Software That Never Breaks: OutSystems CEO Woodson Martin on Building Enterprise-Grade Apps at AI Speed

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Show Notes

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The following notes are AI-generated, based on the episode transcript. Please listen to the episode for the full conversation.

Woodson Martin became CEO of OutSystems in May 2025. He succeeded founder Paulo Rosado, who had run the company for more than two decades. Woodson arrived after 18 years at Salesforce. Nathan starts with the sociology of a handoff like that in the AI moment: how does a company and an incoming leader decide they share a vision? Woodson's answer centers on Rosado's founding idea from 2001. The goal was to build custom enterprise software fast and reliably, and "evolve software at the pace of the business without breaking anything." He argues the "never breaks" part matters more now that AI agents do most of the building.

How do you keep "never breaks" when output has exploded and vibe-coded software breaks all the time? Woodson's answer is an intermediate layer. Whether a developer uses Claude Code, Codex, or OutSystems' own Mentor, the agent edits an abstract model of what the application should do rather than raw code. Code is then generated deterministically, with security, role-based access control, and reuse of existing compliant components built in. Nathan points out a pattern he keeps seeing: incumbents with mature primitives often find it easier to add an AI layer than AI-native startups find it to rebuild the hard parts. Woodson agrees and adds a second advantage: years of trust with regulated customers are hard for a startup to earn, "regardless of how cool your technology is."

That leads to what "enterprise-ready" means today. Nathan notes that the fastest-growing AI companies don't have great uptime by conventional standards. Woodson says SLAs still matter but are a small part of the picture. He describes customers whose finished agentic systems sit in a compliance backlog waiting for approval of the underlying model, including questions about whether its training data was legally acquired. That is true even for jobs as simple as pulling structured data out of PDFs. Some of that caution is organizational calcification, but much of it is prudent: an enterprise will accept more risk in IT resource allocation than in auditable, contestable decisions like loan origination. Nathan asks whether AI insurance could speed things up and mentions AIUC, which he covered in a previous episode. Woodson finds the idea interesting but says he hasn't seen uptake among OutSystems customers yet.

On cybersecurity, Woodson's frame is speed of fixes. A portfolio built on shared platform primitives can be patched once and fixed everywhere. Hundreds of separate AI-built systems on their own stacks cannot. He is clear that there is "no finish line": legacy COBOL, AS/400, and Lotus Notes systems are still everywhere, and the model companies will profit from both attack and defense.

The most concrete numbers come in the discussion of token spend. After a push to "shift everything to AI," OutSystems went from 4 major features shipped in Q4 of last year to 19 in Q1 and 26 in Q2. Woodson says token spend peaked around June and July and is now below forecast. The savings came from a harness built for OutSystems' own engineering team and an LLM router that sends routine jobs to cheaper models. His broader claim is that most enterprise workloads don't need frontier models, and some don't need a model at all. That is also his case for platforms that make it easy to swap models. On Chinese models, he reports that some customers run them, distilled or post-trained, mostly to save money. He also notes that half of his customers are in Europe, and many companies outside the US aren't eager to trust their data to US companies.

Nathan has used Mentor himself and asks how OutSystems lets relatively unskilled builders make almost anything while keeping it secure. Woodson says OutSystems has worked on exactly that problem for 25 years. His examples: if you bank almost anywhere in Asia, your mobile banking app may well run on OutSystems. So does the fuel-terminal system in Rotterdam that makes sure diesel never flows through a pipe that last carried kerosene. On backlogs, he sees two changes. Long-dreaded legacy modernizations are finally happening, with one customer turning "a six-year thing" into six months. And now that employees build their own dashboards, the new backlog is integration and standardization. Customers such as Axos Bank and YESCO report large productivity gains, though Woodson notes that productivity takes time to show up in P&L results.

Nathan asks how to separate valuable early experimentation from effort the next model generation will make trivial. Woodson says Mentor began as a machine-learning project in 2018 and has become a set of MCP services (see OutSystems' remote MCP plugins) that any coding agent can use. In his view the hard question is still what to build. OutSystems' answer is a new agent that reads telemetry from a customer's existing apps, recommends which processes to make agentic, forecasts the ROI, and can build a first version. On interfaces, he expects to still have more than 100 apps on his phone a year from now, but much software may shrink into widgets that appear inside conversations. He cites a demo from OutSystems' June ONE conference in which a loan application moves from a Claude conversation into a bank's mobile app.

The conversation ends with competition and people. Woodson agrees with Nathan that vendors are converging on the same features: every billboard on the 101 now says "the exact same five words" with a different logo. He thinks the winners will stand out through specialization, both in model tuning and distillation and in deep knowledge of regulated industries. On hiring, he is "super bullish on junior talent," is leaning into forward-deployed engineers, decides for each task whether it belongs to a human, an agent, or a mix, and wants to turn onboarding into just-in-time, agent-delivered learning.

Topics covered

Taking over from a 24-year founding CEO, and why OutSystems' founding vision fits the AI era
Keeping "never breaks" when AI writes the code: model-driven, deterministic code generation
Incumbent primitives vs. AI-native startups, and trust as a moat in regulated industries
What "enterprise-ready" means today: SLAs, compliance, model provenance, PII governance
Prudent risk management vs. organizational calcification; AI insurance and indemnification
Cybersecurity: speed of fixes, shared primitives, and "no finish line"
Token spend, shipping speed (4 → 19 → 26 major features), an internal harness, and an LLM router
Model choice: most enterprise work doesn't need frontier models; Chinese models; regional attitudes
Letting relatively unskilled builders make almost anything while staying secure (Asian banks, the Rotterdam fuel terminal)
Backlogs: legacy modernization and cleanup after self-service building
Productivity vs. P&L, and who captures the value by industry
Mentor's history, MCP services, and an agent that recommends what to build
The future of UI: conversation, widgets, and the Claude-to-bank-app demo
Vendors converging on the same features, and specialization as the way out
Hiring: junior talent, forward-deployed engineers, human-vs-agent work design, agentic onboarding

Resources

OutSystems
Woodson Martin on LinkedIn
OutSystems appoints Woodson Martin as CEO (May 2025)
OutSystems Mentor
OutSystems remote MCP plugins
OutSystems Agent Workbench
OutSystems Agentic Systems Platform announcement (ONE 2026)
OutSystems Q2 2026 product updates
OutSystems World Tour Las Vegas 2026
Salesforce
Claude Code
OpenAI Codex
Model Context Protocol
AIUC (Artificial Intelligence Underwriting Company)
Previous episode: Underwriting Superintelligence with AIUC
Axos Bank
YESCO and the Welcome to Las Vegas sign
Paulo Rosado, OutSystems founder and Chairman (link?)
OutSystems "agent foundry" / what-to-build agent (link?)
Kevin Hearn, Axos Bank software engineering (spelling unverified) (link?)

