The hottest Business Models Substack posts right now

And their main takeaways
Category
Top Technology Topics

What comes next with open models

Democratizing Automation • 459 implied HN points • 16 Mar 26
đź•ą Technology Business Models
  1. Closed frontier models are likely to keep pulling ahead, so the model landscape will split into true closed frontier systems, competing open frontier weights, and many small distributed open models that fill niche roles.
  2. Weights alone aren’t a full product — real AI systems need tools, infrastructure, and user interfaces, and vertical integration gives closed companies a strong business advantage, so broad openness will be limited without clear economic incentives.
  3. The biggest practical opportunity for open models is building tiny, cheap, highly specialized models and adapters that handle repetitive tasks, complement closed agents, and form diverse ecosystems rather than trying to match frontier capabilities.

The case for bundling substacks

Wrong Side of History • 669 implied HN points • 03 Mar 26
đź’Ľ Business Business Models
  1. Substack’s paid-subscription model has enabled many talented, quirky writers to earn money and publish longer, independent work outside traditional media.
  2. The current per-writer pay model creates subscription fatigue because many readers can’t afford multiple paid subs, which can limit audience growth for mid-tier writers.
  3. Bundling paid Substack subscriptions into discounted packages with shared revenue and limits on switching could lower costs and grow audiences, but it should be opt-in and may not attract the highest-earning writers.

How Super Apps & Platforms Are Turning AI On Its Head

High ROI Data Science • 79 implied HN points • 30 Oct 24
đź•ą Technology Business Models
  1. Super apps in Asia grow by offering many services to a smaller customer base, unlike Big Tech that focuses on single services for many users. This helps them cater better to local needs.
  2. The advantages of super apps include faster product development, lower costs for data collection, and a unique competitive edge through exclusive data. They can quickly adapt to market changes too.
  3. Wrtn, a South Korean startup, shows how a super app can combine multiple AI services into one platform. This model offers better value to users and keeps them engaged with ads instead of multiple expensive subscriptions.

The AI bubble is all over now, baby blue

Marcus on AI • 15295 implied HN points • 26 Dec 25
đź•ą Technology Business Models
  1. The AI industry looks like a financial bubble that may start collapsing in 2026, with growing signs like heavy debt and strained economics.
  2. Large language models have inherent technical limits—especially their lack of world models—that make them unreliable and hard to monetize, and huge investments haven't fixed this.
  3. Once people accept these limitations as inherent rather than temporary bugs, many promised use cases and valuations will unwind, even though LLMs themselves will continue to exist.

The Sequence Opinion #823: SaaSmagedon, Is SaaS Dead?: Vibe Coding, Agentic Engineering, and the Collapse of the Code Moat

TheSequence • 266 implied HN points • 12 Mar 26
đź•ą Technology Business Models
  1. The SaaS business model is being fundamentally repriced as per-seat pricing, human-first interfaces, and the old code-based moat are losing value, which is causing major market sell-offs.
  2. The computational stack is shifting from human-written code to neural network weights and now to LLMs programmed by prompts, changing how software is built, deployed, and monetized.
  3. Autonomous AI agents and practices like “Vibe Coding” are turning products into outcome-delivering services (Service-as-Software), threatening CRUD-based apps and traditional SaaS monetization.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

AI #152: Brought To You By The Torment Nexus

Don't Worry About the Vase • 2150 implied HN points • 22 Jan 26
đź•ą Technology Business Models
  1. Big AI products are shifting to ad-driven and personalized business models, which raises privacy, incentive, and trust concerns about how answers and user data will be used.
  2. Capabilities are advancing fast — from better assistants and image/audio generation to widespread deepfakes and job-displacing automation — creating real harms, economic disruption, and geopolitical pressure over compute and chips.
  3. Alignment and safety remain unsolved and fragile: current evaluation metrics can be gamed, persona drift and deception are real risks, and trying to hide or censor discussions of misalignment often backfires.

