Ted Neward

Reading Metronome’s Monetization Operating Model, I kept coming back to one idea: pricing has become product. Software now delivers outcomes, not access. Yet most companies still charge as if they’re selling seats or licenses. That disconnect creates friction: for customers, unpredictability; for companies, stalled growth. The paper’s argument is simple but sharp—monetization isn’t a late-stage decision. It’s strategic infrastructure. Pricing needs the same ownership and iteration as any feature. Treat it like a surface that customers touch, not a spreadsheet buried in finance. If value is continuous and dynamic, pricing must be as well. That means product, GTM, finance, and engineering working from one system of truth. How many of us still treat pricing as an afterthought—when it should be a growth engine? https://lnkd.in/gnH7WzYf #Monetization #ProductStrategy #AI

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Agentic AI just took a production-grade leap. Kong open-sourced Volcano—a TypeScript, MCP-native SDK that composes multi-step agents across multiple LLMs and real-world tools, with enterprise-grade guardrails. Think 9 lines of code instead of 100+, automatic MCP tool discovery, plus retries, timeouts, OAuth 2.1, and OpenTelemetry. Paired with Konnect/AI Gateway (MCP server autogeneration and observability), this shifts agents from experiments to governed workflows. 🚀 What this means for the future of work: • Less glue code, more business logic: Developers spend time on outcomes, not wiring. • Governance moves left: Security and platform teams get OAuth, policy, and traces baked in. • Vendor-agnostic orchestration: Mix LLMs per step (plan vs. execute) for cost/performance. • Faster workflow innovation: Product and ops teams can ship agentic automation safely. 💡 Relatable scenarios: • RevOps: “Analyze last week’s sales, summarize for execs, post to Slack”—one chain, multi-LLM, auto-discovered tools. • Support: Agents pull from KB + ticketing via MCP, with end-to-end traces for audit. • IT: Turn your API catalog into an MCP server so agents can discover, request access, and act—without manual credential sprawl. 🔌 Skills to build next: • TypeScript for agent orchestration • MCP literacy (tool schemas, servers, permissions) • Prompt/flow design and evaluation • OpenTelemetry basics for agent observability • OAuth 2.1 and API security fundamentals 🔒 Questions for you: • Are you standardizing on MCP to reduce agent integration debt? • Who owns “AgentOps” (governance + observability) in your org today? • Which workflows would you refactor first if glue code dropped by 90%+? 📈 Near-term outlook (2–5 years): API catalogs become MCP-first, Agent Platform Engineer/Workflow Architect roles emerge, and outcome-driven automation becomes a core competency—not a side project. 🔮 #futureofwork #worktrends #careerdevelopment #skillsdevelopment #AI #automation #digitaltransformation #techtrends #upskilling #reskilling #careerchange #productivity #workplaceevolution #AIAgents #MCP #TypeScript #APIs #DevEx #MLOps

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An entire dev team quit. The token dropped 15%. And $100 million rotated to a competitor in less than a week. Zcash just experienced a full governance meltdown. The entire development team resigned amid internal disputes. ZEC dropped to around $475. Meanwhile, Monero surged 17% to $580 and reclaimed its spot as the largest privacy cryptocurrency by market cap. This happened in seven days. What's interesting isn't the drama. It's the mechanics. When one project falters, money flows instantly to the next best option in that narrative. Privacy coins are a small sector. But the pattern applies everywhere. Decentralization matters because it's a hedge against exactly this scenario. No single point of failure. No dev team that can walk away and tank your investment. Monero proved more resilient. The market noticed.

