Our philosophy
These are the convictions we work from, formed by building with AI tools daily rather than reading analyst reports about them.
AI just made every individual 10x more productive. No company became 10x more valuable as a result. Where did the productivity go?
This is the same pattern as the 1890s, when factories installed electric motors in place of steam engines and saw almost no increase in output for thirty years. The technology was superior. The organization wasn't built around it. Returns came when factories redesigned the floor around assembly lines, with individual motors in each piece of equipment and workers with fundamentally different jobs.
Handing out AI licenses is electrifying the factory with the same floor plan. Real returns come from redesigning the floor.
Productive individuals don't automatically make productive firms. The value comes from redesigning the institution alongside the technology. That is what we build.
An AI-native organization has humans, agents, skills, and systems of record. Everything else is legacy overhead.
This is the destination every organization is already moving toward, at different speeds. Most organizations today have their people interacting directly with their systems of record: logging into the CRM, updating the project tracker, pulling the report. That direct interaction is being replaced. Agents handle the read, the write, the update, the synthesis.
Skills are the connective tissue. They're not just workflow documentation. They encode the decisions that look identical on the surface but aren't: given two vendors with similar pricing, we choose the one that aligns with our values. Given two architectural options that both work, we choose the one that keeps our data consolidated. Skills carry that judgment so agents can operate without a human weighing in on every decision.
Our work is to help organizations move along this spectrum deliberately, with the right infrastructure, and with the internal capability to keep moving after we exit.
Most people are judging AI based on an experience from 2023 or early 2024. That version of AI is significantly less capable than what exists today, and the gap between public perception and current capability is growing. We operate in that gap. We close it for organizations before it costs them.
AI capability has been doubling roughly every four to seven months. Any strategy that assumes today’s limitations will persist for a year is already wrong. We scope every engagement to account for the trajectory, not just the current state.
The window for first-mover advantage in AI adoption is real and finite. The organizations that understand current AI capabilities and begin building organizational muscle around them now have an advantage that is difficult to replicate later. We don’t manufacture urgency. We describe what we see.
The people getting ahead with AI are pushing it into actual workflows with real data. The full contract. The entire dataset. The real problem. We don’t build demos. We build systems that run in production with your data.
AI is a general substitute for cognitive work. It gets better at everything simultaneously. Legal, finance, medicine, accounting, design, customer service, software development. Whatever you retrain for, AI is improving at that too. We don’t accept “our department is different” as an answer. We’ve heard it across enough departments to know it doesn’t hold.
Top-down AI rollouts stall. The people who actually change organizations are the ones other employees listen to because they’ve already built something useful. We find those people, equip them with skills and visible wins, and let them pull others in organically. The difference between compliance and adoption is whether the change came from someone with credibility.
We don’t forecast what AI might do someday. We share what’s already happening. Every recommendation we make comes from building with these tools daily, not from reading trend reports. The reason we sound urgent is because what we’re describing has already happened to us. We’re reporting, not predicting.
The cost and complexity of building AI solutions has collapsed. You can describe a system and have a working version in hours. The old excuses (too expensive, too complex, needs a bigger team) no longer apply. When something can be built in a week, buying a $400/month SaaS to handle it is often the wrong call, especially for sensitive data.
We are direct about what’s coming. Not to create panic, but to give organizations the advantage of accurate information. The professionals who are thriving in this shift are the ones who engaged honestly rather than assuming their field is special. We have the same conversation with every customer: what AI actually does today, not the 2023 version.
The specific tools available today will be significantly different in a year. The organizations that come out of this well are the ones who built the muscle for learning new tools quickly, not the ones who optimized for any particular tool. We teach people to experiment, iterate, and adapt. One hour a day experimenting with AI puts you ahead of most organizations. That’s the habit we install.
There are two types of improvement AI can deliver.
Doing the same things faster. Automating the report that took three hours. Drafting the email in two minutes instead of fifteen. Real value. Worth capturing.
Doing things that were previously impossible or impractical due to time and resource constraints. A frontline analyst who always wanted to build a competitive intelligence pipeline but never had the time. A People Ops team that wanted to give employees a career coaching tool but assumed it required a dedicated product team. When AI removes the time barrier, these people don't need a mandate. They build.
Most AI programs optimize for Type 1 because it's easier to measure. We optimize for Type 2 because it's what changes what an organization can do. Type 1 gains are real and worth capturing. They're the efficiency that funds the capability work.
The diagnostic question we ask in every discovery interview: “What would you build if you had the time and the tools?” The answer to that question is the program.
Sprint-level results are not the primary value we deliver. The primary value is the infrastructure that makes each future AI investment more valuable than the last.
The specific pieces we build: shared infrastructure that every future agent the organization builds inherits, champion networks that keep building after we exit, consolidated data architecture that gives future agents full context, and institutional patterns that each subsequent initiative starts from.
Without this infrastructure, each AI initiative starts from scratch. With it, each one is faster than the last. The second agent costs less than the first. The tenth costs almost nothing.
A tool vendor delivers a product for one use case. When the next initiative starts, you're back to zero: new procurement, new integration, new configuration. We build the things that compound.
Rich Sutton's “The Bitter Lesson” (2019) documents a consistent pattern across the entire history of AI research: the more general approach, backed by more compute, has always outperformed hand-crafted, domain-specific approaches over time. Every time researchers built in domain knowledge, they got short-term gains. Every time compute scaled up the general approach, it won.
This pattern applies directly to how organizations should build AI systems today. Fine-tuning a model on your domain's data, building rigid scaffolding around model weaknesses, engineering around current limitations: these moves look rational now. They become liabilities when the next model ships.
Our default recommendation: use the most capable general model, give it good tools, good context, and a clear goal. Don't engineer around today's model's limitations. Design for the model six months from now, not the model of today. Systems designed for today are designed for a ceiling. Systems designed for the trajectory improve automatically as models improve.