The hottest SaaS Substack posts right now

And their main takeaways
Category
Top Business Topics

Understanding Substack's competitive edge

Simon Owens's Media Newsletter • 374 implied HN points • 13 Mar 26
💼 Business SaaS
  1. Substack has transformed from a simple newsletter tool into a full-service publishing platform with built-in recording, video, podcasts, AI clipping, communities, and OTT apps, making its 10% fee reasonable for creators who use many features.
  2. Creator-focused commerce platforms like ShopMy help smaller creators earn meaningful income by offering higher commissions and easier brand partnerships, expanding monetization beyond low-paying affiliate programs.
  3. Legacy publishers are shifting to subscriber-first newsletters because sending paid content directly to inboxes boosts engagement and lowers churn compared with website-only content.

Claude Code is the Inflection Point

SemiAnalysis • 45763 implied HN points • 05 Feb 26
🕹 Technology SaaS
  1. Claude Code proves agentic AI works in practice by reading environments, planning multi‑step tasks, and executing them so people can ask for outcomes instead of writing code; this shift is already making "vibe coding" and long‑horizon automation real.
  2. The cost of usable AI intelligence is collapsing, so agents can cheaply automate many information workflows and threaten seat‑based SaaS moats, BI, analytics, and lots of back‑office knowledge work.
  3. Anthropic’s agent stack and model advances are driving rapid revenue and compute growth, while big cloud players—especially Microsoft—face a hard choice between allocating GPUs to grow Azure or prioritizing Copilot to defend Office, either of which risks their long‑term position.

A Beginner’s Guide to Bring Your Own Cloud

Technically • 18 implied HN points • 26 Mar 26
🕹 Technology SaaS
  1. Customers in security- or compliance-sensitive industries increasingly want to run software in their own cloud, and they will pay 2–5x for that control to meet data residency, security, performance, and cloud-choice requirements.
  2. Deployment sits on a spectrum—from fully managed multi-tenant SaaS to single-tenant, hybrid (control plane + customer data plane), and fully self-hosted BYOC—each option trading convenience for control and observability.
  3. BYOC can be very lucrative for vendors but brings big operational headaches: installs, upgrades, debugging, and lost visibility get harder, so it works best when buyers have strong platform teams and vendors are prepared to support the complexity.

Clouded Judgement 3.26.26 - Per Token Pricing

Clouded Judgement • 7 implied HN points • 27 Mar 26
💼 Business SaaS
  1. Pricing must shift from flat seat or hourly models to token- or usage-based pricing that aligns costs with the actual value delivered, because inference is a real, growing line item that can destroy margins if mispriced.
  2. Monetizing GPUs by the value of output (tokens) instead of clock hours can generate far more revenue per GPU hour, especially for premium low‑latency workloads, since output is worth more than raw silicon.
  3. Founders and model providers need to manage falling token costs, pick where they sit on the latency vs throughput Pareto curve, and use credit-like abstractions to price on value; doing so will be a decisive advantage while getting it wrong can be fatal.

Vibes is all you need

Alex Ghiculescu's Newsletter • 203 implied HN points • 19 Mar 26
🕹 Technology SaaS
  1. Modern AI can write, test, and interact with your app autonomously, which removes many traditional engineering bottlenecks. This shifts the product vs engineering balance and pushes lead engineers to focus on shipping end-to-end and building the right architecture.
  2. To adopt this, try the tools on real bugs, run hackathons to show what’s possible, give everyone access to AI coding tools, and set an AI budget so teams don’t hesitate to use them. These practical steps lower friction and expand what people will attempt.
  3. Rethink product strategy and jobs-to-be-done: use AI to tackle ideas that felt too hard, cure writer’s block, and automate tedious gruntwork. Aim to build features that fully solve customers’ jobs rather than just incremental pieces.
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The SaaS Extinction Test

Generating Conversation • 186 implied HN points • 12 Mar 26
🕹 Technology SaaS
  1. Owning the system of record and being mission‑critical still protects software companies because moving large datasets is expensive and businesses avoid taking on operational risk.
  2. Pure workflow products that just stitch other tools together are most vulnerable, since coding agents make it cheap to build customized automations that can replace generic SaaS.
  3. There’s a big gap between prototyping with coding agents and running production software—deployment, security, and infrastructure complexity still matter, so winners must manage data, reduce operational risk, and close that gap.

