An underappreciated implication of vibe coding is that software isn’t limited to “apps” anymore. It can be more like content.

Put another way, bespoke software has become so cheap, we will increasingly “hire” software to do the “jobs to be done” like communicating, entertaining, teaching, and persuading that we have relied on content to fulfill.

Where a food blogger might have written a recipe post, they can now deliver a meal planning app based on their recipe catalog. Where a travel vlogger might have recorded a video about a destination, they can now deliver an interactive travel guide. Where a tech expert might have written a buyer’s guide of the latest phones, they can now deliver a recommendation tool based on the reader’s budget and typical use cases. 

To be fair, creator built apps have already been happening at the “high end” of the creator economy, or where the creator was already a developer. In fact, this thesis hits especially home for me since some of my best content (like GPT-2 in Vanilla JavaScript) already is software. But vibe coding will democratize the dilemma I have faced: do I ship a video or do I ship an app? 

Historically, content was how expertise found its audience. But expertise is really just taste, and taste is what vibe coding runs on. Content creators of the future won’t ask “what should I write?” They’ll ask “what should I build?”

P.S. A logical next question is what will they use to build that software. Does the market need a “Lovable+Wordpress” that lets creators manage both their software and legacy content? But that’s a post (or software) for another day.

Remember when “View Source” democratized web development?

That simple right-click opened the door for millions of builders to learn by doing, sparking the participation flywheel that made the browser the dominant platform it is today.

Now, what if we could do the same for AI?

In my recent CascadiaJS talk, I explore how applying “view source” principles to AI models can make them as approachable as a webpage. No complex installations. Just fork, edit, inspect, and share. And all running in vanilla JavaScript in your browser.

If you’re a web developer curious about AI, this talk is your on-ramp. Watch now and try the app!

CascadiaJS Talk: https://www.youtube.com/watch?v=clqB9mpdwok

Saayn GPT2: https://spreadsheets-are-all-you-need.ai/gpt2/

Saayn Notebooks: https://spreadsheets-are-all-you-need.ai/notebooks/

A team at Zoom recently published "Live MCP 101," a benchmark that stress tests MCP agents on challenging queries. It's one of the few benchmarks designed specifically for MCP environments.

Interestingly the paper plots token usage against task success rates, which is useful for comparing efficiency. But token count isn't the same as cost, since different providers charge different rates per token.

So I pulled the actual market pricing for each model and replotted the data to show what you'd actually pay. Some models shift position pretty significantly when you account for real pricing instead of just token count.

The video walks through the original chart, the cost adjustment, and what changes when you look at it through a cost lens.

SAAYN Notebooks let you construct AI workflows using just HTML and JavaScript. This tutorial demonstrates how easy it is to extend these Notebooks with familiar web development tools. We'll use a cover letter generator as our example, but the same approach works for almost any workflow you can imagine.

• Try It Now: https://spreadsheets-are-all-you-need.ai/notebooks/
• Github: https://github.com/ianand/spreadsheets-are-all-you-need
• Discord: https://discord.com/invite/b66stSz87E

Many AI workflows die in isolation.

You craft the perfect prompt chain, get amazing results, and then… it stays trapped on your computer. Your colleagues can't use it. Your future self finds it a hassle. And heaven forbid you want to switch AI platforms.

What if your AI workflows could be as portable as a web page? What if you could email them to anyone, store them anywhere, and run them on any device with just a web browser?

That's exactly what Spreadsheets-Are-All-You-Need notebooks deliver. These aren't traditional spreadsheets. They're portable AI workflow engines disguised as simple HTML files. Open source. No servers. No extra tooling. No lock-in.

Watch the full walkthrough to see exactly how to turn your favorite AI interactions into portable templates that you can take anywhere and share with anyone.

Give it whirl! Questions? Feedback? Reach out on Twitter, Linkedin, Github, or hop in the Discord.

