Hey - It’s Nico.

Welcome to another Failory edition and Happy Thanksgiving to those celebrating!

We’ve been working on new ways to bring more value to founders like you, and this Max MRR Tool is the first step (built with Google’s AI Studio). It shows your startup’s revenue ceiling and what drives it. More tools coming soon.

Here are today’s top 3 highlights:

Today’s sponsor, Delve, helps founders automate SOC 2 compliance with AI agents that turn a months-long headache into a 15-hour sprint. Also, shoutout to Cloudways, grab their Black Friday deal for effortless site migrations and big savings.

This Thanksgiving, we’re serving up more than turkey, we’re helping you stuff your pipeline.

Get $1,500 off compliance + a $500 Visa gift card when you become SOC 2 compliant with Delve before November 27th.

Because while your sales team is feasting, your deals shouldn’t be starving. Delve’s AI agents handle all the heavy lifting: automating your SOC 2 evidence, risk checks, and audits in record time. No spreadsheets. No manual madness. Just compliance done fast, so you can focus on your next big win.

The results speak for themselves:

Use code THANKSGIVING before 11/27 at 11:59 PM PST.

👉 Book your demo

Supercharge your site with high-performance cloud hosting. With Cloudways enjoy 50% off for 3 months and 50 free migrations. Activate the deal now! *

The Complete Guide to AI Prototyping Tools

Designing AI-Native Software

How to Price and Package Your Gen AI Feature

Anthropic releases Opus 4.5 with new Chrome and Excel integrations.

Sam Altman talked about OpenAI’s forthcoming AI device.

X begins rolling out the ‘About this account’ feature to users’ profiles

Warner Music signs deal with AI music startup Suno.

Momentic raises $15M to automate software testing

Onton raises $7.5M to reinvent the way the world discovers and decides what to buy.

AI workflow startup Model ML raises $75 million

Nuclear energy startup X-energy raises $700M in funding

* sponsored

You know the feeling when you’re buying something on Marketplace or meeting a stranger from a classifieds site. The doubt. The “please don’t scam me” energy. goConfirm tried to bottle that anxiety and turn it into a business.

But they ran into a classic problem: the product everyone swears is a great idea is often the product no one actually uses when it counts.

What Was goConfirm:

goConfirm went after the oldest P2P pain point: you never know who you’re dealing with. Their answer was a portable verified identity you could bring into any transaction. Not tied to Facebook, or Kijiji, or Reddit. Just you, verified once, re-used everywhere.

The setup was simple. Scan your ID. Take a selfie. Wait a moment while the system confirmed you were real. After that you had a ConfirmID you could show to anyone, like a digital passport for internet deals.

They bundled that identity with a set of tools meant to make deals feel safer. Verified chat. A “Vault” so you weren’t sending bank details through random DMs. And a small safety net: 250 dollars of loss protection if something went wrong inside the app.

To get traction, they pushed hard into the places where scams are most common. Facebook groups for rentals. Local buy-and-sell communities. And especially event-ticket subreddits, where fake tickets were a daily complaint. They cold-messaged mods. They helped communities create “goConfirm-verified” flare

For a while, it worked at the micro level. The people who actually used goConfirm seemed happy. They told the team the product made sense. But happy users don’t guarantee real growth.

Internally, the growth chart never bent. It just crawled upward in a straight line. Slow, predictable, and far from the type of compounding curve that justifies venture funding. A few weeks ago, the team finally called it and shut the thing down.

The Numbers:

Reasons for Failure: 

Why It Matters: 

People have been talking about AI-powered shopping for a while. Adobe’s data showed a 4,700 percent jump in generative-AI referrals to U.S. retail sites this year, which is insane. But even with numbers like that, the actual behavior always felt early. Lots of curiosity, not a real habit.

This week felt different. ChatGPT launched Shopping Research. Perplexity launched Shop with Perplexity. Both of them push people closer to a world where you ask an AI what to buy, it does the work, and you never touch a search page. It’s starting to feel like the default path, not a fringe behavior.

ChatGPT Shopping Research gives users a structured way to make bigger purchase decisions. It asks for context, pulls recent data from the web, and returns a clear breakdown of options, differences, and tradeoffs. It’s built for the moments when you want to actually understand the choice rather than skim.

Perplexity’s update works in a similar way. It keeps context across questions, uses past searches as signal, and connects everything to PayPal checkout. You discover, refine, and buy without breaking the flow. It’s direct and transactional in a way that feels closer to how people actually shop.

Both launches point to the same thing: the buying process is moving into the assistant itself. Users stay in one place from “what should I buy” to “I’m done.”

The bigger trend is simple: shopping is turning into guided decision-making. The assistant becomes the place where you figure out what you want before you ever land on a retailer’s site.

That raises the obvious question: do generalist assistants (GPT, Perplexity) win the whole thing, or is there room for vertical specialists that focus on specific niches?

There’s room for both. Generalists will dominate broad intent, but verticals can win the categories where nuance or taste matter. Home-decor, fashion, beauty, hobbies, equipment-heavy categories. These are places where you want depth, not generic reasoning.

Take Onton, for example. It focuses only on home decor. You describe a piece you’re looking for or upload a picture, and it finds products that match the style. Because it’s built around one category, the answers end up tighter than what a general model produces. Taste and constraints matter in these verticals, and specialization shows.

Founders should look at the gaps this shift is opening:

Then there’s the infrastructure layer this ecosystem will require. Models need clean catalogs, specs, pricing feeds, images, availability data, and merchant APIs. 

A few companies are nibbling at this (Manifest AI, Alhena, Amio), but the space is wide open. Opportunities include:

That's all for today’s edition.

Cheers,

Nico