AI Won’t Automatically Make Legal Services Cheaper

AI Snake Oil • 648 implied HN points • 12 Feb 26
đź•ą Technology Business Models
  1. AI alone won’t make legal outcomes cheaper because regulatory rules and professional restrictions can block or limit consumer access to AI legal tools.
  2. The adversarial nature of the legal system means productivity gains often spark an arms race—when both sides use AI, more work is produced but outcomes don’t necessarily get cheaper.
  3. Human bottlenecks (judges, lawyers, and the need for oversight) and procedural incentives mean institutional reforms are required before AI can deliver lower-cost, better legal outcomes.

AI as Electricity, AI as Magic

New World Same Humans • 30 implied HN points • 16 Mar 26
đź•ą Technology Business Models
  1. AI will show up in two ways: as cheap, widely available "electricity" that powers systems, and as "magic"—deeply personalized, context-aware tools that feel like enchantment.
  2. Selling raw model access is a commodity business and risks a race to the bottom on price, because many models are already good enough for most needs.
  3. The real winners will build AI magic by combining models with product design, user context, hardware, and distribution, and incumbents with strong user relationships have a major advantage.

Power in the Age of Intelligence

Not Boring by Packy McCormick • 297 implied HN points • 18 Feb 26
đź’Ľ Business Business Models
  1. New technologies make key inputs abundant, which magnifies the value of scarce, industry-specific assets so a few winners capture a growing share of economic value.
  2. To win you must identify the industry’s bottleneck (the Schwerpunkt), break it, seize the High Ground by owning the scarce defensible asset, and then integrate outward to lock in those gains.
  3. That often means building full‑stack businesses or using hardware and services instead of defaulting to SaaS, and investors must judge bespoke strategy and execution rather than rely on standard SaaS metrics.

The Death of Software 2.0 (A Better Analogy!)

Mule’s Musings • 1149 implied HN points • 16 Jan 26
đź•ą Technology Business Models
  1. AI agents with large context windows will act like fast, non‑persistent memory that does the real information processing, and their ephemeral outputs are flushed into longer‑term storage.
  2. Persistent data, state, and APIs become the valuable 'NAND' layer — the single source of truth that AI agents will read from and write to, so software companies must shift toward being infrastructure/API providers.
  3. Human‑facing UIs and many horizontal SaaS products (dashboards, visualization, RPA, connectors, etc.) risk obsolescence unless they retool to serve AI agents, and the next 3–5 years could be a major structural shift.

Why I Deleted ChatGPT After Three Years

The Algorithmic Bridge • 1295 implied HN points • 19 Jan 26
đź•ą Technology Business Models
  1. Ads in ChatGPT are a deal-breaker because they make the service prioritize advertisers over users and change the experience for people who don’t pay.
  2. The economics of running large AI models aren’t compatible with a free, high-quality consumer product, so companies will raise prices, cut quality, or turn to ads to cover costs.
  3. Promises about no ad influence and privacy are hard to verify, and the result will be a two-tier system where paying users get better, ad-free experiences while free users face subtle biases and worse outcomes.

Legal AI adoption is soaring (ft. Harvey)

Enterprise AI Trends • 232 implied HN points • 22 Feb 26
đź•ą Technology Business Models
  1. AI adoption in legal work is accelerating fast as big AI players ship vertical skills and plugins that target legal workflows.
  2. AI acts as a deflationary force for professional services, especially work priced by billable hours, and can hit services harder than traditional software.
  3. AI won’t instantly replace trained lawyers because of liability and regulatory nuance, but it empowers others to do more work faster — often displacing value through “another person using AI.”

Networked Conviction 004

Investing 101 • 83 implied HN points • 21 Feb 26
đź•ą Technology Business Models
  1. Structure investing work around three buckets — portfolio updates, Requests For Startups, and general investing ideas — to keep thinking practical and repeatable.
  2. There’s a real opportunity to build AI rollups that actually work, but most pitches fail because they misunderstand how rollups or AI function, so a clear, correct formula is needed.
  3. The best AI rollup ideas come from real-world experience and untapped market gaps, and someone with passion plus a concrete plan can make a meaningful product out of that greenfield.