Welcome to Spreadsheets-are-all-you-need Notebooks. They’re what happens when a spreadsheet and a python notebook have an AI powered baby in browser land.

A self-propagating, runnable HTML fIle

A Spreadsheets-are-all-you-need Notebook is a runnable HTML file. That's right. You can send this file to anyone, they double-click it, and it runs. No local server, no software installation, no headaches. Recipients can adjust and modify the notebook, hit the export button, and get a new version with their changes. It's like a file format that includes its own application code.

Step-based organization

Instead formulas, a Spreadsheets-are-all-you-need Notebook processes data in JavaScript. Notebooks consist of "steps" (similar to a Jupyter cell) containing data tables and code that you can create, delete, or rearrange. When you tap "run all steps," embedded JavaScript in each step calculates the results.

The exact "formula" you need, on demand

But you don't write JavaScript; you simply describe what you want in natural language. The system uses AI to generate the code for you. For example, you can prompt: "Create a table from the product_orders[] that matches the format of revenue_by_product[]". The brackets tell the system you're referencing a table in the notebook, and it automatically passes table information like column names to the AI.

By placing prompting front and center, you can "vibe code" your notebook to use precisely the “formulas” you need for your task. No more, no less.

Zero-Dependency Sharing

Once you've finalized your workflow, colleagues can run it on their data without needing AI tokens or API keys. All data processing is self-contained in the notebook file itself. Simply pass in a CSV with your data, and get instant results.

Easy to extend

Because it's just a web page, extending functionality is straightforward. Want to add interactive controls? Simply embed HTML elements. API Integration? Call any API that supports CORS or JSONP directly from your notebook. 

Built-in Debugging

When was the last time you struggled with a spreadsheet issue and wished it had a debugger? Open your browser DevTools/Inspector and type console or debugger commands to debug your data processing using familiar browser dev tools without ever leaving your browser.

Give it a whirl

Visit Spreadsheets-are-all-you-need/notebooks to build your own notebook.

Have an existing spreadsheet or workflow you'd like to convert? Reach out! Join the Discord server and visit the #notebooks room or file an issue on Github. Thanks!

Why Should You Care?

Unlike research-focused events such as NeurIPS or CEO-oriented conferences like TED AI, the AI Engineer World's Fair is uniquely geared toward the builders turning AI research into tangible products. That makes it a good crystal ball on the future of AI. The last summit, for example, was one of the early signs that the Model Context Protocol (MCP) was reaching a tipping point.

Brace Yourself For...

With that framing, here's what you should be keeping an eye on:

• Coding Agents (aka SWE Agents): According to the organizers, the track on SWE agents and tools like Claude Code were the most packed of the conference. That's a pretty strong signal if ever there was one.

• Everything is a Spec: Sean Grove's (OpenAI) talk challenged the traditional focus on code in software engineering. In the future, "specs" will dominate and introduce a broader audience of stakeholders.

• Cursor for X: Sarah Guo (Conviction) broke down how Cursor's success can be turned into a recipe for other AI startups building application on top of foundation models.

Essential Viewing

If you're short on time, these two sessions will give you a broad overview of the important trends in the AI and AI Engineering landscape:

• State of AI Engineering 2025 Survey: This was an overview of the challenges and priorities in building AI products as voted on by the engineers building them.
• Trends Across the AI Frontier: This talk was a great summary of where the state of the art in AI is advancing across four frontiers: Reasoning vs. Non-reasoning, Open Weights vs. Proprietary Models, Cost, and Speed.

What Hasn't Changed: Execution is Still King

Before diving into the emerging trends, it's useful to ask what isn't changing. At the top of that list is execution because competition in this space can come from anywhere. As Sarah noted in her talk, Cursor is a prime example:

"Cursor did not invent code completion. They did not invent the model. They did not invent their product surface area. They just out-executed on every dimension."

Four Key Emerging Trends

Four key themes stuck out for AI product builders: Social-Data Network Effects, Prompts as the New Patents, Software is Content, and Domain Experts > Tech Experts.