How does Docusign have 7,000 employees?

SatPost by Trung Phan • 631 implied HN points • 13 Feb 26
🕹 Technology SaaS
  1. Big SaaS companies need large teams because they run mission-critical, globally regulated systems at huge scale, so they require lots of sales, support, engineering, security, and legal staff to ensure uptime, compliance, and customer integrations.
  2. AI coding agents will automate much of code production and shift value toward product taste, orchestration, proprietary data, and reliability/security expertise, forcing companies to rethink roles and org structure.
  3. Software demand won’t vanish — AI will create more software but change who captures the value, pressuring per-seat pricing and pushing SaaS firms to become systems of record or adopt usage- and outcome-based models to stay defensible.

6 Takeaways From Citrini's Viral AI Doomer Article (and a Bunch of Rebuttals)

SatPost by Trung Phan • 191 implied HN points • 27 Feb 26
🕹 Technology SaaS
  1. AI agents could automate large parts of white-collar work, pushing down prices and margins across SaaS, professional services, and payments, and risk creating real stress in incomes and financial markets if job losses are widespread.
  2. There are strong counterforces and practical limits—high compute costs, network effects, compliance, and time for adaptation—and productivity gains, new businesses, and policy responses could blunt or reshape the disruption.
  3. Vivid doomer narratives can move markets and public policy despite deep uncertainty, so businesses, workers, and governments should plan for multiple possible outcomes rather than assume a single future.

TBM 405: Hope, Context, and Control

The Beautiful Mess • 502 implied HN points • 07 Feb 26
🕹 Technology SaaS
  1. Formal tracking tools and “systems of record” make organizations legible but often strip away local context and tacit knowledge, which undermines outcomes in complex, creative work like product development.
  2. Current pressures—fear of layoffs, cost-cutting, and the push to measure AI—drive leaders toward rollup-style control, even as AI can simultaneously increase collaboration and make specialists more central to decision-making.
  3. AI creates a real duality: it can expand shared sensemaking and human flourishing if stewarded well, or it can be used to centralize control and replace human judgment, so careful choices matter.

10 AI pricing moves you can steal this week

Good Better Best • 3 implied HN points • 13 Mar 26
💼 Business SaaS
  1. Companies are experimenting with many AI pricing approaches — credit-based billing, modular add-ons, agent- or conversation-based fees, and freemium or trial offers — to see what customers will pay for.
  2. Enterprise plays are shifting toward bundled AI offerings on top-tier plans and custom credit allocations, which both create upgrade paths and force sales conversations.
  3. There’s no single right answer, so vendors are iterating fast: cutting back free credits, running trials, and adjusting packaging based on real customer behavior.

Clouded Judgement 3.20.26 - Digital Twins

Clouded Judgement • 14 implied HN points • 20 Mar 26
💼 Business SaaS
  1. Digital twins digitally capture human and institutional knowledge so AI agents can access and act on it, making knowledge representation the main bottleneck for scaling AI rather than model intelligence.
  2. They come in practical flavors—workflow capture, institutional memory, expert twins, customer twins, and knowledge multiplication—that help preserve know‑how, raise the floor of performance, and enable continuous research without repeated manual effort.
  3. Building a personal or company digital twin lets you scale and even monetize expertise that used to be limited by time, so early adopters who package their knowledge will gain a big advantage.

How to describe your business as an equation

Lenny's Newsletter • 7567 implied HN points • 16 Jan 24
💼 Business SaaS
  1. Every business can be distilled into a simple equation to understand its core aspects.
  2. Defining a business equation aligns teams, focuses efforts, and identifies leverage points for impact.
  3. Different business models like B2B SaaS, B2C, marketplaces, and DTC/e-commerce have specific equation examples to consider.

It's ok to be a mediocre tool

Frankly Speaking • 254 implied HN points • 28 Jan 26
🕹 Technology SaaS
  1. Switching security tools often costs more than it’s worth because procurement, legal reviews, learning curves, and integrations create huge operational friction.
  2. Choosing consolidated, “good enough” platforms or tools can boost efficiency and speed incident response, so accept mediocrity for low-to-medium risk areas like compliance or commoditized app security.
  3. Keep top-tier solutions for high-risk controls like identity and access, but for startups a simple, easy-to-integrate product that’s ‘not bad enough to switch’ can become a durable advantage.