The long road to cable

The Rectangle • 141 implied HN points • 13 Feb 26
đź•ą Technology Business Models
  1. Tech companies keep 'reinventing' ordinary things and often make them worse by adding needless complexity, monetization, or gatekeeping.
  2. A dominant engineering and data-first mindset has spread beyond tech, turning messy human experiences into crude metrics and encouraging overconfident leaders to act outside their expertise.
  3. Platform consolidation risks recreating cable-style monopolies for entertainment and other services, which shows why we need more diverse perspectives to balance tech's influence.

Crypto Business Models

DeFi Education • 459 implied HN points • 28 Jun 24
đź”® Crypto Business Models
  1. Crypto businesses are evolving beyond just interesting technology. They need to solve real market problems to succeed.
  2. Layer 2 solutions help reduce costs and improve transaction speeds. They thrive on attracting users by offering engaging applications.
  3. Most crypto platforms operate as marketplaces, connecting different types of users. They make money by taking a small percentage of transactions.

Nobody Cares About the News Anymore and the Business Is Built on That Lie

Startup Strategies • 85 implied HN points • 05 Feb 26
đź“° News Business Models
  1. Most people don’t actually care about the news anymore.
  2. People only glance at headlines or blurbs and don’t want to read full articles because they’re long, complex, and often boring; they compete with entertainment like Netflix.
  3. The news industry is deeply broken and is built on the false idea that people will consume traditional news the way they used to.

He Shut Down Time’s Moscow Bureau. Then He Built His Own Magazine

Simon Owens's Media Newsletter • 24 implied HN points • 18 Feb 26
đź’Ľ Business Business Models
  1. The collapse of legacy newsrooms pushed journalists to build new, independent outlets instead of following old corporate paths.
  2. Starting small and using niche entry points like food or quirky platforms can grow into a powerful creative brand, but heavy reliance on brand partnerships or star contributors can leave a media venture vulnerable.
  3. Moving to reader-supported, membership-driven models and combining digital work with an annual print edition can provide a more durable financial foundation after major setbacks.

Why No AI Games?

Donkeyspace • 9 implied HN points • 02 Mar 26
đź•ą Technology Business Models
  1. There are surprisingly few compelling games built around generative AI; early experiments exist but none have delivered the kind of mind‑blowing, new gameplay people expected.
  2. Practical barriers—high API costs, unstable third‑party models, and strong player resistance to AI in games—make it hard to build sustainable, widely accepted AI‑centric titles.
  3. Generative AI’s soft, unpredictable behavior clashes with what makes games fun: simple, deterministic rules that produce emergent surprises, so raw AI output often short‑circuits the mechanics that create playable depth.

A cold play

benn.substack • 1048 implied HN points • 18 Jul 25
đź•ą Technology Business Models
  1. The value of a domain name can vary greatly depending on who owns it. For example, chatgpt.com would be worth a lot more to a company like Google than to an individual.
  2. User experience (UX) is key in getting people to adopt AI tools. A good interface can make a product more appealing, regardless of how advanced the technology behind it is.
  3. Google faces a challenge in convincing users to choose their AI models over others. They have great technology but need to create better products that people actually want to use.

The Gold, the Shovels, and the Switch: Looking Forward at Big Tech’s AI Cap-Ex Build-Out without the Existential AGI Bet

Brad DeLong's Grasping Reality • 169 implied HN points • 18 Dec 25
đź’Ľ Business Business Models
  1. Big tech is building lots of AI infrastructure not because it’s betting the farm on core AI products, but to capture the rents from the AI boom by selling infrastructure and services.
  2. The AI labs are the ones digging for breakthrough models and customer demand, but core AI products may have low margins and fickle users, so those businesses carry higher risk of a bust.
  3. Cloud and platform companies often commoditize or give away core AI tools to protect their high‑margin businesses, and investors are increasingly valuing firms based on real cash generation rather than AI hype.

Guest Essay: VC Contagion

Newcomer • 1808 implied HN points • 31 May 23
đź’Ľ Business Business Models
  1. Venture capital supports unsustainable models to achieve scale, like with tech giants Apple, Google, and Amazon.
  2. Companies like Uber and Airbnb, initially fueled by VC funding, now face challenges as they struggle with profitability.
  3. VC funding has fueled a culture of excessive capital consumption, leading to concerns about sustainability and the future of innovation.

