Greg Langdon

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Greg Langdon

Greg Langdon

@GregLangdon

Louisville, KY USA Katılım Ekim 2011
255 Takip Edilen1.2K Takipçiler
Greg Langdon retweetledi
Keyhorse Capital
Keyhorse Capital@keyhorsevc·
Don’t forget! The Student Venture Capital Summit is this Thursday, March 27 at @SparkHaus in Covington, KY. The event is designed for students eager to learn the ins and outs of venture capital. Free to attend, register here ➡️ luma.com/b5de3cz2
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Greg Langdon
Greg Langdon@GregLangdon·
Eight moats for sustainable software companies
David Cummings@davidcummings

With all the continued talk about the SaaS apocalypse and the challenges facing software in the age of AI and “vibe coding,” last week on the podcast 20VC, startup investor Gokul Rajaram shared his eight moats for sustainable software companies. Similar to other frameworks around what makes a business durable, these are presented in the context of software and cloud. Let’s take a look at the eight moats for software: 1. Data Software companies with proprietary data have a clear advantage. This data can come from reaching a critical mass of customers or from exclusive agreements with third parties. Even in the age of vibe coding, where building software has less friction, some products are 10x better or only possible because of proprietary data. 2. Workflow Workflow refers to software that is mission-critical to running a business. Examples include accounting systems or e-commerce platforms. The more deeply embedded the software is in daily operations, the harder it becomes to replace. 3. Regulatory Companies in industries like financial services and healthcare often face heavy regulatory requirements. These include things like money transfer licenses or approvals to integrate with government systems. In some cases, only a limited number of players are allowed. In others, approval requires significant time and capital. 4. Distribution Getting software into the hands of customers is often expensive and time-consuming. Some companies build strong distribution advantages. For example, Apple’s App Store controls how billions of devices access software. Distribution is one of the hardest moats to build and one of the most durable once established. 5. Ecosystem Ecosystems emerge when a product becomes a platform and third-party developers build on top of it. Well-known examples include Salesforce .com and Shopify, each with thousands of integrations and add-ons. This moat takes significant time to develop and typically only happens once a platform becomes the clear leader in a large market. 6. Network Network effects are especially powerful in marketplace-driven software platforms that connect buyers and sellers. These often combine software functionality like ratings, reviews, and pricing with mechanisms to solve the chicken-and-egg problem. Over time, the value of the network compounds faster than user growth and eventually becomes the de facto standard. 7. Physical Infrastructure While software is often viewed as asset-light, some companies rely on physical components such as devices, equipment, warehouses, or data centers. If a product requires hardware and you have a million devices deployed, switching costs and customer stickiness increase dramatically. 8. Scale Some software companies achieve scale across multiple dimensions including geography, employee expertise, and customer base. This makes it difficult for new entrants to compete. This is especially apparent when startups try to displace incumbents or expand into adjacent markets with well-established vendors. When thinking about these eight moats, one approach is to assign a point or partial credit for each category and then total the score. Moats are a critical component of building a sustainable business, especially for software companies. Entrepreneurs would do well to evaluate their ideas through this lens, recognizing that most of these moats take significant time, effort, and success to build. Achieving one or two is difficult. Achieving three or four is rare. Companies that reach four or more are best positioned for long-term, durable success. That said, entrepreneurs should not limit themselves only to ideas that check multiple moats upfront. Instead, they should understand how software is evolving in the age of AI, where barriers to entry are falling, and be intentional about how they build defensibility over time.