Quotes worth pulling

"When AI does this stuff for us, the pace at which we build, the sheer volume of stuff we're managing — that never-breaks feature turns out to be even more important." — Woodson Martin

"We shipped four major features in Q4 last year, 19 in Q1, and 26 in Q2 this year." — Woodson Martin

"So I think the reality for most organizations today is that they don't need frontier models for their enterprise workloads." — Woodson Martin

"I don't think we were on a sustainable trajectory in the first half of this year." — Woodson Martin

"The idea that at some point we reach a stasis and everything is perfectly secure, I think is a pipe dream." — Woodson Martin

"What to do is still the hardest question, right?" — Woodson Martin

"If you drive the 101 freeway in San Francisco and you see all the billboards, they all say the exact same five words on them, right — just a different logo." — Woodson Martin

"I'm super bullish on junior talent." — Woodson Martin

Transcript

This transcript is automatically generated; we strive for accuracy, but errors in wording or speaker identification may occur. Please verify key details when needed.

Introduction

[00:00] Hello, and welcome back to the Cognitive Revolution!
Today my guest is Woodson Martin, CEO of OutSystems, the pioneering enterprise application development platform that's helped many of the world's largest companies build and run complex, mission-critical applications since its founding in 2001, and which now specializes in helping those same giants build, orchestrate, and govern agentic systems. Woodson took over for OutSystems' legendary founding CEO Paulo Rosado in 2025, so we start with his thoughts on leadership transitions in the AI era, including how he assessed the company's AI readiness and what it was seeking in a new leader. From there, we go on to discuss what makes a software platform "enterprise-ready" in today's world, with Woodson emphasizing the importance of human trust, established over years, and the critical role that well-designed abstractions play in allowing coding agents to accelerate software development while still delivering on the company's core promise of software that "never breaks." For OutSystems' customers, which include Petrobras, Brazil's state-controlled energy company, Vodafone, one of the world's largest mobile carriers, and Toyota, the world's largest carmaker, among many others, this new paradigm makes it possible to accelerate product roadmaps, to the point that some of them are actually starting to clear what had been ever-growing development backlogs. While Woodson says that most of what their customers are shipping today is traditional, deterministic applications that don't require frontier AI, OutSystems itself has gone from 4 major feature releases in Q4 to 19 in Q1 and 26 in Q2, and is rapidly agentifying its own business, with major initiatives including its customer-facing application development assistant Mentor, in-house AI infrastructure including a custom harness and gateway that have allowed it to bring spend down from a peak in June and July to below projections today, and a new "agent foundry" they're using to suggest new agentic systems and forecast the associated ROI for customers. Toward the end, we get his strategic analysis on the future of the software industry, including how competition will shape up as platforms expand to serve adjacent niches, what companies should do to differentiate themselves as everyone begins to do everything, and why he's "super bullish" on AI-pilled junior talent. From the outside, it's often said that for all the amazing things AI can do, we haven't seen measurable impact on productivity or global GDP growth. But, I think you'll agree that Woodson's perspective, from the helm of a platform that serves many of the world's most consequential and often conservative companies, suggests that change is indeed well underway. With that, I hope you enjoy my conversation with Woodson Martin, CEO of OutSystems.

Main Episode

[03:11] Nathan Labenz: Woodson Martin, CEO of OutSystems. Welcome to the cognitive revolution.

[03:16] Woodson Martin: Thanks, Nathan. Exciting times. Good to be here.

[03:19] Nathan Labenz: Yeah. Boy, never a dull moment in the AI game these days. I wanted to start with kind of a sociological question for you because in doing my homework, I learned that you took over OutSystems from a legendary founding CEO who had led the business for twenty four years. And this just has me thinking, what an interesting time to go through a leadership transition at a software company, to be in your position of taking over a software company, to be a company looking for a new leader. All that stuff is always fraud. But in the AI, you're a... There's this whole other dimension of, like, where is this company on their journey? Where is this leader in terms of whose vision is? Or who gets it? Who doesn't get it? So I'd love to hear the story of the thought process and how you guys got to understand one another well enough to know that it would be a fit going into the AI era.

[04:11] Woodson Martin: Yeah. Great. I'll start with Paolo Rosado, who we're talking about here. Paolo's a badass. You know, he started out systems with a very bold vision back, you know, in 2001, and that vision endures. And it was the idea that, like, custom enterprise software. And people build really complex systems, custom ERPs, terminal management systems, global financial consolidation for, you know, reinsurance companies on OutSystems as a platform. But, you know, those projects at that time, custom software projects, were always years late, way over budget, super unpredictable, and resulted in very rigid software that, when you wanted to change it, it was another whole huge project. And Paolo was like, this is crazy. There's a better way. And his vision was simple. Build faster, reliably every time, and evolve software at the pace of the business without breaking anything. And that's still the core of what OutSystems does today. Of course, now we build all that stuff with AI, agents doing most of the work. Right? But the result is that enterprise AI just works every time, all the time. And so it's kind of been amazing how enduring his initial vision was and the platform fundamentals he built. And kinda that was what got me excited and inspired about OutSystems. Sixteen months ago when I joined was really understanding how powerful these platform primitives that the company had built that let, like, relatively unskilled technical users, developers build really complex enterprise systems, change them fast without breaking stuff. That was revolutionary at the time, but it's needed even more now. Right? When AI can build all this stuff for us, the pace at which we build, the sheer volume of stuff we're managing, like, never breaks feature turns out to be even more important. So that's kind of what got me inspired about OutSystems as a business and as an opportunity, and then it was just meeting customers. Like, the the enthusiasm for the platform, because of what that made possible for so many people and so many companies, like, people just love the product. And you always wanna be in a situation where your customers and your users love the technology. And so that's been a big part of it for me too. And then just seeing that some of the problems the company hadn't yet solved things I knew how to do. You know, I came I came here after eighteen years at Salesforce. I did a lot of jobs in that business, everything from running marketing in Europe to product managing the core sales platform to running HR for a while for the company. And through all that experience, learned a ton of things about scaling businesses. And I was like, I think I know a lot of what we could do here to really turn on the afterburners and grow this thing. And so that's super exciting.

