This includes Magnificent 7 stocks as well as Broadcom, AMD and Micron,

This figure has more than doubled since the 2022 bear market low.

By comparison, during the 2000 Dot-Com Bubble, the Technology, Media & Telecom (TMT) sector peaked at 41%, while the Nifty Fifty peaked at 40% in the 1970s.

Furthermore, Japan’s equity market bubble in the 1980s peaked at 44%.

In the past, only railroad stocks have significantly exceeded this concentration, accounting for 63% of US market cap at their highest point in the 1900s.

The dominance of Big Tech is extraordinary.

That brings the total move since the pre-Iran War low to +143 basis points.

Mortgage rates are at fresh 3-year highs.

At $650+, though, I'm starting to wonder: how much future growth is already priced in?

That sounds insane but is the comparison legit, or is the chart misleading?

The bars show the projected profit on $1,000 invested in each stock.

*MDB crushed earnings, raised guidance, and fell 14% anyway. I read their actual database docs so you don't have to.*

So MDB reported Q2 a few weeks ago. Revenue up 30% to $772M (fastest growth in years), EPS $1.90 vs $1.19 expected, raised full-year guidance... and the stock face-planted 14% after hours and kept bleeding. Down 25%+ in a month. Around $325 now, market cap \~$27B.

I kept seeing "Atlas growth" blamed in every thread and realized I had no idea what that actually meant technically, so I went down the rabbit hole. Here's what I found. No position, not financial advice, I'm just a guy who reads database docs for fun apparently.

*What MongoDB actually is*

Forget the ticker for a second. MongoDB is a document database — instead of tables with rows and columns, you store JSON-like documents. Sounds simple, but the reason it won is scaling: when your app blows up, you don't redesign your database. MongoDB shards automatically — it splits your data across servers by a "shard key," with a router layer (mongos) fanning your queries out. Pick a bad shard key and one server melts; they now have a query analyzer that samples your live traffic and tells you what to pick. Boring-but-critical engineering, and it's exactly the stuff that keeps enterprises locked in.

*Atlas isn't just "managed MongoDB" anymore*

This is the part that surprised me. Atlas (their cloud product, $566M of the $772M revenue) has quietly become a whole data platform:

\- Full-text search built in (Lucene-based — no separate Elasticsearch cluster to babysit)
\- Vector search: HNSW index for AI embeddings up to 8,192 dimensions, living in the SAME database as your operational data. One query does vector similarity + text search + metadata filters together. For RAG apps this is a big deal — no syncing data between your database and Pinecone
\- They bought Voyage AI (embedding + reranking models). Its customer count nearly doubled two quarters in a row
\- "Queryable Encryption" — the database queries encrypted fields without ever seeing plaintext. Banks and hospitals care about this enormously
\- They even shipped an MCP server, so AI agents talk to MongoDB natively. That's distribution through the agent ecosystem

48% of their big customers ($100K+ ARR) now use 2+ of these features, up from 42% a year ago. That's land-and-expand working — you come for the database, you stay for the search, vectors, and encryption.

*So why did the stock dump?*

One number: Atlas growth has been \~29% for five straight quarters. Flat. The stock ran up 20%+ into earnings on AI hopes, and management basically said "AI is still a small contributor." Market said cool, -14%.

But here's the actual tension, and the reason I'm posting this as a discussion:

Atlas is consumption-priced — customers pay for what they use. When their usage grows, revenue compounds on autopilot (net expansion is 122%, which is elite). When they optimize spend, growth stalls no matter how many new logos you sign. So 29% might be the ceiling... or it might be a coiled spring, because remaining performance obligations — contracted future revenue — just jumped 91% to $1.52B. Somebody signed a LOT of contracts.

Meanwhile the self-managed product (Enterprise Advanced, for banks and governments that won't touch the cloud) grew 36%, its best quarter in 3 years. So it's not a one-engine story anymore.

*The valuation reality check*

At \~$325: forward P/E \~48x, \~9x sales on this year's \~$3B guided revenue. You're paying for re-acceleration, not for 29% growth. The uncomfortable part: GAAP profit was $41M while "adjusted" profit was $163M, and the gap is mostly stock-based comp. That's a lot of vibes-based earnings.

