Our AI Philosophy: Why We Haven't Built AI Into Basa...yet (But Use It Everywhere Else)

After seven months focused entirely on researching AI's impact on content creation, I came to the conclusion that AI will create orders of magnitude more content, which means orders of magnitude more deals. That research led directly to founding Basa. But understanding AI's transformative power so deeply is exactly why we don't build AI into our customer-facing platform today.

So What (Basa POV)

Companies that understand AI's second-order effects will capture more market advantage than those chasing AI features. We build infrastructure for human negotiations at AI-driven volume because the teams preparing for coordination at scale will win over those trying to automate away the humans who actually make deals happen.

Why Basa Doesn't Have AI Today: These Processes Aren't Automatable Yet

Ever negotiated with a teenage creator? Had a client change deliverables mid-negotiation? Worked with an agent representing multiple creators for the same campaign? If so, you'll understand why AI only works when you have repeatable systems - and there's no such thing in deal-making today.

In our world, 99% accuracy isn't good enough. It has to work 100% of the time because every failed negotiation represents lost revenue, damaged relationships, and missed opportunities. When you're coordinating deals worth hundreds of thousands of dollars with creators whose careers depend on getting terms right, there's no room for algorithmic misunderstanding.

Picture negotiating with a 17-year-old who's built a million-follower gaming audience. They love your campaign concept, but halfway through the call, their mom joins and suddenly wants to understand every clause about image rights because "what if this affects college applications?" The conversation shifts from usage windows to parental consent requirements, then to whether filming in their bedroom violates school policies. An AI trained on "standard" creator negotiations would have no framework for this dynamic.

These processes aren't automatable today because human creativity, relationship dynamics, market timing, and strategic positioning create variables that can't yet be encoded into algorithms. Every deal becomes a unique puzzle combining creative vision, legal requirements, relationship management, and business strategy. The combinations are infinite, the stakes are high, and the context changes constantly.

But AI Opportunities Will Emerge - We're Preparing

We're not anti-AI. We're strategically patient. AI capabilities are advancing rapidly, and deal-making patterns that seem chaotic today may become predictable tomorrow. Contract analysis, negotiation intelligence, and workflow optimization will likely become viable AI applications.

But we won't pass AI capabilities to customers until we trust the value they create. That's why we're so AI-forward internally - we need to validate AI's reliability, accuracy, and business impact in our own workflows before asking customers to depend on it.

Our Internal AI-First Philosophy

While we don't build AI into our customer-facing platform, we leverage it extensively internally. Our approach creates a testing ground for future customer applications while maximizing our team's current capabilities.

AI as Force Multiplier: For our lean team, AI isn't optional - it's essential. We use AI to scale our abilities, maintain consistency across outputs, and preserve focus by automating routine tasks so our attention stays on high-value activities that require human judgment.

Internal-First Validation: We believe in "eating our own cooking" when it comes to AI. Before implementing any AI capabilities in our customer-facing product, we rigorously test AI tools in our own workflows to understand their strengths, limitations, and failure modes. We need to trust AI systems ourselves before asking customers to trust them.

AI Usage Expectations: Everyone working with Basa is expected to try AI first before scheduling meetings or asking for guidance. This practice protects our collective time for truly high-value activities that require human collaboration while building our understanding of where AI excels and where it falls short.

How AI Is Changing Everything: A Real Example

Here's how radically AI is transforming how we work, and why this matters for our customers. Six months ago, a tool like V0 (Vercel's AI design platform) didn't really exist. Today, it's completely changed our operational model.

Traditionally, when someone who understands our customers identifies a product improvement, the process is broken and slow. Our subject matter expert - the person who actually lives in customer workflows every day - has to explain their idea to a product manager. The product manager translates that to a designer. The designer creates mockups. Back and forth refinement happens. Eventually it gets handed to developers. Weeks later, you might get the feature you originally envisioned.

But innovation happens bottom-up, not top-down. After months of frustration trying to capture what was already clear in her head through traditional design handoffs, our customer expert took it upon herself to test V0. The results were transformational.

Now she can turn her understanding of customer pain points directly into functional UI prototypes. Instead of playing telephone through multiple people, she generates working code that communicates her vision exactly to our development team. What used to take weeks of back-and-forth now happens in hours.

This isn't just faster - it's fundamentally different. The person closest to customer problems can now prototype solutions without becoming bottlenecked by traditional workflows. Our development team receives requirements as functional prototypes rather than static mockups, eliminating interpretation errors and implementation delays.

The impact on our customers is direct: we listen to their issues and implement improvements in days instead of weeks. While competitors wait for design resources or struggle with communication gaps between customer insights and technical implementation, we're iterating at the speed our customers need.

This represents something much bigger than a process improvement. It's a glimpse of how AI can fundamentally reorganize how work gets done - not by replacing human expertise, but by eliminating the friction that prevents expertise from flowing directly into solutions.

The Competitive Advantage of Internal AI Mastery

By using AI extensively internally while building human-centric customer experiences, we're accumulating advantages that competitors can't replicate quickly:

AI Literacy: Our team understands AI capabilities and limitations through daily use, not theoretical knowledge. When AI becomes ready for customer applications, we'll implement it thoughtfully rather than reactively.

Process Intelligence: Our internal AI usage reveals which workflows can be automated and which require human judgment. This intelligence informs both our product development and our customer AI strategy.

Trust Calibration: We're learning to calibrate trust in AI systems through real-world application. This experience will be invaluable when evaluating customer-facing AI opportunities.

Innovation From the Ground Up

The V0 example illustrates our broader philosophy: real innovation happens when the people closest to problems have the tools to create solutions directly. We don't dictate AI adoption from leadership - we enable team members to discover and implement AI tools that solve their specific challenges.

This bottom-up approach means we're constantly discovering new applications we never would have planned centrally. Our subject matter experts find AI tools that help them serve customers better. Our developers discover AI capabilities that accelerate implementation. Our operations team identifies AI solutions that eliminate administrative friction.

Each discovery becomes part of our collective AI literacy, building toward a future where we can confidently implement customer-facing AI because we understand exactly how and where it creates value.

The Strategic Choice We're Making

My AI research taught me that companies preparing for AI's second-order effects will win, not those chasing AI features. We're not building AI into Basa today because we understand AI too well to implement it prematurely.

The future isn't fewer human negotiations - it's many more, happening faster, with higher stakes. When AI makes content creation nearly free and deal volume explodes, teams will need infrastructure designed for human judgment at AI-driven scale.

We're building that infrastructure now while internally mastering the AI tools that will eventually enhance it. When AI capabilities mature enough to handle the complexity of real-world negotiations, we'll be ready to implement them thoughtfully, backed by years of internal validation and customer trust.

That's the foundation everything else gets built on - infrastructure so well-designed for human decision-making that it becomes the natural platform for AI enhancement when the technology catches up to the complexity of the problems we're solving.