CROSSPOST: NOAH SMITH: The AI Bust Scenario That No One Is Talking About

Brad DeLong's Grasping Reality • 169 implied HN points • 09 Dec 25
đź’Ľ Business Business Models
  1. AI could be widely useful but still be a low-profit industry, with most of the value flowing to downstream complementors and users rather than to model makers or operators.
  2. Huge, debt-fueled data-center buildouts risk a finance-driven bust if the economic returns take many years to materialize, even if the technology itself keeps improving.
  3. A total technological flop like VR is unlikely given rapid adoption, but big incumbent platforms can block rivals by giving good-enough AI features away for free, preventing startups from capturing big rents.

Your 10 Favorite Stocks

Compounding Quality • 746 implied HN points • 11 Jan 24
đź’° Finance Business Models
  1. The Wisdom of Crowds can lead to remarkably accurate predictions.
  2. Learning from others and collaborating can result in more knowledge and insights.
  3. Choosing stocks based on sound investment rationale and data can lead to successful investments.

Issue #3 - Pulling Back the Data Ecosystem Curtain

The Data Ecosystem • 219 implied HN points • 28 Apr 24
đź’Ľ Business Business Models
  1. Data in a business starts with understanding its goals and needs. The success of data efforts relies on how well it aligns with what the business wants to achieve.
  2. The data lifecycle turns business needs into actionable insights. It involves sourcing data, organizing it, and finally consuming it to gain meaningful insights that support decision-making.
  3. Surrounding factors like market trends and organizational issues can impact how data is used. It's important to recognize these influences to address challenges and keep data initiatives on track.

OpenAI is coming for the app layer

Alex's Personal Blog • 131 implied HN points • 17 Nov 25
đź•ą Technology Business Models
  1. OpenAI is aiming to dominate both consumer and enterprise AI markets. They believe they can create valuable tools for everyone, not just the wealthy, and want to monetize these opportunities.
  2. Nvidia's upcoming financial results are highly anticipated, as they could impact the perception of the AI market. Their growth and demand for AI products might influence investor confidence significantly.
  3. Startups in the AI space face tough competition from giants like OpenAI and Anthropic. Those focusing on niche applications may have better survival chances, while broader ideas might get absorbed by larger companies.

Is being data driven a fad?

benn.substack • 843 implied HN points • 18 Oct 24
đź’Ľ Business Business Models
  1. The way we value companies might be changing. Instead of just looking at numbers, people are considering things like hype and public interest.
  2. Being data-driven used to be seen as a key to success, but now it seems less effective for some businesses. There are successful examples, but many companies struggle to use data well.
  3. Cultural factors, or 'taste', are becoming more important in the business world than just relying on data. This shift might mean that how people feel about a company matters just as much as the finances.

Dario Amodei + DeepSeek’s New Burdens

ChinaTalk • 518 implied HN points • 06 Feb 25
đź•ą Technology Business Models
  1. DeepSeek is facing challenges in managing corporate partnerships while maintaining its research-focused culture. They might have to balance getting support from big tech companies with staying true to their values.
  2. As DeepSeek becomes more popular, it risks losing its talented employees to other companies offering better pay. This could change the company's culture and innovation approach.
  3. If DeepSeek forms closer ties with the Chinese government, they could get funding and resources, but it may come with scrutiny and reduced independence. They need to navigate these relationships carefully.

Twitter or Bluesky? How about neither.

Silver Bulletin • 418 implied HN points • 13 Feb 25
đź•ą Technology Business Models
  1. Twitter has lost much of its influence compared to its heyday, now having less engagement and relevance in social media discussions. Many users are finding better alternatives for their online engagement.
  2. Despite challenges, Twitter still holds some value for quick updates, memes, and keeping up with special interests. However, its growth seems stagnant, and it's not heading towards a broader 'everything app' vision.
  3. The business model of platforms like Twitter faces inherent issues, as they struggle to balance being a platform and a publisher. This affects their profitability and long-term sustainability.

Leaving Substack

Mostly Python • 1257 implied HN points • 29 Feb 24
đź•ą Technology Business Models
  1. The author is moving their newsletter from Substack to Ghost as they feel Ghost is a better fit due to its focus on writing and its open-source foundation.
  2. It's important to consider the platform's business model when deciding on a service, as sustainable revenue streams can help avoid unwanted platform changes and dark patterns.
  3. Being able to export your data easily and understanding the platform's funding history are crucial factors to consider when choosing a service for the long term.