[07:34] Nathan Labenz: It is striking how... As I was listening to you describe the original vision, I was like, that all sounds very consistent with where we are now in the AI times.

[07:44] Woodson Martin: Yeah. But for maybe the never breaks. Right? Because we're

[07:47] Nathan Labenz: all, I would say, at least most of us are living in a world where AI is doing a ton of stuff for us. The amount of output is up tremendously, and I'd be interested in hearing some metrics around what that looks like at OutSystems. But then we also are seeing things like break pretty often. How are you managing to maintain the value of never breaks in the vibe coding era that we're now in?

[08:13] Woodson Martin: Yeah. We take a pretty different approach here to AI for software development than most things on the market today. And that's because there is an intermediate layer in the OutSystems platform, an abstraction of the intent of the application that you build with an AI. You can use Claude code. You can use our built in AI assistant called Mentor. You can build with Codecs. You bring any coding agent you like. And rather than manipulating code in our platform, they manipulate this abstract model of what you need. And then we... You press a button to deterministically... Or you ask Claude to do it. You deterministically generate the code in a way that produces an asset at the end of the day that meets all of your requirements for security, you know, role based access controls. All those things are baked in to the at the platform level so that every code asset created on OutSystems respects a 100% of that. And by the way, automatically reuses things you already did. So I go build with AI. I pick up Claude, and I'm say, hey. Just write me an app. It'll start everything from scratch, basically. And in a complex enterprise where I've already got hardened systems that meet my compliance requirements for GDPR or HIPAA, depending on what kind of business I'm in, I don't want that stuff reinvented all the time, go through another compliance cycle, pick up what works, add the capability I'm looking for on top of it, and push that thing out to production in a way where I've... I know that it all works the first time. And so that's really kind of the secret sauce in OutSystems. When you're building a new system on OutSystems, you're inheriting every piece of your enterprise control plane, if you will, in every code asset that gets generated. And that's really a very different story than almost any kind of other harness people are using today to build AI tech.

[10:25] Nathan Labenz: Yeah. In general, I've seen this pattern a lot where the incumbent that has really well developed primitives is in... Initially, you have the kind of, oh, these startups might come in AI native and win the market, but it seems most of the time that having these really well developed primitives makes it easier to put an AI layer on top of that so you can get the most out of them and reconfigure, remix them as much as you might want.

[10:49] Woodson Martin: Those foundations matter a lot. And it's it's really hard to build those primitives that work at enterprise scale in the way that OutSystems is done. And it's not just that. Like, the software is one thing. The other thing is trust. Like, it takes for... If you're a regulated enterprise operating in Europe somewhere and you have hundreds of regulators looking at your stuff, you've got contractual obligations with all kinds of customers and business partners. You've got privacy concerns everywhere you look. The trust that you have in any system is not just a pure technical evaluation of its underpinnings. Right? It's actually the entire history of the delivery of systems like the one you need on that platform that you know, and there are plenty of of others out there that you can ask about their ability to trust it, what works and doesn't, etcetera. Like, so the reputation that you build up over time for that kind of trust for these kind of hardened systems in regulated industries is a thing that is just very hard to get as a startup regardless of how cool your technology is.

[12:15] Nathan Labenz: Yeah. That... That's really interesting. And it's maybe the answer to another question I was gonna ask you around. What does it mean to be enterprise ready in today's world? I... Of course, everybody's claiming it, incumbents and startups alike. But then what I thought it used to mean was, like, high uptime and reliability and things like that. And now I look out at the world, and I see that the fastest growing companies out there, like your Anthropix and OpenAI's, don't really have a super great uptime, actually, by any conventional standard. Right? So it seems like they have shown that if you have the spice, so to speak, enterprises are actually maybe a little more forgiving of some of these requirements that used to be, like, sacrosanct than it used to seem like. How do you... What's your take on that, and what do you think really is required to be enterprise

[13:06] Woodson Martin: ready today? Yeah. Okay. There's a lot here. And so... And it's not simple. Like, the reality is every enterprise is a little different, and what they need is different. And what they need for different workloads, is not always the same. So sure. In a lot of regulated enterprises, people have AI helpers doing work. There's not a lot of regulated enterprises today who have unleashed autonomous agents to run mission critical work. And that's a maturity thing for the industry part of it. Yeah. Stuff like SRAS, you know, the reliability of the system, and is the model always there and able to respond uptime, etcetera. But it's way more than that in most of these organizations. And I've got customers who are well down, and I think this is true, by the way, of a lot of technology providers, Customers who are well down the path of an agentic system they have built and designed and tested and they're super excited about, and it's sitting in a compliance backlog today somewhere in their organization where it's pending the approval of the use of a specific AI model under the covers for that work. And the obstacles that get in the way are things like, well, we need to understand whether all the data used to train that model was legally acquired by the person or the entity that trained that model. Right? There is a set of requirements that exist in many of these organizations that make it extraordinarily challenging to trust a workload to an autonomous agent. And this... Some of these are just like, all it's doing is reading data out of PDF documents and turning it into structured data. And yet there is this type of compliance concern that slows progress. So I think enterprise ready means a lot of things. Right? Yes. Of course, it means the systems have to work when you need them, but it also means that they need to meet a rather extensive set of requirements that are nonfunctional in... You know, not just, like, does it do the thing when we press the button or say the words, but does it help us meet all of our regulatory requirements, contractual requirements, etcetera? So that's a bit... There's a lot when it comes down to what enterprise ready means in the market today, and a lot of it hasn't changed with AI. Yeah. Some of it has. That whole model provenance question, that's a relatively new one. But a lot of it isn't. Like, how do I know exactly how every bit of PII is being managed in this complex workflow across six systems to get this thing into production? And that's a lot of where the governance comes in, where a lot in the form of the security model comes in. So how do I ensure that the model is executing with access to only the data that's appropriate for that. It's not consuming stuff that it shouldn't. And those are all the kinds of core primitive capabilities you need in a platform to be ready for enterprise AI.