Is n8n / low-code automation dead?

Enterprise AI Trends • 232 implied HN points • 01 Feb 26
🕹 Technology SaaS
  1. Natural-language, markdown-first automation tools challenge the assumption that non-technical users need visual drag-and-drop builders, because describing automations in plain English can produce deterministic, scalable workflows for complex AI tasks.
  2. Visual low-code tools are not dead but their role is evolving; enterprises will adopt natural-language automation gradually, leading to hybrid stacks and different tools for different problems.
  3. Product teams, operators, executives, and investors must reevaluate tool choices, training, renewals, and investments because bets on visual workflow platforms may be riskier as natural-language automation gains traction.

Killing SaaS. The anatomy of a murder

Experiments with NLP and GPT-3 • 23 implied HN points • 11 Mar 26
🕹 Technology SaaS
  1. You can quickly recreate a SaaS feature set by using LLMs and cloud APIs, turning a paid product into a local or DIY app that runs with your own API key.
  2. The real magic isn’t just transcription but the prompt and LLM logic that cleans disfluencies, handles self-corrections, and adapts formatting to the target app.
  3. Code and a working prototype are easy to produce, but distribution, product polish, and the business model remain the hard parts. Open-sourcing or packaging executables makes replication and customization trivial.

The Ultimate Positioning Guide (+ Worksheet)

Startup Business Tips 🚀 • 25 implied HN points • 01 Mar 26
💼 Business SaaS
  1. Make a clear positioning bet now instead of waiting for perfect data; deciding what you are, who it’s for, and who you compete with creates the data you need to test and improve.
  2. Follow the 3-step framework: pick a primary anchor (Activity, Use Case, Product Category, or Competitive Alternative), add one or two differentiators, then combine them into a single positioning statement. This structure makes messaging, targeting, and comparisons much easier.
  3. Choose the right level of specificity so you’re not too vague or too niche, and pick only real, defensible differentiators. Use a decision tree and worksheet to map your ICP, use case, alternatives, and to create clear internal and external positioning statements.

What real marketers are building with Claude Code

MKT1 Newsletter • 20 implied HN points • 02 Mar 26
🕹 Technology SaaS
  1. Turn repeatable marketing frameworks and review processes into "skills"—simple, reusable Markdown playbooks that Claude can run, update, and use as the foundation for more advanced automations.
  2. Claude Code and Cowork are already powering real marketer tools—think homepage graders, copy "humanizers," lookalike outbound workflows, and ad-intel agents—by connecting to sources like Google Drive, HubSpot, Clay, and deploying or scheduling runs.
  3. Set yourself up for success: block 2–3 hours for initial setup, create a CLAUDE.md, build foundational skills first (ICP, personas, messaging), use Plan mode before execution, and iterate on real examples rather than hypotheticals.

What Moltbot tells us about the ideal UX for AI

Generating Conversation • 186 implied HN points • 29 Jan 26
🕹 Technology SaaS
  1. AI should be present in the tools and workflows you already use, integrating deeply so it can act where and when you need it.
  2. Trust is earned by making the AI's work visible and giving users control to inspect, accept, or correct steps and decisions.
  3. Design AI like a teammate: it should do real work on your behalf, learn from feedback, and fit into your team's existing practices rather than forcing new ones.

How to build a data moat

Generating Conversation • 116 implied HN points • 05 Feb 26
🕹 Technology SaaS
  1. Think of a data moat as a loop: usage generates data that improves the agent, which drives more usage. Optimize both short-loop (real-time guidance) and long-loop (periodic model training) because the short loop speeds up gains and makes training more effective.
  2. Loop density — how often the loop runs and how much users trust it — determines whether a moat forms. Small, frequent units of work with low cost of failure (like code edits) create far better signal than rare, high-cost tasks (like full slide decks).
  3. Maximize high-fidelity signals by engineering for more and varied feedback: run multiple hypotheses, capture implicit negative and positive signals, and don’t rely only on explicit buttons. You generally need frequency plus either natural feedback or clear ground truth to collect useful, hard-to-replicate data.

