Pivotal | Revenue & Trends

The hottest Substack posts of Pivotal

And their main takeaways

Silicon Valley focuses on 'temporal arbitrage', which is about making money over time by investing at different stages of a startup's growth. This helps investors bridge gaps between early ideas and established companies.
The modern venture capital system divides funding into specific stages, like seed and series rounds. Each investor specializes in different stages, making the process smoother and more efficient.
Success in venture capital often comes from being part of a shared consensus on what makes a company fundable. Investors try to follow trends rather than go against the grain to align with what other investors believe.

The story highlights the importance of adapting to changing market conditions. The team learned they needed to pivot their strategies quickly to stay ahead in trading.
Building successful trading systems requires not just good math, but also understanding market behaviors and managing risks effectively. The approach taken focused on quickly executing trades to capitalize on market noise.
Competition plays a big role in financial markets. As more players adopted similar strategies, the initial advantages of their system decreased, emphasizing the need to continuously innovate.

Strong opinions, weakly held is a powerful decision-making approach under incomplete information.
Commit fully to your chosen path, but be open to changing direction if needed.
Maximizing rate of learning is key - move quickly with strong opinions and adjust when necessary with weakly held beliefs.

Startup experimentation is about learning from clear successes or failures, not mediocre results.
The danger of a mediocre success is the lack of clarity on whether a strategy can be scaled or needs to be changed.
It's important to design experiments with well-defined outcomes to avoid the messy middle of mediocre success.

Data has no inherent value; its worth depends on how it's used and by whom. Different users will find different values in the same dataset.
The pricing of data isn't straightforward; it involves understanding the uniqueness and quality of the data, its lifecycle, and the specific needs of users.
As AI continues to grow, the demand for data is changing, with more emphasis on quantity over quality. This shift makes understanding how to price data assets increasingly important.

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Data and compute values have changed, affecting software and data business models.
The data explosion in the decade led to new successful business models downstream.
AI impact on data and compute leads to increased data value and the need for new tools and ecosystem in the AI-first world.