do that. They need infrastructure built for them, structured workflows, constant feedback, observability, and self-evaluation. But the big software vendors aren’t going to lead this shift. They can’t. Their business depends on seat licenses, manual workflows, and keeping humans in the loop. The so-called “system of record” is a relic.
If Excel disappeared tomorrow, half of enterprise software would stop working, and the other half would finally be exposed for the scam it is. Everyone knows this, but no one at the top wants to admit it. The system of record never truly existed.
So what do the incumbents do? They slap some AI on top of their old tools. A chatbot in a dashboard. A sentence autocompleted in a text box. Cosmetic upgrades to avoid rewriting their own playbook. It’s not innovation, it’s fear of losing control.
At Distyl, we’re not trying to retrofit the past. We’re building agent-first systems that can actually operate autonomously, systems that learn, improve, and replace brittle workflows instead of dressing them up.
Most of the industry hasn’t caught up, but a few forward-looking enterprises have. They’re no longer asking if these systems work. They’re scaling them, optimizing them, and rethinking how their organizations operate at the most fundamental level.
Enterprise software is undergoing a complete reset, and it will be a big shift for incumbents.
Fair point, but when you learn modern standard Arabic you are taught to do this as Arabic writing is basically all consonants, with only long vowels. As a beginner you transliterate the Arabic script to English letters to learn vocab and then it basically becomes a habit, but also important when pronunciation of the same letters can change the meaning.
There is an evals crisis; but I don’t think the problem is associated with benchmarking or even running evals. Companies need access to *people* who can develop evals by applying their domain expertise; and need systems to transform user feedback (regressions) into evals.
🎄🎅starting tomorrow at 10 am pacific, we are doing 12 days of openai.
each weekday, we will have a livestream with a launch or demo, some big ones and some stocking stuffers.
we’ve got some great stuff to share, hope you enjoy! merry christmas.