Introducing Large Rule Models: Combining Scope and Trust in AI Finance

We’ve been debating pricing a lot lately. And honestly - I still don’t have a perfect answer. But let's talk about it... AI products have broken the old SaaS playbook. Because how do you price something that learns, improves, and compounds value over time? We still chose fixed pricing for now. Why? Because it’s simple. Predictable. It makes onboarding and sales easier. But the more we scale, the more I see the problem: AI doesn’t create static value - it amplifies it. The same client can get 10× more outcomes using the same plan. So how do you charge fairly for that? 💭 Usage-based? 💭 Outcome-based? 💭 Hybrid? Every model has trade-offs: → Hybrid protects margins but adds complexity. → Usage feels fair but unpredictable. → Outcome-based aligns incentives - but is hard to measure and scale. Some of the best AI companies are already experimenting with “pay for success” pricing. And I believe this will eventually become the norm. But I’m curious - for those of you building or buying AI products: 👉 What pricing model actually works for you? What feels fair, sustainable, and scalable? here is the pricing options from Kyle Poyar So what do you think? comment, pls

💡 Should AI decide what you pay? Dynamic pricing powered by machine learning is everywhere, from airline tickets to SaaS subscriptions. While it boosts efficiency and personalization, it also raises tough ethical questions: 🔸 Is algorithmic pricing fair? 🔸 Can models unintentionally discriminate? 🔸 Should users know when prices are tailored? In our latest blog post, we explore the moral and operational challenges of AI-driven pricing strategies and why businesses must align optimization with transparency and ethics. 📊 Efficiency is powerful, but trust is priceless. 👉 Read the full post: https://lnkd.in/dKjZkrMQ

Everyone’s building “AI tools.” Few are building business results. Customers don’t care that you use GPT. They care that you: - Replaced a $1,000/month software stack - Saved 10 hours a week - Drove 20% more sales That’s the difference between hype and revenue. The winners won’t be the ones who say “we use AI.” They’ll be the ones who prove what it’s worth. So stop shipping shiny features. Start automating painful workflows. That’s where the money hides.

When I first started building AI systems, I’d pitch features. Integrations. APIs. Automations. And guess what? No one cared. Business owners don’t want “AI.” They want more deals, fewer costs, less stress. That was the turning point for me. Stop talking about how the system works and start talking about what it delivers. Now, every project I take on starts with one question: “What’s the outcome you actually care about?” If you can answer that clearly, the tech part becomes simple.

💡 I’ve been asking one simple question in every tech conversation lately: 👉 Is your solution truly AI-native? Because there’s a real difference — and it matters. A. AI-Native → born from day one around data science, iteration, and model improvement. B. AI-Bolted-On → traditional SaaS that added AI later as a feature, not a foundation. C. AI-White-Labeled → SaaS that outsources its “AI” entirely through a vendor. None of these paths are wrong — but they don’t produce the same results. When something is AI-native, intelligence isn’t a layer you add — it’s the operating system itself. You see it in accuracy, automation, and how effortlessly insights translate into action. In a world where everyone claims AI, the real question isn’t who has it — it’s who was built for it. That question sits at the core of 5-Out — built from the ground up as an AI-native platform helping restaurants make smarter, faster, more profitable decisions.

True vertical AI investing is about going where the big LLMs won’t. Not because they can’t build it. Because they lack the expertise. And the niche isn’t worth their time. Big models chase horizontal use cases. Productivity. Search. Copilots. They don’t go deep into regulated workflows, legacy systems, or obscure data. That’s where the opportunity is. You want founders in big, specialized niches. They should own SaaS contracts. They should have unique data. And they should truly understand the problem. These are the companies that build on top of the LLM layer. They own the workflow, the data, and the customer. That’s real vertical AI. And that’s where the best early-stage returns are hiding.

Everyone’s excited about AI agents — but few are budgeting for the real costs. Here is what my poll found. Agents don’t run in a vacuum. They need context, APIs, memory, and lifecycle orchestration. A few concepts to familiarize yourself with in <comments>. Hope you'll find them helpful!

AI “Fake People” for Research — A Real Talk for SaaS Founders & VCs Synthetic panels and simulated personas are transforming how SaaS teams validate ideas, test pricing, and model adoption — but the hype hides real risks. In 2025, AI-generated “fake people” can accelerate insight or amplify bias. 🔥 What founders & VCs need to know: Synthetic data ≠ synthetic truth Models can mirror past bias faster than you can detect it Used correctly, synthetic panels can cut research costs by 70% and speed PMF discovery If you’re investing in or building research-driven SaaS, read this before betting on “AI audiences.” Full analysis → https://lnkd.in/eKz-jFEn

The 'AI vs. SaaS' question isn't just theoretical; it's a strategic challenge for every business. This post provides a practical guide on navigating the evolving landscape. We'll identify which categories of SaaS are most vulnerable to direct AI disruption (e.g., simple automation, basic data entry) and which are thriving by deeply embedding AI (e.g., complex analytics, specialized industry solutions). Learn what questions to ask your current SaaS providers, how to evaluate new AI-powered tools, and strategies for future-proofing your technology stack. Get actionable insights to ensure your business not only survives but thrives in the AI era.

The DORA Report 2025 is out. Still the best resource for understanding what actually drives software delivery performance. AI is front and center again this year - and the data shows we’re in the middle of a real shift. A few things that stood out: - 90% of users now use AI. Most see clear efficiency gains. - But stability is down. Throughput is up, yet delivery has become less predictable. - AI is an amplifier. It makes strong systems stronger - and exposes weak ones. I’ve followed DORA for years. It remains my go-to reference for how SaaS companies can improve delivery and culture. This year’s findings mirror what we’re seeing in practice: success with AI isn’t about the tools - it’s about the systems - your platform, your data, your culture. Worth reading: https://lnkd.in/dhNSgrtp