Growth Ritual #91
📋 In This Issue:
AI Engineer in Your Pocket
Four Thieves at the Louvre Just Taught Us a Masterclass in Building AI Products
God 2.0: Building the First “All-Knowing” Entity That Actually Answers — 🔒
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AI Engineer in Your Pocket
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Four Thieves at the Louvre Just Taught Us a Masterclass in Building AI Products
I want you to picture this. It’s a sunny Sunday morning in Paris, October 2025. The Louvre. The most surveilled building on the planet.
Four guys walk in. Eight minutes later, they walk out with €88 million in crown jewels.
Did they hack the mainframe? Did they rappel from the ceiling like Tom Cruise? No.
They wore hi-vis vests.
They brought a furniture lift, dressed like construction workers, and just... went to work.
The security guards didn’t stop them because our brains —and the security cameras—are wired to ignore “normal”. In sociology, it’s called the presentation of self. If you look like you belong, you become invisible.
Here is the scary part (and the opportunity for us): Your AI product has the exact same brain damage.
The “Normality” Trap
AI doesn’t actually “see”. It categorizes. It’s a math equation, not an eyeball.
AI is trained to flag outliers.
If a person runs fast in a museum → FLAG.
If a person wears a mask → FLAG.
If a person wears a construction vest and walks slowly → IGNORE.
The thieves hacked the “Ignore” function. They performed a “Contextual Injection Attack” on human reality.
As builders, we rely on these same datasets. We train our models to look for the statistically suspicious. This creates two massive problems (and one massive business idea):
The False Positive (Bias): AI over-scrutinizes people who don’t fit the “norm”. This is why facial recognition systems have historically had error rates of up to 34% for darker-skinned women compared to 0.8% for light-skinned men. The AI doesn’t have enough data on the category, so it defaults to “suspicious”.
The False Negative (The Heist): AI ignores the “Hyper-Normal”. If a bad actor figures out your “safe” parameters —like a spammer using perfect grammar or a thief wearing a vest— they walk right past your firewall.
Building “Contextual Verification”
Most of you are building apps that look at Content (What does the image/text say?).
I want you to pivot to Context (Does this make sense here and now?).
The Louvre guards saw “Construction Workers” (Content = Match).
They failed to process: “Construction Workers + Sunday Morning + Museum Balcony” (Context = Mismatch).
Here is the Niche Application Angle for your next SaaS: Don’t build another “Security Camera AI” that just looks at pixels. Build a “Logic Layer” API that looks at metadata relationships.
You don’t need to build the foundational model. You build the “common sense” wrapper around it.
Here are 5 specific “Contextual Verification” micro-SaaS ideas you could build right now to monetize these blind spots:
1. The “Impossible Expense” Auditor (FinTech)
Current expense AI just reads the receipt via OCR. “Does $200 at a steakhouse look real?” Yes.
The Contextual Layer: Connects to the employee’s Google Calendar and company phone GPS data. “Receipt shows steakhouse in Chicago at 7 PM. Calendar shows employee was on a flight to London at 7 PM” → AUTO-FLAG.
2. The Remote Deepfake Detector (HR Tech)
Current hiring AI analyzes video frames and voice patterns for authenticity.
The Contextual Layer: Analyzes the metadata around the interview. “Candidate claims to be in San Francisco. IP address is residential Moscow. GitHub commit history shows active coding during their claimed ‘sleeping hours’” → AUTO-FLAG.
3. The “Party Risk” Engine for Airbnb Hosts (PropTech)
Current platforms verify the user ID and credit card.
The Contextual Layer: Ignores the ID and scores the booking logic. “User lives in the same city as the rental. Booking is for one night only, on a Saturday. They booked a 4-bedroom house for 2 guests” → HIGH RISK FLAG.
4. The Shopify Review Logic Layer (E-commerce)
Current fraud detection looks for bad grammar or copy-pasted text in reviews.
The Contextual Layer: Looks at transactional behavior surrounding the review. “User left a 5-star review. But they have returned 90% of their purchases in the last month and never opened the tracking email for this specific item” → SUSPICIOUS FLAG.
5. The “Tire-Kicker” CRM Filter (B2B Sales)
Current lead scoring looks at job titles and email domains.
The Contextual Layer: Scores the session reality. “Lead filled out the ‘Enterprise Demo Request’ form. But their session duration was under 8 seconds, and they came from a low-quality display ad network at 3 AM their local time” → LOW INTENT FLAG.
The Takeaway
Whether you are an Indie Hacker or running a VC-backed ship, stop trusting “patterns”.
If you are attacking (Marketing/Growth): Put on the “Hi-Vis Vest”. Don’t try to be the loudest outlier in the inbox. Mimic the “normal” behavior of your customer’s favorite peer. Fit the category to bypass the mental spam filter.
If you are defending (Product/Tech): Audit your code. Where are you assuming “Standard = Safe”? That is exactly where you are going to get robbed.
The thieves in Paris proved that conformity is the ultimate camouflage. Don’t let your startup get fooled by a cheap vest.
IN COLLABORATION WITH RETURN PRIME
Your Biggest Leak is Now Your Best Funnel
For those of us building in e-commerce or running a D2C brand, “returns” is the metric we hate to look at.
It’s pure revenue leakage, a logistics nightmare, and a black hole for customer support hours.
We spend a fortune optimizing CAC to get the customer, only to watch profits walk right out the back door. But what if we’re looking at this all wrong? What if the return process isn’t an operations problem but a high-intent marketing touchpoint?
That’s the entire thesis behind Return Prime, and it’s a total game-changer for revenue retention.
Instead of just processing a refund, their platform turns that moment into an automated micro-funnel.
When a customer initiates a return, Return Prime doesn’t just say “OK”. It nudges them to exchange for a different size or product. It also has built-in upsell features, allowing customers to add new items to their cart during the return process.
And that’s not all. If shoppers still choose to return, it intelligently encourages them to convert the cash refund into store credit —often with a small bonus incentive— helping brands retain customers and increase LTV.
The results are wild: brands using it are seeing an average of 12% additional revenue generated from what used to be a total loss.
We obsess over building scalable systems, and this is a critical, often-ignored, part of the stack. Return Prime automates this entire revenue-saving flow, plugs into over 100 logistics and tech partners (like Shopify, of course), and gives you a new lever to pull for increasing LTV.
They have raised a total $68 Million in Funding from marquee investors and are the only 5-star rated returns app on Shopify (with over 700 reviews), so this isn’t a guess, it’s a proven, high-ROI play.
If you run a D2C brand or build for them, stop treating returns as a cost center. Check out Return Prime and start turning your returns into revenue.