[16:37] Nathan Labenz: When it comes to these apps that are built and in this sort of compliance backlog that you describe, and feel free to pick it apart and subdivide your answer, but does that strike you as just prudent risk management, or is that ultimately a mistake by leadership to not expedite some of these things in the spirit of getting the transformation that I think everybody kind of agrees is inevitable underway now.

[17:07] Woodson Martin: It's certainly some of both. The calcification processes inside organizations is real in large enterprises and is something that organizations need to challenge and break, in order to advance. But a lot of it is also very real. The, in a lot of places, the regulatory constraints are significant. The liability risks are big, and reputation risk is also high. And so a lot of this is prudent decision making. And the reality is organizations are taking different risks in different parts of their business for different kind of workflows. Right? There's one level of risk I'll take for managing, for example, the allocation of IT resources to people who work in the company. Banks need that. Insurance companies need that. Right? Regulated enterprises of all kinds need that kind of stuff. Take more risk there than perhaps the loan origination process where they are making decisions contestable by regulators, and they need to be able to prove, right, what and how the decisions were made at every step of the process because it was all auditable. And so they need to make sure that they have what they need to be able to defend themselves in the case that they ever need to for any decision they're making. So I think it's a mix in terms of appropriate prudence and also just legacy conservatism.

[18:42] Nathan Labenz: What are you seeing in terms of indemnification from the big model companies, does that move the needle for people? And what about the role of insurance on some of these things? I've pre... Previously done an episode with a company called AIUC, the AI underwriting company. They're developing standards and trying to exceed the insurance market to hopefully grease the wheels on this. Are you

[19:05] Woodson Martin: seeing traction there? Entrepreneurs doing work in this domain, you know, basically AI agent insurance type offerings. We haven't actually seen that uptake of that with our customers, with the workloads they're managing on our platform. So I think that is a very interesting idea. I do see that probably could be an unlock. But I think for a lot of these organizations, it's also another unproven thing and also needs some maturation for the kind of regulated industries that often operate on our platform to, like, jump in on that too.

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Main Episode

[22:43] Nathan Labenz: One obviously huge risk that everybody is facing these days is the risk of cyber attack. How are you dealing with that? Obviously, you have a code base that goes back many years. Are you... There's... Many people are talking about, we have to move everything to Rust. We gotta do a a ground up rewrite. We've gotta be memory safe. We gotta have all these things. Otherwise, we're just way too exposed. Where... How how much investment do you plan to make there? We're making a

[23:11] Woodson Martin: lot of investment there, and, obviously, we're thinking about two things. Like, we have our own platform, our own infrastructure, our own business operations where we're thinking about this, but we also have all the thousands of systems that our customers have built on our platform, and we think about risk in that domain. And in both those cases, the first thing we think about is speed remediation. Right? Like, the velocity of cyber threat is increasing, the pace with which everybody needs to be able to respond to that. We need to be able to respond, patch our own systems. We need customers to be able to respond, patch their own... Their systems. And there's two worlds. Let's paint. Let's paint the world where you've got a concentrated portfolio running on a platform where there's a lot of shared primitives. There is just something a lot easier about speed remediation in that context than in the alternative context where you've got hundreds of AI built systems, each running on their own frameworks and stacks and using various open source components and so forth. And you're like, okay. Now there's a vulnerability. How do I even find out where I have it? Much less, how do I remediate. So there's just an inherent benefit in the kind of platform approach to the kind of, hey. Wild West AI builds all the things. So that's one place where we think about the benefit of a platform like ours for speed remediation from from our customers. Well architected systems with a lot of reuse of common components means that any remediation can be tackled kind of once in a spot and have a broad impact anywhere. And so that's one thing that we think about when we think about this. Are we upping our game? Absolutely. Are we adding resources? Are we bringing the world's most powerful AIs in to try and run our own pen tests on our own things? Are we encouraging our customers to do the same thing? Are we automating a lot of that testing in the build process and the test and the deployment process inside our own platforms so that customers don't have to think about that as much? Yes. All of those things are important investments for us as we think about, you know, an increasingly risky cyber environment.

[25:34] Nathan Labenz: Do you think that will be a sort of permanent tax on the software industry that the frontier companies just collect in perpetuity because they're constantly creating new cyber risks that only they can help you solve? Or do you envision the alternative would be like, we close all the bugs, you know, and we're in a happy place in, I don't know, eighteen months or something?

[25:58] Woodson Martin: There is no finish line on security and cyber threat. Right? Like, as technology evolves and gets better at all things, it also gets better at cyber attack, and the world is full of legacy technology today. So the big inter... We have lots of customers out there who still have 60 old COBOL systems running in corner or a s 400, Lotus Notes, like, all of it. And there's so many of these systems. Like, the idea that at some point we reach a status and everything is perfectly, you know, secure, I think is a pipe dream. So, no, I don't think it ever gets to the point where there's nothing. Now are the model companies who are both fueling the tech that can discover vulnerability and penetrate, right, gonna benefit also from the use of those models for protection, right, and and white hat and everything, yes, for sure. Like, obviously, that's driving a lot of token burn today. Probably will continue to. It's hard to see why it wouldn't.

[27:15] Nathan Labenz: Yeah. How is your token spend to the degree that you wanna share the details? How has it evolved over recent months, and how do you think about it? I often ask people, what's your ratio of token spend to payroll as one way of kind of getting a handle on it?