Le papa : Mediapart

L'Atelier Galita • 219 implied HN points • 19 Jan 24
đź“° News Business Models
  1. Mediapart is a well-known investigative journalism outlet that has been involved in many high-profile revelations.
  2. Investigative journalism is costly, time-consuming, and critical for revealing important facts and holding power accountable.
  3. Mediapart follows an independent journalism code, relies on subscriber revenue, and uses a technique of progressive revelations to maintain media attention.

The ROI Problem of AI: Dazzling Capabilities, But Powerful Market Incentives Blocking Bottom-Line Corporate-Profit Gains

Brad DeLong's Grasping Reality • 176 implied HN points • 30 Jun 25
đź•ą Technology Business Models
  1. AI technology is advancing quickly, but companies are struggling to turn that technology into real profits. Just having cool tech doesn't mean money will follow.
  2. When many companies are trying to give away AI services for free, it makes it hard for anyone to make a profit. This can lead to a situation where only a few big players survive.
  3. While users benefit a lot from new AI tools, the business world may not see the same gains. So, businesses need to be careful and think long-term about making money.

Nailing Release 1.0

Running Lean Mastery • 314 implied HN points • 03 Jun 23
đź’Ľ Business Business Models
  1. Focus on balancing speed, spend, and scope for a successful release 1.0.
  2. Understand the Kano model to prioritize essential product features like must-haves, delighters, and performance factors.
  3. Maximize your startup's unique value proposition by minimizing features using the Kano model.

The Nvidia economy and the benefits of toilet paper.

Jon’s Newsletter • 119 implied HN points • 09 Mar 24
đź’° Finance Business Models
  1. Nvidia is rising fast in the market and could soon be worth more than Apple. Experts believe its growth is different from the tech bubble of the past.
  2. During election years, the stock market often has ups and downs, but usually rallies in the second half. Historical trends show that certain sectors perform better depending on who wins the election.
  3. Investors should look for companies with strong 'moats' that protect them from risks. Essential products like toothpaste and toilet paper are always in demand, making companies like Procter & Gamble good choices.

From selling access to selling work (and what it means for you)

Kyle Poyar’s Growth Unhinged • 512 implied HN points • 30 Oct 24
đź’Ľ Business Business Models
  1. Companies are shifting from charging for access to software to billing for the actual work done by AI. This means businesses will charge based on how much the AI helps them, like charging per task completed.
  2. With these new pricing models, customers can pay only when they see results. However, it can be tricky to figure out who deserves credit for any success achieved.
  3. As the industry evolves, businesses will have to rethink how they predict revenue and manage customer relationships, making it more about actual usage and outcomes instead of just subscriptions.

10 Charts That Explain the AI Era

Perspectives • 7 implied HN points • 30 Jan 26
đź•ą Technology Business Models
  1. AI adoption has happened extremely quickly, with consumers embracing tools like ChatGPT far faster than past technologies, and we’re still in the early stages of broader impact.
  2. Training and running large AI models is very expensive and investment and infrastructure are concentrated in a few firms, so the ecosystem is still in a heavy build/investment phase rather than a mature, profitable one.
  3. Benefits are uneven: many corporate pilots fail to reach production, executives tend to gain more productivity than frontline workers, women use AI less, and entry-level jobs are being disrupted, so careful redesign and policy are needed to avoid widening gaps.

Deepseek: From Hedge Fund to Frontier Model Maker

ChinaTalk • 400 implied HN points • 09 Dec 24
đź•ą Technology Business Models
  1. High-Flyer, a hedge fund, is making big moves by venturing into AI research through a new company called DeepSeek. They want to create human-level AI instead of just copying existing models.
  2. Their success in the AI field comes from a unique hiring process that focuses on curious and passionate individuals rather than experience. This helps foster innovation within the company.
  3. Despite the high costs of running AI research, High-Flyer believes in funding their projects through a mix of their own resources and philanthropy. They prioritize long-term research over quick financial returns.