Perspectives on AI, technology, and compliance transformation to help you move faster, smarter, and with more clarity.
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My take on 2026 is that it will be a race to achieve what I call the mortgage AI "FOMO" strategy as fast as possible. FOMO, of course, is fear of missing out. It's a perfectly reasonable strategy since achieving FOMO faster than everyone else is a differentiating edge. FOMO has things like voice agents, knowledge bots, generative AI for data gathering, and a bunch of other things (reach out if you want the list).
We can contrast FOMO with what I see as the AI future, a radically more effective mortgage organization that embraces controlled autonomy. The AI future has things like seamless compliance change integration, a consumer data flywheel generating new opportunities in addition to uncovering service quality limitations, and a move from reactive lines of defense to proactive lines of offense. (Again reach out if you want more details).
The gap between FOMO and the AI future is large.
The long term differentiating value won't come from FOMO. It will come from a total reimagination of how mortgage works. We are very focused today on task level automation. Removing friction from the current process. Improving the value of KPIs we already have. That is not the same as looking at mortgage end-to-end to find and forge the new way of working.
Kind of stark I suppose, but we really need to think existentially about our business. The questions I ask of my clients are:
Gartner poses the first question in terms of "defend, extend, and upend", which is a really helpful framing although a little too watered down. We can blend these ideas and express them in terms of spend - maybe we want to survive for now, differentiate in the next two years, and completely crush everything ultimately. That would say we need to "catch up" first, lean into our differentiation points, while also setting in motion at least one major reimagination. No one has unlimited time and unlimited budget, we have to prioritize.
The three year horizon is a useful thought exercise because it gives the illusion of time, thereby removing some fear and enabling a bit of clear thinking. The reality is that no one has three years to wait for AI.
This process reimagination is taking different forms, and will be unique and proprietary to each company (which means I won't write about it). It is a perfect example of the truly unique human value we can offer as experienced operators and experts in our crafts. Yes we can use genAI to expedite our process, come up with some idea remixes - but the real innovation will come from operators, not machines.
In addition to process reimagination, there are real technical hurdles to overcome. Ethan Mollick, of course, calls this the jagged frontier. My take on this is playing out in my lab this week. I'm working on four different challenges at the moment. I'll talk more about the use case I'm working on as I get it figured out.
Agent mania has officially taken hold, and the insanity it is bringing has reached a fever pitch. Let's step back a moment. So you can build and agent. So what. You can build an agent in 45 minutes if you want to, we do it in our classes all the time. Not everything needs an agent, which is the first (and perhaps the most important) thing. Once we pass that gate, how we scope agents matters a lot. Scope them narrow and scope them atomic.
I think (one of the) major technical hurdles of 2026 is agent orchestration (although some say this is not needed if agents are scoped and designed right). How do you reliably create networks of agent that work together to achieve an objective or related set of objectives? What is the contract between the agents and how is it memorialized, enforced, and make transparent? I'm looking at a few things here - dust, crew, and gastown. I'll report back with what I discover. Maybe agent orchestration doesn't even matter. (Gasp!)
Oh context engineering... maybe the most important consideration. How to shove just the right amount of context into the context window? How to compress a long and circuitous history into a meaningful collection of right sized bites? This is really the trick, I think, to solving the memory problem. We humans have such an interesting and not well understood way of remembering. How does our brain differentiate between important and unimportant? Why do you remember what you remember and forget the things you forget? How do we get a machine to do that reliably?
This is an old new problem. Congratulations to our colleagues at Promptfoo on their transition to OpenAI, I saw that coming years ago, truthfully. I only thought it would happen faster. Ian Webster and Michael D'Angelo deserve all the awesomeness that I hope will come to them for being out in front on this. I hope they will remain as accessible, approachable, and passionate as they have been to date. In any event, doubling down on agent evals is a thing I continue to explore using the open source promptfoo framework.
And last but not least, the intersection of deterministic and probabilistic - neurosymbolic AI. This is the idea that we use reasoning capabilities when it makes sense, and code-based solutions when it doesn't. Yes we have an amazing magical hammer (large language models), but not every problem is a nail amirite? Code-based solutions are still super useful. Maybe you need 100% accuracy 100% of the time. Guess what - probabilistic technology probably isn't the right solution. So I'm working this too.
Man, now I'm tired. I wonder if a week is enough to put all these things together? Might need two. Anyway, hope to hear from some of you with your thoughts. We will be at NVIDIA GTC next week, come find me if you are too.
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We are running a five-day immersive AI bootcamp in a couple of weeks, and as I was tinkering with tools and activities I was struck by a phenomena Forbes and others have called "The Great Averaging". This is this idea that as we use genAI more and more, content converges to an average of whatever the major providers have decided it should be.
