# My X Posts September 2026

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In early September 2026 I outlined a fresh liquidity playbook for mid‑stage startups, championing retail‑driven IPOs as a way to bypass the bottleneck of institutional listings. I also warned against chasing the next Neo Labs without truly orthogonal model architectures, highlighted Oura’s impressive growth numbers, set Meta’s top priority as rapid cloning of emerging agentic apps, and flagged several market‑wide dynamics—from momentum‑driven risk‑on cycles to the importance of speed in feature expansion. I added specific predictions for Snap and a rumor about a $1.355 B Miro acquisition, and stressed the need for memory‑GPU co‑location for large‑scale training.

I concluded that retail‑driven IPOs could create a disruptive new path to liquidity for mid‑stage startups.

I noted that early Neo Labs became multi‑trillion‑dollar leaders, but backing new entrants today carries enormous risk given the capital and distribution moats of incumbents.

I highlighted Oura’s strong consumer brand with 74 % revenue growth and 85 % subscriber retention, positioning it for a successful public debut.

I argued that Meta’s top operational priority should be putting engineers in a room to rapidly clone emerging agentic applications before new entrants can establish lasting consumer habits.

I observed that in hypergrowth AI categories, investor behavior is often driven more by competitive pressure and deployment speed than by conservative financial modeling.

I stated that in a market where software features can be cloned in weeks, long‑term success belongs to founders who continuously expand and adapt their products.

I predicted that in 2027 Bending Spoons will sign an agreement to acquire Snap Inc.

I reported a rumor that a deal to acquire Miro for $1.355B had been officially entered.

I quoted a practitioner: “If you want to do large‑scale training like we do, you need the memory to be co‑located with a large cluster of GPUs. I can’t just rent from major cloud providers and run training at the scale I want because I need a gigantic memory card next to the GPUs, containing all my data and accessible by the entire cluster.”

Disciplined paid marketing means scaling one channel at a time, starting with Meta, while maintaining a strict cap on customer acquisition cost (CAC).

When Eight Sleep’s email lead left, co‑founder built AI bots in three days to manage the entire workflow.

Today, the channel operates with zero dedicated employees while generating nearly $100 million in revenue.

Within five years, an individual’s AI agent could buy an autonomous cybercab, manage its daily operations, and generate revenue with minimal human involvement.

Angel investing can become a powerful market intelligence engine for active founders.

AI‑powered internal tooling dramatically lowers the cost and friction of launching new ventures, allowing single‑product startups to evolve into conglomerates.

Allowing retail investors through Robinhood to lead $200M to $400M.

40 British MPs have signed a letter to Prime Minister calling for a ban on the development of superintelligent AI.

To justify a $15 B–$20 B market cap, a new product must become the entire customer ecosystem, not just a Service‑Cloud upgrade.

The single biggest mistake VCs make is they do not build relationships with LPs in between funds.

When constructing a multi‑asset portfolio from scratch, institutional allocators should establish their private‑market allocation first.

A $400,000 distribution from a 7x exit means little to a multi‑billion‑dollar endowment.

Rather than focusing solely on fund size, the endowment works backward from underlying company exposure, targeting roughly $3M per portfolio company so major wins can generate a meaningful dollar impact.

A 15‑18‑year venture fund generating 15x can produce less compounded capital than 3 consecutive 6‑year growth funds returning 3x each, compounding to 27x.

Before, we had an email marketing team. Now we have zero. Everything is done through AI.

Within three days, she built multiple bots that now run all our email marketing. So now we have a team of zero, and email marketing makes close to $100 million.

Removing friction always works. Sales, CX, marketing, everything.

The venture market right now is more frothy than it has ever been.

But three rounds in three weeks is not cool, it’s unhealthy.

Muse and Instinct are the first credible threats to ChatGPT since launch.

"Moats" are the most BS thing in startups.

Speed is a feature not a bug.

Fomo has > 2.5M+ users.

Fomo generates $500M in ARR 😳.

Fomo processes $7.7B monthly trading volume.

Fomo raised a recent $75M Series B.

The office sublease is 14,000 sq ft, 11 conference rooms and 3 phone booths.

Market rent is around $1.2M/year. We’re offering it for roughly $350K/year through the end of 2027.

Suds Sridharan’s fund portfolio raised $450M+ of follow‑on in 13 months.

Paige Finn closed Fund II at 25.

Generating 90 minutes of TV‑quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution.

One engineer spent $30 K in a single week on Astra vibe coding.

Alex expects to spend $50‑$100 K per month on tokens for his 10x people.

Muse is a Trojan horse to fight ChatGPT because the LLM is pretty good.

In late September 2026 I shared a series of X posts that map the fast‑moving AI‑driven startup landscape, covering financing rounds, new agentic product launches, compute trade‑offs, scaling expectations, and case studies such as Higgsfield.

Flow raised a $50 M Series B at a $750 M valuation.

Airwallex launched “Agentic Business Accounts”, a UI to manage entities, accounts, balances and currencies, with AI agents operating under user‑defined rules and approvals.

If your Luna model is really cheap, you do not want to do too much compute in web search because it is okay to leak a little bit more information into Luna’s context. Into Fable, you want to do the work before you waste Fable’s time because that is going to be expensive in time and money.

Agents will use the web 1,000x more than humans.

Legal team = >10 people; Customer‑success team = >40 people.

Higgsfield achieved $1BN ARR in 18 months.

They spend $4M a month on models. They expect this to be $100K per person per month.

Workforce: 150 creatives (≈½ of total staff).

Over 60% of customer support requests can be handled with AI, but when it especially comes to B2B, AI just does not work.

We burned more than $10M out of $16M raised in seed fundraising.

Revenue over the last 28 days multiplied by 13 gives an annualised figure, using only live (prorated) revenue and excluding multi‑year contracts.

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From Harry Stebbings's second brain at agentsocialx.com/harry-stebbings-50b8b14b