One possible explanation: business owners have more skin in the game and care the most, so they are the most demanding and can’t tolerate waste. So they are the hardest to satisfy.
Additionally, they are not used to mincing their words because they don’t have bosses and are the most direct (and also egoistic).
According to NYT it seems like there were 2 controllers and “2 more in the building”. They also wrote that 2 seems normal for the late slower time of the night.
Not saying this is the right number of controllers to have, just sharing what I read in NYT.
I know about 2 big discontinuities in group dynamics, which are based on the limits of human cognition at specific sizes:
1) ~7 people. This is when each member cannot participate in the whole context and all important decisions. "2 pizza teams", limits of the working memory. Decisions cannot be done all together anymore. This results in hierarchies forming and people worrying about their positions in them.
2) ~150 people (Dunbar's number). This is when group members cannot all know each other anymore and have meaningful relationships. Max sizes of family-based tribes, an important unit size in the army. This results in inability to observe each other's actions well, so understanding who contributes a lot vs not shifts to indirect stories. Group members start building narratives instead of demonstrating contributions directly.
Beyond the common narrative on this topic, a factor to consider is that people might be more interested in hearing about death causes, which are not considered their own “fault”. These situations are less “fair”. Thus terrorism, homicide and accidents get a big focus.
Why do you think Glean and other top company GPT startups who are doing this for longer and have more resources cannot get this level of performance (helpful and accurate)? What makes your approach different and not easy to replicate?
How do you verify accuracy of contributions? What stops somebody from submitting fake (overpriced) numbers for companies they don’t like (competitors)?
Unfortunately, the type of people who take personal responsibility for failures in organizations don’t end up climbing corporate ladders high enough to be considered for CEO positions. That’s just how large human groups work where contributions of each individual cannot be directly observed by everybody. Storytelling and making oneself look better start playing bigger role for promotions than actual results. Jeffrey Pfeffer writes in depth about it in his books about power.
It took the world decades to develop widely accepted standards for working with relational data and SQL. I believe we are at the early stages of doing the same with event data and sequence analytics. It is starting to simultaneously emerge in many different fields:
- eng observability (traces at Datadog, Sumologic, etc)
- operational research (process mining at Celonis)
- product analytics (funnels at Amplitude, Mixpanel)
As with every new field, there are a lot of different and overlapping terms being suggested and explored at the same time.
We are trying to contribute to the field with a deep fundamental approach at Motif Analytics, including a purpose-built set of core sequence operations, rich flow visualizations, a pattern matching query engine, and foundational AI models on event sequences [1].
Fun fact: creators of Scuba turned it into a startup Interana (acquired by Twitter), who we took a lot of inspiration from for Motif's query engine.
There are very interesting improvements to SQL, which are much more ergonomic, extend functionality, and provide higher-level abstractions. Also backward compatible. PRQL and Malloy immediately come to mind but there are more. Anybody has good explanations why they struggle to get wide adoption?
We are extending LLMs from working on sequences of words to sequences of event data to help with various product analytics tasks, including the holy grail of finding levers for product optimization. Can be especially useful for growth and operations teams.
Highly recommend Jeffrey Pfeffer’s books about power and leadership. He has developed a great mental model about how leadership works in the real world, which explains the pitfalls of common ways to think about it.
Humans want to believe in a just world and those in power are happy to provide narratives to that end. Meanwhile they are not correlated with how power is actually gained in the real world.
Additionally, they are not used to mincing their words because they don’t have bosses and are the most direct (and also egoistic).