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$2 million ARR per employee isn’t a fantasy anymore: vibe coding platforms are doing it now. Vibe Coding: The Platform Shift VCs Can’t Ignore Let’s cut to the chase: vibe coding platforms (AI-native code & app builders) are breaking every growth chart in tech. Valuations? Up 350% in a single year. Collectively, these companies jumped from ~$7–8B in 2024 to $36B in 2025. That’s not SaaS inflation—that’s what a true platform shift looks like. One SaaS/VC model puts the TAM at $150–400B by 2030. That’s “household name” territory. Think AWS, not niche dev tool. But here’s what actually matters: Annualized revenue for leading players just crossed $800M. None of these startups are even out of diapers yet, most are 2–3 years post-launch. Investors are paying nosebleed ARR multiples. Recent rounds are closing at anywhere from 14x (Lovable) to a staggering 140x (Devin/Cognition). Classic SaaS trades at 8–15x. Belief in generational growth, or everyone just FOMO’ing the next Stripe? You decide. Capital efficiency is off the charts. We’re seeing $2M+ ARR per employee. Lovable hit $130M ARR with a team small enough to fit in a WeWork kitchen. (Some are crossing $1–10M ARR with literally single-digit teams.) What does that unlock? “Capital-light unicorns.” Teams can build multi-million dollar companies without needing to dine at the VC buffet for years. Founders keep leverage. Fundraising happens later (if at all), which means less dilution and a better shot at owning your destiny. And this shifts what it means to be a “value-add investor.” It used to be, “Here’s money, go hire engineers.” Now? Cash is cheap. Access, distribution, real insight, those are the differentiators. If all you bring to the table is a checkbook, you’re replaceable. Bottom line: The “old way” breaks down when a handful of devs + AI can ship products at the speed (and margin) of a midsize SaaS team. Vibe coding isn’t just a devtool trend. It’s a wedge into how startups get built, funded, and scaled. And the definition of startup efficiency is getting rewritten in real time. If you’re a VC and you’re not revisiting your approach, you already missed the first inning. Here’s the question I keep coming back to: Is this 2021-style hype, or will vibe coding be the next $100B+ category that quietly takes over the world? Curious where others net out. Is this the new Ferrari of startupland—small engine, ludicrous speed—or just the toy of the week? If you want the “why VCs should care” deck outline, DM me.

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Do not create 3D interactives… that must be rebuilt for every engine that need compiling every time you tweak that rely on brittle asset bundles and broken pipelines that lose behavior when exported that can’t load at runtime that are trapped in Unity, Unreal, or Godot that fall apart when your toolchain shifts URT3D is here. The first universal real-time 3D file. Today, build in Unity. Soon, load in Unreal, Godot or 3Js. One file. One format. Editor + runtime. No bundles. No lock-in. Available now at https://URT3D.com — Download the Unity SDK and start building today. Try it. Break the cycle. Feel the difference. #URT3D #3Dinteractives #gamedev #Unity3D #UnrealEngine #Godot #buildonce #fileformats

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Waymo at 600x. Perplexity at 90x. Klarna at just 5x. This chart tells you why AI-native SaaS is in a different league (and what SaaS founders can learn) 👇 I saw this chart comparing revenue multiples across the world’s top 37 unicorns. At first glance, it seems absurd: • Klarna does $8B in revenue → valued at just 5x. • Perplexity, OpenAI, Anthropic → all soaring beyond 40x–90x. • Waymo? $75M revenue → valued at 600x. And yet... it makes perfect sense once you dig deeper. 1️⃣ Revenue isn’t revenue anymore. Investors aren’t just buying numbers on a P&L. They’re buying motion. A dollar earned through AI-native GTM carries more weight than one earned through old-school outbound and AE headcount. Because it: • Scales faster • Converts with less friction • Comes with a higher margin and leverage 2️⃣ Distribution is becoming embedded. Traditional SaaS relies on motion: → Marketing drives leads → Sales pushes demos → CS fights churn AI-native products flip this. They integrate directly into where work already happens: Notion, CRMs, and terminals. The product embeds itself. Usage compounds. Expansion becomes the default. Distribution is no longer a motion. It's a system baked into the product. 3️⃣ Product-led isn’t the edge it used to be. PLG got you discovered. AI-led GTM gets you adopted. Today’s best companies aren’t just waiting for users to explore - they’re deploying AI agents that guide, recommend, and convert in real-time. It’s not just a UX shift. It’s a revenue engine. And investors are pricing it in. 4️⃣ GTM systems are replacing execution teams. In traditional SaaS, every stage of growth demands more people. In AI-native SaaS, the goal is different: Can you grow revenue without growing ops? But in AI-native SaaS, growth = smarter systems. Founders are building leaner orgs using: • AI-personalized outbound (Clay + GPT) • Matched audience ads (Meta, LinkedIn) • End-to-end RevOps infrastructure It’s not about hiring more. It’s about designing smarter systems. That’s why exits look different. $10M ARR at 30% YoY growth with a heavy team? → Maybe a 4x revenue multiple. $10M ARR but with AI-native GTM and lean ops? → Investors start seeing a 10x+ machine. P.S. Source of chart: Palle Broe's Substack (Rule of Thumb) ------------ That’s why we built SaasRise. To help SaaS founders build modern GTM systems that raise, grow, and exit on their terms. (Link in comments if you want to learn more)

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