Only the answer
Lenny’s tweet
Open Lenny's tweetNot enough people are talking about how much AI is impacting the role of data science.
— Lenny Rachitsky (@lennysan) May 13, 2026
I was chatting with a DS friend, and he said that most of his team's work now is reviewing half-assed AI data analysis from PMs and engineers. And that 50% of the time, that analysis is wrong.…
Answer with evidence
Details
Open the notebook
Net revenue — Product A, Q4 FY25
“What was net revenue for Product A last quarter?”
Short answer: $4.21M. This notebook shows how that number was built and what was checked against a trusted source.
1. Metric definition
Net revenue is gross − returns − discounts. Read the definition from the governed metrics layer so this matches what finance reports.
Python▶Code collapsed✦ Ask AI ⌘K
import yaml defn = yaml.safe_load(open("metrics/net_revenue.yml")) defn["expr"], defn["source"]
2. Net revenue for Product A
Pull net revenue for Product A for the quarter, straight from the order lines.
SQL▶Code collapsed✦ Ask AI ⌘K
SELECT SUM(gross - returns - discounts) AS net_revenue FROM finance.order_lines WHERE product = 'Product A' AND fiscal_quarter = 'Q4-FY25';
| net_revenue |
|---|
| 4,210,442 |
That's the $4.21M the chat answer reported.
3. Sanity-check the distribution
Break Product A down by region, then plot it. Nothing should look out of place against last quarter's mix.
SQL▶Code collapsed✦ Ask AI ⌘K
SELECT region, SUM(gross - returns - discounts) AS net_revenue FROM finance.order_lines WHERE product = 'Product A' AND fiscal_quarter = 'Q4-FY25' GROUP BY region;
| region | net_revenue |
|---|---|
| West | 1,740,000 |
| Central | 1,160,000 |
| East | 890,000 |
| Intl | 420,000 |
Python▶Code collapsed✦ Ask AI ⌘K
m = by_region.set_index("region")["net_revenue"] / 1e6 m.plot.barh(title="Net revenue by region · Q4 FY25")
West and Central drive most of the revenue, with International a small tail.
4. What I could not verify
Two inputs have no trusted source to check against. Instead of treating them as settled, each is left below as a cell you can run and edit to dig in.
SQL▶Code collapsed✦ Ask AI ⌘K
-- the agent's -$0.7M returns figure is an estimate; -- check the returns table for Q4 rows to back it SELECT COUNT(*) AS n_rows, SUM(amount) AS returns FROM finance.returns WHERE product = 'Product A' AND fiscal_quarter = 'Q4-FY25';
| n_rows | returns |
|---|---|
| 0 | NULL |
So that −$0.7M has no source to check against yet. The other open item is the customer join:
SQL▶Code collapsed✦ Ask AI ⌘K
-- 183 of 12,480 unique customers are in Billing, not CRM, -- so some revenue can't be attributed. Pull them: SELECT b.customer_id, b.amount FROM billing.invoices b LEFT JOIN crm.customers c USING (customer_id) WHERE c.customer_id IS NULL ORDER BY b.amount DESC LIMIT 5;
| customer_id | amount |
|---|---|
| BIL-44821 | 18,400 |
| BIL-39105 | 12,950 |
| BIL-50277 | 9,310 |
| … | … |
That's $61,540 of revenue whose customer attribution is uncertain, surfaced so a person can resolve it before the total is trusted.
I pulled Product A's net revenue from the order lines using finance's governed definition, broke it out by region, and flagged what I couldn't verify. The cells are on the left.
Hex examples
Generated from scratch
Jog the perimeter, then dynamic stretches: arm circles, lunges, and high knees. Finish with a partner toss to warm up the hands.
Pair students and set cones 10 feet apart. Partners practice underhand throws, then overhand. Cue them to step with the opposite foot and follow through. Widen the gap as accuracy improves and rotate partners every 5 minutes.
Static stretches and a quick recap of throwing cues: step, point, follow through.
Watch for a stepped throw with the opposite foot forward and eyes on the target. Note students who need a shorter throwing distance next class.
12 cones, 6 foam balls, 3 station markers.
Anchor plus diff
Fifty pages
The claimant is a 47-year-old warehouse associate who reports a lumbar spine injury on March 3, 2025 while lifting a carton estimated at sixty pounds. He describes immediate low back pain radiating into the right lower extremity, followed by numbness along the lateral calf.
Records reviewed include the intake questionnaire, an MRI of the lumbar spine dated March 18, 2025, twelve physical therapy notes, and the treating physician's progress reports through August 2025.
On examination, lumbar flexion was limited to 40 degrees with pain. Straight leg raise was positive on the right at 50 degrees. Strength was 4 of 5 in the right extensor hallucis longus, with diminished sensation in the L5 distribution.
Lumbar disc herniation at L5-S1 with associated right L5 radiculopathy, supported by the imaging and examination findings above.
Within reasonable medical probability, the disc herniation is causally related to the industrial lifting event of March 3, 2025. The claimant had no documented history of lumbar treatment prior to that date.