[27:32] Woodson Martin: I'll say, you know, maybe one thing I'll just say is kind of peak for us was kind of June, July. It's been tailing off. And part of that has been, like, learning a lot about... And frankly, focusing on it. Like, last year at this time, our sole focus was like, hey. Shift everything to to AI. Use models for everything. And that created a huge amount... A bunch of learning first. Like, we learned a ton. Second, a huge increase in velocity in terms of just capability delivery. So think software engineering, big part of what we do, shipping software products. We... I think we shipped four major features in q four last year, 19 in q one, and 26 in q two this year. So, like, we have saw a huge surge in our... You know, and this is not minor things. These are major new capabilities that was a dramatic acceleration due to that kind of AI investment. We saw it in the token burn, but worth it for the impact. So that was great. But one of the things that we've learned is actually most of those things that we were doing, we could do just as well, a lot cheaper with a better harness. We built one, right, for our own engineering organization that is, of course, full of all of our own context and is optimized for us. We built a gateway. Right? A route... LLM router. So we're like, not all those jobs need to go to, at the time, whatever, Opus four eight or something and could cut... We could reroute to lower cost models for specific jobs. So we've actually seen... We're actually burning less today than we'd forecast in q three because of optimizations largely that we've been able to do. So I think the reality for most organizations today is that they don't need frontier models for their enterprise workloads. Like, almost none of enterprise workloads. And I think about the real operations of a business as opposed to necessarily the creation of a new software asset. And those workloads can use models that are three years old for most of the stuff. Like, when you look, it's really boring what most of the enterprise workloads where AI can make a real advance happen today, and a lot of that stuff can be done with deterministic code cheaper than it can be with a model at all. Or two, can be done with a lower cost model either because you're using open weight or you're using a... Just an earlier version of one of the foundation models. And the, and that's, I think, a key learning for us. We're seeing that in our own work. We're seeing that certainly with customers. And this is why it's important to build your workflows, right, to build your agentic systems on a platform that makes it super easy for you to swap models to get the best of what's out there. Maybe because capability improves, maybe just because you can do it cheaper with another model. And I think we're gonna see without question that most enterprise workloads are are operating at a much lower per token cost than, you know, what the frontier of the frontier is offering today. And at the frontier, people are cutting prices. Right? Like, I think that's the other thing that's happening here. So there's gonna be a lot of shift. Like, I don't think we were on a sustainable trajectory, in the first half of this year.

[31:32] Woodson Martin: Right? Like, everybody... Every CFO got the token bill in February February and and March, March, and it was, like, obviously, an oh shit moment for the whole world. And so everybody started doing controls, whatever they were, capping token spend on a per person basis, putting in a router that would allow you to route jobs to lower cost things, pulling back entirely from, you know, an AI first for everything everywhere strategy. We saw that in some companies. We saw big announcements about that. I think Microsoft, Meta, Uber, like, all these companies who are like, wow. Maybe went too far too fast. But I think you can you can get back to the point where you're really leveraging AI and everything. You just need to do it in a way that is smart. Right? And that's why you need platforms to help you do that. And that's the kind of job OutSystems is doing for our customers. So let's do a little double click into that. I would be very interested to know, like, how you advise customers in terms of what your model mix is, what you think their model mix should be. I would guess that developers when they're coding are still upgrading to OPUS five five immediately, but maybe not. And then I do understand how if you're trying to convert PDFs to structured data, obviously, you can do that more cheaply. Do you recommend even going as far as fine tuning for some of those use cases to really dial in performance on a cost adjusted basis? And what about Chinese models? How are you seeing people react to just the... I don't personally feel it's all that scary, but it's sometimes the scary prospect of a Chinese intelligence in their business. Yeah. I don't know if I have unique insight on the Chinese model question. I would say that we certainly have some customers who are perfectly happy to go there either by running their own versions of those, distilling them, or post training tuning. And that's happening for sure and usually driven by cost concerns. Although I'll also say that we have a very global business. Half my customers are in Europe. I got tons of customers in Asia. And attitudes around this question are very different in different parts of the world. Right? Not everybody is super excited to trust their stuff to US companies when you go to a lot of places around the world. And so we definitely see a huge diversity in terms of model... Models used in AgenTic systems that customers build on our platform. We also have done a ton of tuning on our own. So you think about so we offer a service today to build your apps on our platform, to build your agentic systems on our platform. We have a service we call Mentor to do that. Mentor offers its own agent experience for you. As a user, you can come on to our site. You can use our IDE, and you can use our Mentor directly there. But you could also operate that through MCP services using any coding agent or harness of your choice. So what we've done, of course, is we've picked and fine tuned a bunch of models on the back end to drive the effective and efficient execution of these builder workloads for our platform. And so when you use a frontier model on top or even an older model on top of those MCP services, you're getting the benefit of a whole bunch of optimization under the covers and a much lighter weight job that your harness needs to or its models need to do to give you the result that you want in our platform. And so there's frankly just a lot of answers here around optimization at various layers of the stack that come into play. And I think one of the things that we do for our customers is we simplify all that. And we're like, we've done the hard work to optimize and to deliver, therefore, the... What you need, rapidly building, maintaining, managing complex systems with AI. And we make that easy and inexpensive, but also trustworthy for you, and we're simplifying that world. So that's a big part of the value proposition of the OutSystems platform.

[35:38]OutSystems: OutSystems is the leading agentic systems platform, empowering enterprises to build, coordinate, and govern AI agents and mission-critical applications securely. Learn more and start building your agentic future at https://outsystems.com/tcr

[36:57]Tasklet: Tasklet empowers your business with AI agents that connect to your tools and automate recurring workflows with no code required. Visit https://tasklet.ai and use code cog rev for $50 in free credits

Main Episode

[38:35] Nathan Labenz: What have you learned about balancing... And I did go use Mentor on the website, so I have a sense for the experience. On the one hand, it seems like the platform is designed to support anything or almost anything. Right? There...

[38:51] Woodson Martin: You have

[38:52] Nathan Labenz: customers that wanna connect to all kinds of data sources, whatever. Right? There's no end to it, I'm sure. But then on the other hand, you also have this security constraint and promise. And it seems like it's a... Would be quite a challenge to allow random users to build anything while still making sure that what they build on the platform is secure to the standard that your ultimate customer at their company expects you to uphold. So how do you strike that balance?