AI Resume Builder 101: A Claude Skill That Customizes Your Resume for Every Job in 60 Seconds

The Product Channel By Sid Saladi • 10 implied HN points • 14 Mar 26
🕹 Technology SaaS
  1. Many AI resume tools fabricate experience, invent metrics, and add skills you don’t have, and they usually charge monthly fees.
  2. A skill that only draws from your personal experience library can generate ATS-friendly .docx resumes tailored to each job without inventing anything, rewriting summaries and reordering experience to match job keywords.
  3. With the right Claude plan the skill is essentially free and gives you full control; you just enable code execution, spend 10–45 minutes filling your experience library, and then get a tailored resume in about 60 seconds.

The AI churn wave?

Kyle Poyar’s Growth Unhinged • 441 implied HN points • 10 Dec 25
💼 Business SaaS
  1. AI-native apps have much lower retention than traditional B2B SaaS because many users are experimental and leave after trying the product.
  2. Pricing and distribution matter a lot: cheap, self-serve AI tools (under $50/mo) see massive churn while products above about $250/mo show retention similar to B2B SaaS.
  3. Sustained growth depends on durable retention. To reduce churn, focus on real-budget workflows, offer services or forward-deployed engineers, avoid overselling, accelerate adoption, and favor annual plans.

Clouded Judgement 3.13.26 - In Defense of Model Lab Profitability

Clouded Judgement • 12 implied HN points • 13 Mar 26
💼 Business SaaS
  1. Model labs can reach high, sustainable gross margins as they scale because serving and architecture improvements, better GPU utilization, and product optimizations drive down inference cost per token.
  2. Training costs are likely paybackable within reasonable timeframes similar to CAC payback, and even though retraining is recurring, marginal gross profit after payback can make labs profitable.
  3. Platform lock-in and enterprise needs (fine-tuning, SLAs, tooling, context storage) raise switching costs, so open-source models won’t fully commoditize large customers and retention should stay high.

Do software moats still exist? (ft. Claude Code)

Enterprise AI Trends • 189 implied HN points • 10 Jan 26
🕹 Technology SaaS
  1. Agentic coding tools can rapidly build and interact with complex enterprise apps, putting classic software moats at risk and forcing them to evolve.
  2. In a quick experiment, an AI built a barebones CRM in a few hours and autonomously extracted data from logged-in pages, showing how easily core functionality and data access can be replicated.
  3. Software businesses aren't necessarily doomed. They must rethink moats, focusing on continuous product differentiation, integrations, and defenses beyond enterprise inertia.

Why your marketing isn't working

Kyle Poyar’s Growth Unhinged • 425 implied HN points • 07 Dec 25
💼 Business SaaS
  1. Marketing needs a balance between great content and effective distribution. If you're creating amazing material but no one sees it, then you have a distribution problem.
  2. Product-market fit is no longer a final goal; it’s more of a constant challenge. As customer expectations rise quickly, businesses must keep up or risk losing their fit.
  3. Understanding your target buyer is crucial for success in selling your business. Different buyers look for different qualities, like profitability or growth, so tailor your approach accordingly.

An Interview With Peter Fernandez

ciamweekly • 125 implied HN points • 19 Jan 26
🕹 Technology SaaS
  1. CIAM is more than just security — it’s the gateway to seamless experiences across devices and providers using federation, MFA, and passkeys, and it’s becoming essential for B2B SaaS.
  2. Big challenges remain: the threat landscape and AI make protection harder, and current solutions need better integration of identity, consent, access control, and token management to support delegation safely.
  3. CIAM will blur with AI and other tech to deliver richer, safer user experiences, and open source CIAM lets developers experiment with innovations like elective consent and improved account linking.

SaaS 2.0

benn.substack • 1534 implied HN points • 11 Jul 25
💼 Business SaaS
  1. Salesforce is more than just a way to store lists; it's also a guide for sales teams on how to navigate complex selling processes. It provides structure to help salespeople do their job better.
  2. Creating a personalized software solution might require more work, but could lead to better results than a one-size-fits-all approach like Salesforce. Custom solutions can fit specific needs more effectively.
  3. Instead of relying solely on software to manage processes, hiring experts can be a better option. Experts can use their knowledge to adapt methods for your unique situation and simplify tasks.