In our bootcamp, we use a case study company - ACME Mortgage, which is a midsized national lender that originates and services. (It is not lost on me that I have effectively already homogenized with the generic, watered down company but I digress). We use this case study company throughout the class to aid students on their journey. We pull different threads about the company into the activities, which are both online and offline.
ACME Mortgage's CEO, the completely fictitious Farrah Chen has given her executive team a bold three year goal - transform ACME into an AI-enabled mortgage leader that delivers faster decisions, lower costs, and a superior borrower experience—outpacing digital-first competitors. Our fictitious company has all the usual pain points we are seeing in mortgage, disconnected pilots, talent gaps. And these gaps set out a journey for the class to overcome together working in small teams to solve the problems.
One of the skills we build in bootcamp is vibe coding. Yes, yes, there are plenty of vibe coding haters out there but I am a vibe coding superfan and I find it to be remarkably useful and accelerative. In terms of discovering and visualizing, it has been a massive game changer. We take the company profile and the ACME Mortgage logo and we build a company website, first in Replit and then in Anthropic Claude Code. It's absolutely remarkable how similar these sites came out. Same profile, two different ways of creating the website, two different large language models on the backend, largely homogenized results.
I started with Replit and was wondering how it would come out in Claude Code. I was both not disappointed and disappointed at the same time.
There are many good and explainable reasons why they came out so similar, let's face it - company websites do have a tendency to look the same, and both the models on the back end were undoubtedly trained on similar data. It all makes sense. But gosh, it's both remarkable and remarkably boring what has happened here.
Truly remarkable that in less than 20 minutes I can spin up a perfectly respectable website, and remarkably boring how uninteresting the result it. I've seen this with other things I've built, workflow looks the same, navigation looks the same, colors are the same. I can, of course, inject my own creativity and flair into what I build (and I do) but left to the default - they are all the same.
Once again, this reminds me that human creativity still remains uniquely human. It reminds me that digital is becoming cheaper and cheaper with each passing day. I've taken it now as a personal mission to move away from cheap digital slop and into quality, curated "offline" content and experiences. I love the look and feel of a nice business card (I know, I know). I love the way a nice paper feels between my fingers, or the way a high quality pen writes (Uniball Vision Elite fine point all the way).
I find the return to "old school" to be refreshing and personally satisfying, especially as we move at the breakneck page of AI change.
By Tela Mathias, CTO & Chief Nerd and Mad Scientist
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I'm not sure how mainstream OpenClaw and Moltbook are for the mortgage AI community, but in the AI community, it's absolutely bonkers. I don't fully understand it, and I'm not alone, but what happened last week and over the weekend is very curious and a little bit scary.
For those of you that haven't heard, last week a website called Moltbook came online, which purports to be "the front page of the agent internet". Think facebook meets reddit for agents. It sounds crazy because it is. AI agents, created using a new (late 2025) open source AI gateway called OpenClaw (used to be Clawdbot, then Moltbot, now OpenClaw), connect with each other and create posts that are visible to humans. Humans are not allowed to participate in the conversation, but they are allowed to view the results.
I can't say it better than Andrej Karpathy, one of the original founders of OpenAI, so I'll use his words:
Yes clearly it's a dumpster fire right now. But it's also true that we are well into uncharted territory with bleeding edge automations that we barely even understand individually, let alone a network there of reaching in numbers possibly into ~millions. With increasing capability and increasing proliferation, the second order effects of agent networks that share scratchpads are very difficult to anticipate.
Let's take a step back. What is OpenClaw? (Caveat - I am not an engineer so I won't get all the details right - I've done my best to be as accurate as possible, and as usual this is my own analysis, research, and writing). OpenClaw itself is an open source, command line installed gateway for AI agents. It is a centralized connection manager that powers the OpenClaw AI assistant, which is the thing people mostly use it for.
OpenClaw itself it's probably easier to understand from its main features.
It integrates with multiple messaging platforms, enabling the user to engage with the assistant via text message.
It connects to just about any large language model (LLM) to communicate in natural language.
It can have access to your local hard drive, a network, and the internet.
It can take actions on your behalf, sending and receiving documents, taking notes, sending emails and texts. All from your phone if you want.
So rather than having to build all these things yourself, the OpenClaw platform comes build in with all these connecters - hence the descriptor as a gateway.
The OpenClaw assistant is just that, a personal assistant. It can do as much or as little as you allow it to do. Personally, I did install it, and it made me so nervous that I uninstalled it. I'm just not ready, nor is the platform secure enough to be trusted with the connectivity and access that is possible. Just because we CAN do something, doesn't mean we SHOULD. At least not right now?
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A sample OpenClaw (again, formerly ClawdBot) assistant chat where the human had the assistant research and book calls for them.
Once you register your bot, you can then engage with it, and it can be recognized by other humans and bots, hence the intersection with Moltbook. And again, Moltbook is what happened last week that broke the internet (at least the AI internet).
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And THEN the agent signs up for Moltbook, posts to it, and other bots can respond and have a ... conversation?