[39:25] Woodson Martin: Yeah. Well, one thing is that's not a new balance for us. That's been the story about systems for twenty five years. So how do you let a relatively unskilled technologist, maybe doesn't have a computer science degree, doesn't really understand software architecture, isn't a database expert, doesn't deeply understand open source libraries, like, whatever. And you say, how do you let that person build mission critical systems that run a bank? So that problem is the problem that we've kind of been solving for twenty five years. And now what we do is we give the same tools that you used to have access to through a visual editor. Right? Hey. Let me drag and drop these things around on screens and talk about... Add a field for what I need. Now, of course, that's all a conversation with an AI. But under the covers, it's using the same primitives to deliver the hardened asset at the end. And it takes a lot. Right? Like, you have to build a sophisticated model under the covers capable of serving almost an infinite variety of use cases. So if you go if you're wandering around if you are a if you have a bank account almost anywhere in Asia, the chances are pretty good that the app you use to manage your balance and pay your bills runs on OutSystems. Right? A ton of front ends, mobile apps, consumer experiences built on this platform. If you are a oil tanker pulling into port in Rotterdam and unloading millions of liters of petroleum into tanks at a terminal to move onto trucks and trains and other boats to move stuff around Europe and fuel the economy, the system that runs all of that is built on OutSystems. Incredibly complex logic for ensuring that when you pump diesel through this pump in the terminal, you're not putting it through a pipe that last had kerosene in it. Right? And your... All the quality control, all the system for managing the process of cleaning and flushing those pipes and making sure that everything works in a way that doesn't spoil literally millions and millions of euros of value of product, Those things all run on OutSystems. So a huge variety of complex back end, sophisticated front end to manage that diversity of capability in a single platform. Yeah. It was a lot of work. That's why it took twenty five years to do it. And that's why we're confident that those fundamentals that we've built serve all those use cases and all those highly regulated environments make it easier for our customers across any industry, honestly, regulated or not, to have the trust in the platform so that they can rapidly iterate at the experience layer, if you will, and build all kinds of new things that just always work and always work together.

[42:43] Nathan Labenz: That's the idea. Things I noticed on the AdSystems website is... Or maybe was an article with you, but somebody said that AdSystems customers have a lit... A limited budget and a very long backlog. Is that changing today? Is the backlog actually getting shorter now that everybody's moving so much faster?

[43:02] Woodson Martin: Yeah. Things are changing. So many things happening in this one. I think probably the most exciting thing happening for me is that projects that everybody was always afraid to put on the backlog are, like, these old legacy systems, the COBOL, the a s 400, the Lotus Notes and stuff. They're finally getting their day because it's now possible with AI operating and accelerating the work at every phase of a very complicated process, by the way, just even understanding what these old legacy systems do, translating those into new requirements that are modernized where we take old laborious work, turn it into a work, and then the production of the new of the new technology, the testing, the rollout, all of those things now can be accelerated dramatically with AI. And so we're seeing customers pick up the modernization, like insurance companies picking up the modernization of archaic sixty year old case management systems and saying, we're finally ready to take on this project. Instead of planning it as a six year thing, we're now gonna do it in six months. And that is, like, a huge change into sort of what's on the backlog. There's another thing happening, which is, of course, like, we used to need somebody's backlog to have our... Like, pretty common thing in most businesses is, hey. I need to do dashboard to track this new thing. And I would ask somebody for the dashboard, and that would go on some backlog. And so when you build it. Right now, you probably... In most organizations, you're like, hey, Claude. Take this spreadsheet. Make me a dashboard and boom. Right? Like, you've got it yourself. So much more things have become self-service. Now one thing a lot of companies are seeing is, okay. With all that self-service, now we're losing standardization. We're losing integration. We're gonna need to do... Go do some cleanup of those things. But the good news is a lot of the experience has been figured out. What do people really need? Well, they built it themselves. They kept talking to Claude until they got it. So now we can say, okay. Now how do I just make sure that I wire the back end of those things together in a way where they use our common data definitions, where they use our common standards for role based access control, where they... And that's where a platform like OutSystems makes a big difference in processing, frankly, a very different kind of backlog than than what it looked like even a few months ago.

[45:38] Nathan Labenz: So how does this translate to the success of companies and economic growth? It's trite at this point to say, kinda like computers in general. We see AI everywhere, but the GDP growth statistics. Right? Do you feel that your customers are approaching a tipping point where they are going to start to see faster growth that will be measurable? And if not, like, why not?

[46:04] Woodson Martin: Yeah. I think it very much is a customer by customer story. So I've got some customers. I think about a guy named Kevin Hearn who runs software engineering for a bank called Axos Bank. They're based in San Diego. They're a entirely digital bank. They have been at this for a long time. They are seeing huge gains in terms of their own productivity, and that has been the starting goal. Right? Like, dramatic acceleration of delivery of new capability for the bank so that they can better compete in a digital landscape for deposits, for customers, on lending. And so I think they're far ahead. I'll let them speak to their own financial results from that, but they've got a pretty exciting story. I think we've seen other examples of companies like... There's a company, YESCO. I don't know if you've been to Las Vegas since the welcome to the Las Vegas sign. They build these and operate these kinds of signs all over the world, and they have dramatically accelerated their productivity in ways that are exciting for them because it means that they can tackle more new business opportunity than they could with existing resources before. I'll have to let you talk to them about their own financial results from that, but they they tell an optimistic story, which is exciting for for us to be able to help fuel that. There are... There is lots of potential. Everybody sees it. It takes a while in an organization for progress in productivity to translate into p and l results. And, you know, that's true in my business. That's true in any business. Everything doesn't happen overnight. But... And I think not all of the investments that people are making are gonna pay off. Right? A lot... There's a lot of experimentation here because a lot of these things are new. But I think everybody's pretty convinced that the potential is high, and we're gonna see the returns as we learn how to do these AI workloads at scale. And I think that's the that's the promise. And now, yeah, our job is to go help deliver it.

[48:23] Nathan Labenz: So as OutSystems does go about delivering it, how much of the sort of savings of increased productivity

[48:32] Woodson Martin: do

[48:32] Nathan Labenz: you think gets passed on to the customer? Like, a lot of... The sort of first thing is, like, we couldn't be more productive. We have higher margin. Obviously, there's competition that, in theory, should discipline high margins. Where do you think we are on that story? And do you think that in the end, post transition, like, margins are the same as they were before and everybody just gets a lot more for their money? Or do you see the structure of the industry changing in terms of who captures what value?