What's really going on in software

Kyle Poyar’s Growth Unhinged • 489 implied HN points • 12 Nov 25
💼 Business SaaS
  1. Companies are seeing stability in key metrics like growth rates and revenue retention. New startups are achieving higher growth rates compared to previous years.
  2. It's important for companies to focus on the combination of customer acquisition costs and revenue retention to predict long-term success. This new matrix can help clarify business performance.
  3. AI is a major trend, but it's changing the industry landscape. Companies born after the rise of AI are experiencing much faster growth than traditional B2B software firms.

How to Build an AI Agent Company in 2026: Lessons from Glean’s $7.2B Playbook

Market Curve • 100 implied HN points • 26 Jan 26
🕹 Technology SaaS
  1. Make AI agents easy and reliable by hiding RAG and knowledge-graph complexity, connecting across apps, and grounding answers in company data so the system retrieves facts and says “I don’t know” instead of hallucinating.
  2. Get early customers by solving a real internal pain with long free trials and usage-first metrics, use high-touch onboarding and customer advocates to expand pilots into large enterprise deals.
  3. Start in a language-heavy vertical, build deep integrations and reusable agent templates (amplified by influencers), then scale with sales-led motions, bundling features while making security, permissions, and governance core.

Clouded Judgement 3.6.26 - Get in the Token Path

Clouded Judgement • 16 implied HN points • 06 Mar 26
💼 Business SaaS
  1. The biggest cloud-era infrastructure winners aligned their revenue with the platform's core consumption unit — they "owned the meter" so more usage automatically meant more revenue.
  2. In AI, tokens are becoming that core unit, so companies directly in the token path (models, inference platforms, and coding agents) can structurally scale as token consumption rises.
  3. Being in the token path is necessary but not sufficient — companies must build real differentiation and moats (better developer UX, vertical models, security/compliance, or proprietary data) and move quickly before token economics commoditize.

The new UX era

Kyle Poyar’s Growth Unhinged • 370 implied HN points • 19 Nov 25
🕹 Technology SaaS
  1. The prompt bar is becoming the standard part of many new apps. It allows users to quickly interact with the software but can also confuse them if they're unsure what to ask.
  2. Users now often learn how to use a product through their interactions rather than traditional onboarding. This means guiding them effectively in every chat is crucial for their success.
  3. Effective activation in AI products should help users quickly see value, with clear examples and next steps. This encourages them to return and use the product more often.

Will AI kill SaaS? The case for and against disruption

Brick by Brick • 45 implied HN points • 03 Feb 26
🕹 Technology SaaS
  1. AI that generates code and autonomous agents is collapsing the upfront cost of building software and can replace much of the human labor that SaaS products currently coordinate, threatening the old SaaS economic model.
  2. Big frictions—like high switching costs, regulatory and accountability needs, data gravity, and organizational inertia—make wholesale replacement of incumbent SaaS slow and hard.
  3. Disruption will be uneven and gradual: tools that automate repetitive, text-heavy workflows are most at risk, and winners will be challengers who target high-toil use cases or incumbents who proactively adopt agentic solutions.

The feature backlog has gone *poof*

next big thing • 32 implied HN points • 08 Feb 26
🕹 Technology SaaS
  1. AI coding agents have recently crossed a threshold and are letting developers and multi-agent setups write and ship a lot more product, so many teams are seeing their feature backlogs disappear.
  2. Companies are at different adoption stages, and engineering teams need to become fluent with agentic tools or risk falling behind; startups that use these tools can amplify their speed and focus.
  3. Public SaaS and companies aiming to IPO must show they leverage agentic engineering to drive faster feature delivery, revenue growth, and better margins, because easier software development risks commodifying existing offerings and hurting valuations.

Jevons Inversion

davidj.substack • 83 implied HN points • 09 Jan 26
🕹 Technology SaaS
  1. As code generation gets cheap and easy, people will build way more software than before and the line between writing and using software will blur.
  2. Many traditional application developer jobs may disappear as non-specialists who can orchestrate agents — "vibe engineers" — handle the long tail of one-off tools and automations.
  3. User-built software sidesteps much enterprise overhead (scaling, security, support), and with agents that remember and iterate, single-use scripts become cheap, reusable solutions rather than full products.