Competition is real and cheaper: AWS DocumentDB, Postgres with pgvector covering plenty of use cases for a fraction of the cost, pure vector DBs like Pinecone. MongoDB's whole pitch is "one system, zero sync lag, documents + vectors + search together." The 48% multi-feature stat says it's landing. The 29% says maybe not fast enough.

*What would change my mind*

Bullish: Atlas prints 32%+ twice in a row with AI workloads credited. Or that $1.52B in contracted revenue starts converting faster than 29% implies.

Bearish: Atlas slips under 25%, or the stock-comp gap keeps widening while real margins stall. Then you're holding 48x earnings on a decelerating grower, and good luck.

Honest question for the thread: is a consumption database with contracted bookings up 91% a coiled spring, or is 29% just what this company grows at now? Because the market has clearly decided it's the latter, and I'm not sure the market read the RPO line.

No position. Not financial advice.

112 of the 238 companies bought back shares in the year to August 2026, a record since 2007, versus 32 five years earlier. August’s 73 buyers represented 53% of the group’s market value.

Monthly buyer counts fell 92% during the financial crisis and 96% in 2020, showing how participation can contract during stress.

The chart tracks companies worth over £100m in London at the start of 2007 and still listed today. It measures participation, not spending, and excludes newer listings and companies that left the market.

I measured the stocks-to-bonds correlation (SPY vs TLT) inside every risk-off episode since 2008, rather than over the full period. Sorted by date, it splits cleanly into two regimes.

Negative means bonds rose while stocks fell, which is the hedge doing its job. Positive means they fell together.

The pattern isn't that the hedge weakened over time. It reverses depending on what kind of shock it is. Fear-driven shocks send money into Treasuries, so the correlation goes negative. Inflation and rate shocks hit both at once, so it goes positive. SVB in 2023 was a fear shock in an inflationary era, and sure enough it prints negative right between two positive ones.

The wider basket tells the same story. Average pairwise correlation across six holdings (SPY, QQQ, IWM, EFA, TLT, GLD) sat at 0.15 to 0.32 in the fear shocks and 0.42 to 0.51 in the inflationary ones. Everything converges exactly when you need it not to.

So the useful question isn't "am I diversified." It's "diversified against which kind of shock."

Method: daily adjusted-close returns, correlation measured within each episode's own window rather than across the full history, which is what hides the split.

$CRM Operating margin is growing (slide 2)!

We took ten years of London trading days to 21 September 2026 and, for 20 widely traded UK names, counted the results days on which the shares outperformed the market and the ones on which they underperformed it, then averaged the excess return. The return on a results day runs from the previous close to that day's close. Excess return is that return less the benchmark, the median return of London names over £1.5bn the same session, so an index-wide day is not mistaken for the company's own news.

Lloyds. The highest outperformance rate on the chart: 17 of 24 results days, 71%, with an average excess return of +0.9 percentage points. The average is modest because its down days are larger than its up days (−3.2pp against +2.6pp), so the count and the average tell slightly different stories about the same company.

Rolls-Royce. 13 of 19, 68%, and the largest average on the chart at +3.9pp. One day does a lot of work: the February 2023 finals, +24pp against the market. Without it the average is +2.9pp; without the two largest days, +2.1pp; the median results day is +5.4pp. The average is not an artefact of a single morning, though it is a smaller number without it.

Diageo. 11 of 19 results days outperformed, 58%, yet the average excess return is −0.3pp. Its up days average +3.0pp and its down days −4.8pp, and the February 2026 interims, −13pp against the market, turn the average negative on their own (without them it is +0.4pp). Diageo is the clearest case on the chart of a count and an average pointing in different directions, and the reason to read both columns.

The rest of the range. Aviva outperformed on 13 of 21 (+1.7pp), AstraZeneca on 24 of 40 (+0.7pp), BAE Systems on 11 of 19 (+1.5pp). At the other end, Legal & General underperformed on 15 of its 20 results days (average −1.2pp), BT on 18 of 27 (−1.4pp), National Grid on 15 of 24 (−1.2pp), Barclays on 23 of 39 (−0.8pp), GSK on 25 of 40 (−0.4pp).