Social-Data Network Effects

Advancing product and model capabilities increasingly hinges on cultivating a robust user-data flywheel. Unlike classic network effects where more users mean more reach (like early Facebook), here, user engagement and data help define the capability frontier of AI and ultimately drive improvements to the product functionality itself. This isn't new to AI but we should expect to see it as a common pattern. Kevin Hou from Windsurf presented a great "Data Flywheel" diagram illustrating this.

Prompts (and Evals) are the New Patents

These crucial data flywheel insights are distilled into new software artifacts like evals and prompts. These artifacts capture real intellectual property (IP) even though they are not code. As we result, they should to be treated with the same care as traditional IP (i.e. saved and versioned just like code).

Software is Content

On the first day of the conference, I had trouble choosing sessions, so I dumped the descriptions into ChatGPT to help me decide. By day two, I had "vibe-coded" this into a tool called SessionSorter.com that anyone could use and it was picked up by the AI Daily Brief to illustrate how packed the conference was with content. The point? This is software that wouldn't have existed without vibe coding and was used like content to illustrate an idea the same way a tweet or photo might have done. As AI makes building easier, expect more software to be created and used like content. What's the software equivalent of a listicle?

By the way, if you want to catch up on the important talks from the AI Engineer World's Fair, I've improved SessionSorter.com with deep links into YouTube for every talk that was in the live stream. To use the AI features, you'll need your own OpenAI API key but the source is on GitHub if you want to check it out.

Domain Experts > Tech Experts?

The "Tiny Teams" track got a lot of deserved attention, but a session by Denys Linkov (WiseDocs) on building modern AI teams in the enterprise was particularly insightful. He open with an interesting thought experiment: If you had a $5M budget, who's on your team? Is it a roster of top AI researchers from OpenAI, DeepMind, and Anthropic, or is it a team of nurses, army vets, insurance pros, designers, and a few ML engineers? As technology becomes easier to build with AI, domain expertise might become more important for unlocking product-market fit than pure AI expertise.

The Big Picture: Software is Experiencing a Value Chain Disruption

These four themes share a root cause: The manufacturing of software is undergoing a value chain disruption. This is similar to how Uber disrupted taxis or Airbnb disrupted hotels as shown below.

As explained by Ben Thompson of Stratechery, what tends to happen is in these value chain disruptions is:

• One layer of the incumbent value chain becomes commoditized and modularized.
• Another layer, often adjacent, integrates and captures the value.

Post-AI, it's clear there's a disruption, particularly in the "Code" part of the value chain, with "Spec" (prompts, evals) becoming more critical. It’s not hard to imagine “specs” as being the latent capital of software needs that would have been unmet and unimplemented if not for vibe coding (just as unused rooms were latent capital for Airbnb or unused cars were for Uber).

Where exactly the value will reintegrate is still unfolding, but the shift is viscerally undeniable for anyone who has used these tools.

So Long, and Thanks for All the Fish

A huge thanks to Swyx, Ben, and the entire crew for an incredible AI Engineer World's Fair. I can't imagine the work that went into it, and I'm already looking forward to next year.

About Session Sorter

SessionSorter was vibe coded during the 2025 AI Engineer World's Fair. After manually using ChatGPT to help me decide between the over 150 sessions (often happening simultaneously) by the second day of the conference I turned it into a tool that anyone can use. It even got a shout out on the AI Breakdown:

Thanks to the AI Engineer World's Fair for encouraging third party conference tools by making the session feed available in JSON:

Twitter "Change Log"

A year ago, I half jokingly proposed that “If you're not using AI 20 times a day, you're probably doing your job wrong.”

In multiple instances over the past month, we've seen that prediction become official corporate policy.

Companies like IBM, Morgan Stanley, Bank of America, Fiverr, and most publicly, Shopify are making AI usage mandatory. In a leaked internal memo, Shopify stated that “Reflexive AI usage is now a baseline expectation” and will be part of performance reviews.