Clouded Judgement 2.27.26 - The Poison of Inertia

Clouded Judgement • 14 implied HN points • 27 Feb 26
💼 Business SaaS
  1. AI is rapidly changing how work gets done, letting smaller, flatter teams and new tools replace old roles and prompting big reorganizations and layoffs to remove inefficiency.
  2. Large incumbents are crippled by organizational inertia and often need to rewrite playbooks or start fresh, untethered units to adapt to new platform shifts.
  3. AI will materially lower software production costs, so legacy players must proactively cut bloat and restructure their cost base or risk being undercut by cheaper, modern competitors.

Paid subscriber tool: Claude Code & Cowork plugin for MTK1 frameworks

MKT1 Newsletter • 5 implied HN points • 02 Mar 26
💼 Business SaaS
  1. MKT1 offers a set of Claude-powered skills that run marketing frameworks so you can build strategy and materials faster.
  2. The included skills help with channel strategy, homepage positioning reviews, identifying marketing advantages, generating GACCS briefs, searching the MKT1 newsletter archive, and finding templates.
  3. The skills come as a plugin for Claude Code and Cowork — use slash commands or natural prompts, the plugin auto-updates, and installation details are available to paid subscribers.

How to Package Agents

Good Better Best • 2 implied HN points • 06 Mar 26
💼 Business SaaS
  1. SaaS companies are mainly packaging AI agents two ways: as paid add-ons with clear per-unit (credit) pricing, or bundled into higher-tier plans to drive upgrades.
  2. Credits and usage-based models are becoming the standard metric, often paired with gated business access and generous trial windows to prove value.
  3. The right packaging depends on fit: flexible, multi-agent needs favor add-ons, while purpose-built solutions like support automation are better bundled into core plans, and the market playbook is still forming.

Elon Musk sues OpenAI ⚖️, Rethinking the startup MVP 🔄, The Future of Digital Health 🩺, & more!

The VC Corner • 499 implied HN points • 03 Mar 24
🕹 Technology SaaS
  1. Elon Musk is taking legal action against OpenAI. This seems to be a significant move concerning AI and its implications.
  2. There is a need to rethink how startups create and test their minimum viable products (MVP). It's essential to find better ways to bring ideas to market.
  3. The digital health sector is evolving and has a lot of potential for the future. New technologies are changing how we approach healthcare.

The rise of AI forward deployed engineers

Generating Conversation • 140 implied HN points • 04 Dec 25
🕹 Technology SaaS
  1. Forward-deployed engineering is everywhere in AI now: engineers are working closely with customers to deeply customize agents, but this model is essentially advanced sales engineering and doesn’t make sense for low-value deals.
  2. AI buyers pay for work, not just access, so building useful agents requires significant customization and expert technical time to pull the right data at the right time rather than a one-size-fits-all product.
  3. Customer success has to move faster and act like a partnership; companies must choose between self-serve onboarding for simple, high-volume customers and white-glove engineering for complex, high-value customers, and prove value month-to-month to keep trust.

How to sell AI to Private Equity industry

Enterprise AI Trends • 126 implied HN points • 06 Dec 25
💼 Business SaaS
  1. Private equity is an ideal AI customer because they obsess over profitability, move fast, and have the capital to pay for tools that boost returns.
  2. There’s a big information gap: many PE firms don’t deeply understand AI, so they sometimes overpay for simple or copyable solutions, creating arbitrage opportunities for sellers.
  3. Winning in PE means selling differently — understand their buyer psyche and segments, and package pricing, delivery, and value messaging to match how PE evaluates and implements technology.

AI is everyone's favorite excuse to raise software prices

Enterprise AI Trends • 84 implied HN points • 17 Dec 25
💼 Business SaaS
  1. AI is making software more expensive right now. Many SaaS vendors raised prices in 2024–25 and are likely to keep raising them through 2026–27.
  2. Companies are bundling AI features into existing plans and hiking fees, effectively converting subscription revenue into “AI” revenue and limiting opt-outs.
  3. Structural forces beyond direct product value — like customers tolerating higher prices for high-value AI improvements and halo effects from better foundational models — are giving vendors sustained pricing power and a temporary “AI windfall.”