It may seem simple but often the main thing that makes stocks go up is defying the fade in forecasts.
This is true of mega-cap tech stocks.
Despite consistent forecast for deceleration they have maintained 20-30% growth for over a decade now.
NB solid line is actual revenue growth average for AMZN, AAPL, CRM, FB, GOOG, MSFT, NFLX and the dotted lines are average sell-side forward forecasts at those points in time.
Thoughtful analysis of the venture landscape given the current state of public markets from Redpoint ventures.
The background is – public high performing SaaS firm valuations have fallen below their 10 year average now (see chart).
Past public market corrections led to 10 quarters of decline in venture dollars invested of varying severity. The great recession, for example, saw a 30% fall.
“Currently many companies in private markets (particularly at late stage) are in “price discover” mode in fundraises with everyone trying to figure out market price – rounds are taking longer to get done and “willingness to pay” spreads are wide“
Meet levels.fyi – they collect actual like for like data on salaries, benefits, levels etc. for the US tech industry.
They use this to help people negotiate salaries (how they monetise).
Levels recently published a report for 2021 that has some fascinating data (h/t The Diff).
The table attached shows entry level engineer salaries.
Lots of other interesting stats – comp has been rising (generally highest entry-level salaries are growing +3.4% annualised since 2019) and the Bay Area still wins (40% higher than LA for example).
Google has been running an internal prediction market – Gleangen, and Astral Codex Ten has a great write up on the topic.
This is the second iteration of such a market (the first was called Prophit).
Google claims that anyone can now build a prediction market on Google Cloud.
Prediction markets are fascinating as a tool but have struggled to get really big and more importantly to solve the three key issues (real money, easy to use, easy to create own markets).
Metaculus is a community dedicated to making accurate predictions (they have a great resource page) as is Manifold. Neither use real money.
Kalshi is a new startup ($30m of funding) that is trying to make events into an asset class via a real money prediction market. As is Futuur.
Polymarket, the biggest such market in the US, was recently fined and forced to shut down in the US (it remains open elsewhere).
You probably know, AlphaGo, the 2016 AI program that dominated the game of Go.
Soon after, a software implementation called Leela was made available, to train human Go players.
Data from 750k Go moves from 1,200+ players between 2015-2019 shows a significant improvement in move quality – especially among younger players (see chart) who are likely more open to learn from Leela.
Starship, the fully reusable rocket under development by SpaceX, is a revolution the industry grossly under-appreciates. So goes this fascinating blog post.
“Starship matters. It’s not just a really big rocket, like any other rocket on steroids. It’s a continuing and dedicated attempt to achieve the “Holy Grail” of rocketry, a fully and rapidly reusable orbital class rocket that can be mass manufactured. It is intended to enable a conveyor belt logistical capacity to Low Earth Orbit (LEO) comparable to the Berlin Airlift.“
“Consider the two critical metrics: Dollars per tonne ($/T) and tonnes per year (T/year) … Starship is intended to reach numbers as low as $1m/T and 1000 T/year for cargo soft landed on the Moon. Apollo achieved about $2b/T and 2 T/year for cargo soft landed on the Moon.“
It is developing in leaps – “Two years ago Starship was a design concept and a mock up. Today it’s a 95% complete prototype that will soon fly to space and may even make it back in one piece.“