[48:58] Woodson Martin: I think it's gonna be very different depending on industries. I think there are a lot of industries. I think of manufacture... Discrete manufacturing as an example where so much of the costs, in the business are tied up in physical materials where the influence on what the margin profile of the business may be is probably a lot more closely related to tariff policy than it is to AI. That's just one extreme example. I take another example we all love to talk about, which is kind of the consulting industry, right, where I think we basically deal in information. And so in that world, I think there's a lot more where AI can take on more of the work and have a greater impact. And I think it's... Will have an impact both in terms of, like, how much demand there will be for that kind of work and also the margin profile at which that can get delivered. So I do think it's gonna be a very different story by industry, and there's not one answer. I also think there's gonna be a big variation within industries. Some organizations are moving fast, leaning in, trying things. Some are sitting back and saying, we'll watch and wait. And I do think, that will have an impact on, you know, who gets there first, who will gain on margin, but who will gain on share as well, and therefore, you know, who ultimately wins.

[50:25] Nathan Labenz: When I tried Mentor product that you described with the builder experience, I was struck by a couple of things. One was just... And I'm interested in your kind of take on what is the value of very early aggressive experimentation? Because one thing that has been true for me over and over again is I strive, I toil, I sweat to finally make something work. And then the next model generation or certainly two model generations later, it's that suddenly easy. Right? So even building something like Mentor two generations ago, probably pretty tough, right, to get it to work, to get it to be semi reliable, etcetera, etcetera. Now you could put five five or Astra on it and probably build something very similar to what you have in whatever 10% of the time or even less. When you think about, like, strategically what organizations should be doing, how do you separate spinning your wheels and just churning on stuff that will soon be easy from waiting? How do you know what things you really wanna attack early and where maybe a little patience actually could be a virtue?

[51:34] Woodson Martin: Yeah. Okay. Good question. Lots to talk about there. Certainly, like, the build of Mentor has been a big lift. Right? Absolutely. We began this effort in 2018 before I was with the company. And, obviously, the state of techno... It was all machine learning at the time. There was no generative AI. It was back... Question was, how do you use AI or machine learning AI in every phase of the software development life cycle to speed things up and make quality better and and everything? That, of course, the project has evolved a ton over that time to the point where now, you know, Mentor is really a set of MCP services that can be consumed anywhere to build software reliably that that always meets all of your nonfunctional requirements and helps you stick with your compliance goals and all those things. So, like, it's been a huge transformation. The models we've used under the covers change all the time as we optimize for cost as we talked about earlier. The problem that most of our customers still face today, and honestly, this is not a dramatic shift from previously, but capability is better. People can do more faster. But the real question is, like, what should I build to actually solve problems in my business to take advantage of opportunities in my business? Like, what to do is still the hard... Hardest question. Right? So how much should I play around and experiment and try? I'd say a lot of that effort should probably be going into where can I get the biggest bang for my buck? Where do I... Where... What business process does it make sense for me to transform into something agentic? We've we've learned the hard way on this with our existing customers. You know, we've got portfolios of thousands of applications today. So what we did is we built an AI that says, tell the customers what agentic system they should create over top of the assets that they already run on our platform. And we've just launched a brand new capability. It's currently in the hands of our customer success teams, which is an agent that simply tells you what built... To build and forecasts for you the ROI in terms of time savings and all this. We use all the telemetry data from the apps that you've built. We understand the structure of what you're doing. We see the data flow through those. We reason over that, and we say, hey. You might wanna think about an agent here for taking this manual job and turning it in an automated one. And by the way, press this button to build it and let the AI actually go build a v one of that that you can then run through, of course, your development, testing, validation process, and and try it out. And so, like, solving the problem of what to do is, like, a huge part of the challenge for most organizations trying to get there in terms of an agentic transformation of their business.

[54:32] Nathan Labenz: Very interesting. The other big question I had around mentor is, what do you see as the future of the user experience in software? The mentor experience is the now increasingly familiar AI sidebar that kind of wraps the legacy application. Right? What you used to do through drag and drop, it can now do through tool use, and now you have both. But then you also have this, like, MCP paradigm where I sit in some other software entirely, or maybe I even take a walk and just use voice mode to talk to my agent about it that way. And the the tools are never perhaps even visualized in the way that they are on the core platform.

[55:14] Woodson Martin: What are you anticipating in terms of skating where the puck's gonna be? What... Where are you trying to get ahead of those trends? Yeah. Great question. I I personally still imagine that a year from now, I'm gonna have more than a 100 apps on my phone. Right? Like, I do think that the user experiences for different things that we want to... Don't all lend themselves into conversational experience like we think principally about whether we're doing it voice mode or text or whatever. Now I I don't know how it evolves. I do think there's a real significant chance that a lot of software as we know it today is just widgetized into little things that show up in the flow of conversation. Right? So, like, I think about the Google Maps question. Google Maps is one of the most useful things on my phone. I use it every single day, whether it's to look up a place to order a pizza or it's to navigate me to, you know, a destination where I'm driving or walking around a foreign city that I don't know well. So I'm like, okay. When does Google Maps just become part of my conversation with an AI? Okay. Could happen immediately. Like, right, I could just say, chattypety. Give me turn by turn instructions on how to get from here to there. But I still wanna know, like, what the hell does the world look like around me? How is it shaped? What am I gonna pass on the way? And I don't think the conversational experience is gonna lend itself perfectly there. Microcosm of, you know, the whole challenge you're putting forward here, but an example of where I think things will still be different. I think for a variety of reasons that could be oriented around privacy, that could be oriented around compliance, that could be oriented just purely around user preference and experience that we're gonna still see a wide variety of software interaction paradigm in the future. Now I'm open to the idea that the big monolithic chunks of that that we have today, a portal I go to with 80 features, might be delivered very differently in little chunks just when I need them in the flow of other work. And, you know, that's gonna be interesting to watch and see you develop. I think that we we... At our user conference in June, we showed a pretty compelling demonstration of one of our banking customers initiating loan applications through a consumer conversation with Claude about a home remodel. Hey. Let's think about this new stove and all these things. And, Jesus, this is gonna be expensive. I might need some money. Do you know you're preapproved from a low... For a loan for 30 k? Would you like to initiate that application? And a seamless flow from the conversation in Claude straight through to the mobile app of the bank with the same agent operating behind the scenes in both contexts, seamlessly carrying the conversation through, recognizing which typically required assets you need for a loan application process that they already have because you're already a customer of the bank. They don't need to ask you for your bank statement. And straight through processing that kind of application through the back end where there's agents doing the work or making recommendations to humans in the loop at every phase of that process and streamlining the entire experience for a consumer. That's interesting. Right? There's... You know, a lot of the workload moves directly into the ex... The agent experience, if you will. But there's still some software assets there, in this case, in the form of a mobile application that need to be there for a series of compliance and and privacy and regulatory requirements. Will those change and evolve over time so that's not needed? I don't know. We'll see. I think the interesting question for me is kind of not how are all the things gonna become the same, but what will the imagination now of creators produce now that AI and platforms like OutSystems remove so many of the typical traditional constraints of building amazing experiences.