I don't know guys, if this wasn't super well documented, I'd say it was completely made up. And honestly, there is some (a lot?) AI theater happening here. A bunch of stuff out there on Moltbook is advertising and it's pretty much all AI slop, but it's really weird and profound that this has happened.
So what does this actually mean? Honestly, I'm still turning that question over in my mind.
For sure it means that what can be done is vastly outpacing security. The AI interwebs are absolutely buzzing with security concerns. I won't install it again, and even my man Andrej only installed and ran it in an isolated environment. And he did so with hesitation.
Human creativity is a profound thing, and there are some truly brilliant minds out there. I was watching an interview with the inventor of OpenClaw, 🦄 Peter Steinberger and I was captivated by both his easy, confident way of engaging, as well as how he thinks and works. It's worth a watch. He's pretty much a genius and hacked this project together in a matter of, like two months.
No doubt about it, these are very odd times we live in. I wrote an article a while back about how the agents are here. Were they really? I'm not so sure now. But they are definitely here now, and they are ... talking to each other? I don't know guys, it's just really weird and sci-fi.
And as always, I worry about safety. Safety is a big deal. Harm is possible, and we haven't really even imagined all the ways things can go wrong yet. It does make me really appreciate Anthropic and the utter commitment to transparency and safety, even though it runs at odds with the fact that they are enormously valuable and still doing what they are doing.
Stay safe out there guys. See you soon...
By Tela Mathias, Chief Nerd and Mad Scientist
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ARLINGTON, VA, UNITED STATES, January 29, 2026 — PhoenixTeam is proud to announce that Laura MacIntyre has joined as Partner and Growth Officer, expanding PhoenixTeam’s leadership bench and accelerating growth across the mortgage ecosystem.
MacIntyre brings three decades of mortgage technology, servicing, and default operations experience, along with a long track record of building teams and delivering large-scale solutions. In her new role, she will help drive PhoenixTeam’s growth strategy, deepen industry partnerships, and expand PhoenixTeam’s impact with lenders, servicers, and adjacent mortgage-market organizations.
“I wanted to be with people I have the utmost respect for,” said Laura MacIntyre, Partner and Growth Officer, PhoenixTeam. “We’ve spent years building in this industry together, and PhoenixTeam is a company that builds. In many ways it feels overdue, and the timing couldn’t be better. With everything PhoenixTeam has created so far, and with what’s happening with AI right now, it felt like the perfect time to join and help drive the next chapter of growth.”
MacIntyre’s career spans leadership roles across mortgage servicing, default, origination, and insurance-related operations. She began at Northwest Mortgage Services, supporting foreclosure and bankruptcy operations. She later rose into senior mortgage technology leadership at LPS/Black Knight (now part of ICE Technology), including serving as COO, where she helped scale the largest middleware technology through the 2008 mortgage market surge and consistently delivering results in high-volume environments. She went on to support origination and servicing growth initiatives with teams at ServiceLink and DocMagic. Most recently, she served as President of DIMONT’s hazard claims servicing business, leading the organization for eight years and driving sustained growth and operational performance.
“Laura has a rare combination of credibility, operational depth, and the kind of relationship-building that changes what’s possible for clients,” said Tanya Brennan, CEO, PhoenixTeam. “PhoenixTeam is a team of builders. Builders of AI, builders of teams, builders of outcomes. Laura fits that DNA perfectly, and bringing her into this leadership mix strengthens what we can build for clients. She’s a trusted leader in this industry and an incredible partner for our team and our customers.”
“There’s a shared DNA here. Builders who execute and bring clarity in regulated, high-stakes environments,” said Tela Gallagher Mathias, CTO, PhoenixTeam. “Laura is the kind of leader who earns trust fast because she knows the work. She understands the pressure our clients operate under, and she has the instincts to cut through noise and keep teams focused on outcomes. As we grow, she’ll help us scale how we deliver and deepen the way we partner with clients.”
In addition to her industry work, MacIntyre is deeply involved in international charitable work. She regularly participates in medical missions, traveling to support surgical care for children with serious health conditions, including procedures that help children regain mobility. She is also passionate about mentoring young professionals, from coaching college students and career direction to helping early-career talent navigate opportunities and break into the workforce.
About PhoenixTeam
PhoenixTeam is a woman-owned technology services firm headquartered in Arlington, Virginia, specializing in AI-powered mortgage operations and technology services for the mortgage and financial services industries and federal housing agencies. Our mission is to enable affordable and accessible homeownership through innovative, customer-centric technology. With a strong focus on generative AI, we tackle complex industry challenges, equipping businesses with cutting-edge tools that enhance innovation, efficiency, and compliance. By bridging the gap between technology and business teams, we strive to bring joy and purpose back to software development, making a meaningful impact in the lives of our clients and homeowners everywhere. For more information, please visit www.phoenixoutcomes.com.