And yet, many professionals still aren’t using AI regularly. Why? Because despite all the hype using AI well isn’t always obvious.

At a recent event on AI in HR, someone admitted to me they felt behind and didn’t even know where to begin. So I gave them two starting points:

1. Set an “AI Step Count”

Think of it like your Fitbit goal, but for your brain. Try to use tools like ChatGPT, Claude, Copilot, or Gemini 20 times a day. Since the average knowledge worker works a 10 hour day, that’s roughly every 30 minutes in a typical workday. If you go an hour without using AI, you’ve probably missed a chance to flex your AI muscle.

2. Replace Your Search Engine with a Chatbot

This is the easiest upgrade. Change your browser’s default search box to a chatbot. The search box is ideal because it latches onto a habit you already have. And it assuages data concerns: if you're willing to type data into a search engine, it's probably safe to type into an AI search tool as well.

Tools like ChatGPT have browser extensions, but you can also configure Claude or Gemini manually in your browser search settings. In the video, I show this for Chrome.

How are you upskilling on AI?

I’m currently researching how teams are upskilling for AI: What skills are missing? Which tools matter most? If you're navigating this shift individually or inside your organization, I’d love to hear what you’re learning. Please reach out.

You've probably encountered the headlines of companies struggling to see financial returns from their AI investments or executives cancelling AI rollouts due to disappointing ROI, and skepticism around AI's value in the enterprise. Yet, paradoxically, if you've personally used AI tools, you've likely felt their value. Why is there this disconnect?

The Disconnect Explained

Imagine a hypothetical company with three employees:

• Alice (Product): Highly skilled in product management, but less so in engineering and marketing.
• Bob (Engineering): Expert engineer but weaker in product and marketing.
• Eve (Marketing): Expert in marketing, with minimal skills in product and engineering.

Collectively, the company's competencies are strong across product, engineering, and marketing because each employee excels in their specialty. But what happens when we introduce AI?

Research shows that AI disproportionately boosts the productivity of non-experts, transforming them into "quasi-experts". In our hypothetical company, that means Alice, Bob, and Eve each become significantly better in areas outside their core expertise. For example, Alice can now handle engineering tasks reasonably well, Bob becomes decent at marketing, and Eve can engage effectively with product tasks.

Individually, this represents massive gains in versatility but most organizations are structured by speciality. Alice's newfound engineering skill or Bob's marketing insight is lost if their roles and workflows remain rigidly segmented. The gains in individual productivity may not translate to organizational ROI without changes to the company's structure.

Lessons from the Past

This scenario mirrors past historical technology transitions. For example, in transition from steam power to electricity, it took factories nearly 50 years to recognize the productivity gains. It took decades before factory owners realized they needed to reorganize completely, breaking away from centralized power sources of steam-power and redesign their workflows to fully leverage electricity's potential.

Similarly, to capture the full value of AI, organizations must reconsider and possibly reinvent their structures and workflows.

The Frontier Firm, The Frontier Job

Microsoft’s 2025 Work Trend Index provides a glimpse into this future, introducing the concept of "Frontier Firms," organizations where humans initially leverage AI assistants but eventually lead autonomous teams of AI agents.

The startup Oleve is an illustrative example. As discussed on the Latent Space podcast, this small company is generating outsized recurring revenue by empowering "product engineers" who blend multiple skillsets and enabling by AI agents to tackle tasks from product discovery to marketing.

AI has the potential to redefine your organization, your market and even the problem you're solving for your customers.

For organizations seeking meaningful ROI from AI, the question isn't merely "How can AI speed up our existing processes?" Instead, leaders should ask, "How must our processes evolve because of AI?"

Recommended Reading

Power to the people: How LLMs flip the script on technology diffusion

The many fallacies of 'AI won't take your job, but someone using AI will'