[59:33] Nathan Labenz: That notion of everything becoming the same and also your comments earlier around how many major features you guys have been shipping recently and how much that's grown has me wondering about the future of competition. I do feel like everybody is newly emboldened to go chase these adjacencies. And the more adjacencies they chase, then the more new adjacencies become available to chase. And so I do see this convergence of, like, all the platforms growing into the same super horizontal everything you need in one place kind of offering. And I wonder if you see that too and if that is where you think it's going. Oh, yeah. How do we win? How does one win in that environment?

[1:00:16] Woodson Martin: Yeah. Yeah. I mean, of course. Let's be clear that if you go... If we drive the 101 Freeway in San Francisco and you see all the billboards and they all say the exact same five words on them. Right? Like, it's just a different logo. And for sure, I think the industry is struggling to crystallize differentiated messaging in this world. And you know why? Look. Part of it is the sea of same. Right? Like, so many experiences across so many dimensions today can be delivered through a conversational experience, and that's kind of one factor. It's what's behind that conversation that now is the question. What are the systems back there? What is the data back there? How do those things interact back there to give me more of what I want through such an experience? And, you know, you're like, pretty quickly, you come to, well, okay. Great. I need an agent agent platform. I need an a... I need an AI builder. I need integrations to lots of things. Right? Like, stuff it takes to deliver on that question is kind of some basic building blocks. Right? And at the moment, a lot of platforms are rushing into that space and saying, okay. Well, let's fill it up with all those things. So how will we differentiate in the future? I think it's going to be really... Ultimately, it's going to be specialization. Right? There's going be a common set of building blocks everybody needs. And then there's what do you get really good at? What do you optimize for? And part of that optimization will be in domains like tuning and distillation and model specificity and associated cost structures that optimize for that. I think part of that will be, you know, in industry and workflow specific optimizations. I think part... And there's gonna be concentration around that gets you out of just pure horizontal. Everybody has the same stuff because everybody has the same primitives because you need primitives, but you gotta have specialization on top. So for us, a lot of that is in regulated industries where we've solved a ton of those problems in ways that we can bring to bear to this platform. It's like this 37 features that are not on that basic list that you need to actually get through the hurdle of building or deploying the first thing. And so I think that's gonna be a big part of it in our domain. And then we've got specialization that we've built over years in banking and insurance and in government and health care, transportation logistics, energy and utilities, which are all big regulated industries, all of which are the core of our installed base today, where our teams, have a lot of specialized skill and understanding and where we can rapidly help organizations modernize processes. And so I think it's gonna be that kind of specialization, how we differentiate. At the moment, we're in the thing... We're in the point where everybody's saying, yeah. We got all the stuff. And I think we're all rapidly learning that that's just confusing everybody. Everybody's like, oh, everything's the same. And so I do think we have work to do as an industry there to get our get our game on.

[1:03:24] Nathan Labenz: How have your hiring practices changed, and what advice would you give to people who are just entering the workforce these days who I think have got a lot of signals recently that they're nervous about their own employment prospects. They're somewhat resentful toward AI because they feel like it's competing with them. What are you doing at OutSystems, and how would you translate that to broader advice?

[1:03:47] Woodson Martin: Yeah. I'm super bullish on junior talent, on people who grew up whose last five years have basically been native to all these new technologies where they can bring those into organizations, our customers, our partners, our own organization, and really drive acceleration... Accelerated innovation. They obviously need to learn a lot about the core business processes in all these industries to be of value, but I think that they bring generally a fresh perspective, an attitude, an AI first, AI pilled, if you will, kind of approach to things. And so I'm bullish there. I don't think the organizations today have built the infrastructure to do the other part yet well, which is really indoctrinate these people into the industries and the specialized knowledge that it takes to really... To put those skills to use as effectively as possible. You know, what are we... What's changing about the way we're hiring? We're definitely leaning into the FTE model, you know, thinking about how do we help customers more directly in in in imagining the work. Right? A big part of what it is, like designing what is the... Where are we gonna make the investments to have significant impact? And we think about that as the people we hire. We also think about the agents we hire. And so the work I was talking about today about our agent foundry that goes in and inspects your architecture and says, here is an op... Here are opportunities to turn into AgenTic is another thing that we're doing. So we're thinking very differently today about every new piece of work that needs to happen. And is that work for a human? Is that work for an agent or a team that's a blend of those? And how do we structure everything around this new model for delivering work. And then we're thinking about the journey of every employee that we hire and how we dramatically accelerate what we've typically called onboarding, which, you know, for decades in most businesses was like, hey. Here's the new hire thing you do for a couple days or a week, and here's the follow-up trainings you do on all these things. And shifting all of that into something that is more gentic, more real time, more just in time, give you the knowledge you need at the point when you can actually then use it and therefore internalize it. And so a lot of the practices around people are evolving fast. And those are investments that we're making in our own right, and we're certainly encouraging customers to think through in their businesses and how it fits there too.

[1:06:18] Nathan Labenz: Anything else you would wanna leave people with before I let you get back to work?

[1:06:22] Woodson Martin: Yeah. Well, tune in today to the OutSystems world tour from Las Vegas where we will be showcasing a lot of these amazing technologies, the latest in the agent experience for OutSystems, which dramatically helps organizations to accelerate trusted delivery of AI systems for their business.

[1:06:44] Nathan Labenz: Witson Martin, thank you for being part of the Cognitive Revolution.

[1:06:48] Woodson Martin: Cheers, Nathan.

Outro

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