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Greg Langdon
Greg Langdon@GregLangdon·
The American Healthcare Conundrum: github.com/rexrodeo/ameri… "The American Healthcare Conundrum is an investigative data journalism project. Each issue identifies one fixable problem in the US healthcare system, quantifies the waste, and recommends a specific solution.
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Greg Langdon
Greg Langdon@GregLangdon·
A guide to thinking about competitive risks to existing B2B SaaS companies from AI: saas-capital.com/blog-posts/int… "We developed the three candidate Rating Dimensions that we believe are close to the “principal components” of B2B SaaS AI risk as we understand it.
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Greg Langdon
Greg Langdon@GregLangdon·
A more rigorous test of LLM logic and reasoning using the Car Wash Test: opper.ai/blog/car-wash-… "I want to wash my car. The car wash is 50 meters away. Should I walk or drive?"
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Greg Langdon
Greg Langdon@GregLangdon·
AI agents are starting to eat SaaS: martinalderson.com/posts/ai-agent… "But my key takeaway would be that if your product is just a SQL wrapper on a billing system, you now have thousands of competitors: engineers at your customers with a spare Friday afternoon with an agent.
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Greg Langdon retweetledi
Amplify
Amplify@AmplifyStartups·
Most brands overlook a high-value part of their funnel: visitors who show intent but never convert. Untitled solves this by combining Identity Resolution and Contextual Intent Data into a single platform. Join Untitled and ViB for a webinar! REGISTER: getuntitled.ai/upcoming-webin…?
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Greg Langdon
Greg Langdon@GregLangdon·
"For us, we had 10 SDRs doing this inbound workflow, & now we just have one that is effectively QAing the agent. The other nine, we deployed on outbound. We were able to hold that lead-to-opportunity conversion rate flat. So the agent is as good as our humans were.
Tomasz Tunguz@ttunguz

I remember hosting a dinner of sales leaders to talk about AI. I asked them what will your CRM do for you in the future? Nearly unanimously came the reply : enable salespeople to spend most of their time with customers. Listening to Lenny’s Podcast with Jeanne Grosser, COO at Vercel, I discovered how some companies are achieving that milestone by transforming their inbound sales teams in six weeks. For us, we had 10 SDRs doing this inbound workflow, & now we just have one that is effectively QAing the agent. The other nine, we deployed on outbound. It was six weeks before we felt confident going from 10 to 1. So it wasn’t like this was a multi-quarter process. It actually moved super quickly. We were able to hold that lead to opportunity conversion rate flat. So the agent is as good as our humans were. In retrospect, it should be obvious that speed creates an unexpected advantage. A sales representative, or an AI sales representative, can respond & analyze a lead at any point during the day. It’s important to meet a customer at the point of maximum interest to capitalize on their inertia or excitement. It’s actually condensed the number of touches it takes to convert because it’s so much quicker at responding relative to leads inevitably sitting in the queue or coming in at nighttime & no one can get to it. The build? One engineer, part-time. The person who built the lead agent was a single GTM engineer. He spent maybe 25, 30% of his time on this. The target that every sales leader at that dinner articulated is now within reach. I think we’re getting to a point where with layering in agents, ideally, we finally get salespeople to a point where they’re actually spending 70% of their time interacting with humans. Thirty percent customer-facing time becomes seventy percent. Double the human contact means salespeople developing deeper relationships, driving more success for customers, & less on administration. That’s the promise of AI in the workplace. tomtunguz.com/vercel-ai-sale…

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Greg Langdon retweetledi
Amplify
Amplify@AmplifyStartups·
Join us on November 13th at the Kentucky Derby Museum for Vogt Awards Demo Day. Hosted by the @cflouisville, this free event starts at 5:30 p.m. with six innovative founders showcasing their groundbreaking ideas. RSVP here --> eventbrite.com/e/vogt-inventi…
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Greg Langdon
Greg Langdon@GregLangdon·
"Vibe engineering establishes a clear distinction from vibe coding. It signals that this is a different, harder and more sophisticated way of working with AI tools to build production software: simonwillison.net/2025/Oct/7/vib…
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Greg Langdon
Greg Langdon@GregLangdon·
The Content Signals Policy initiative for search engines & LLMs: arstechnica.com/ai/2025/10/ins… "allows website operators to consent to the following use cases: - search: providing search results - ai-input: retrieval augmented generation - ai-train: training or fine-tuning AI models
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Greg Langdon
Greg Langdon@GregLangdon·
Traction and funding for vibe-coding startups: techcrunch.com/2025/09/29/vib… "The company’s initial traction was explosive, reaching $2 million annualized run rate in just two weeks.
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Greg Langdon
Greg Langdon@GregLangdon·
A staff engineer's 6-week journey with Claude Code: sanity.io/blog/first-att… "Treating AI like a junior developer who doesn't learn became my mental model for success.
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