Equity, Inequity, and Machine Learning

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The document discusses equity, inequity, and how machine learning could be used for compensation analysis. It provides information on equity basics like common stock, preferred stock, and options. It then discusses a study analyzing equity data from thousands of companies which found wealth inequality is driven more by differences in ownership rather than salary. It suggests democratizing ownership could help reduce inequality. Finally, it discusses how machine learning could be used in the future for tasks like determining market equity offers, data-driven company valuations, and analyzing startup network effects.

Technology

Equity, Inequity, & Machine Learning
Jerry Talton
Director of Data & ML

● Equity is a stock or security representing an ownership interest
● Equity usually vests over time (e.g., four years with a one year cliff)
● Liquidity events necessary to convert privately-held equity to $$$ (IPOs,
M&As, tender offers)
● Tax implications of equity vary wildly depending on type of security and
length of ownership (ordinary income, long-term capital gains, AMT)
Equity 101
https://carta.com/blog/equity-101-equity-basics-for-founders/

● Founders awarded common stock; investors buy preferred stock; early
employees get options to buy common stock; later employees get RSUs
● Preferred stock may have liquidation preferences, antidilution provisions,
observation rights, board voting rights, etc.
● Options have a strike price, a fair market value (or 409A valuation), and a
post-termination exercise period (historically 90 days)
Equity 101

“Come for the tool,
stay for the network.”
@cdixon

AverageInitialShares
AverageInitialValue
Initial Equity Grant by Employee Number
1 150 300
(Equity data anonymized & aggregated)

Equity Events as a function of Job Tenure
(Equity data anonymized & aggregated)

Average Initial-to-Total Equity Ratio by Employee Number
(Equity data anonymized & aggregated)

Post-Termination Exercise Rate by FMV-to-Strike Price Ratio
(Equity data anonymized & aggregated)

The Gap Table
● Joint work with #ANGELS investment collective
● 6k companies; 15k founders; 180k employees; $45bn in equity
● Excluded companies whose ToS exempt them from aggregate analysis
● Analyzed options, RSUs, and common stock
● Used 409A FMV and strike price to calculate option value
● Binarized* names to male & female using US Social Security data
● 13.6% of shareholders removed due to ambiguity (<85% gender associated)
https://carta.com/blog/gap-table/

Equity is the most valuable component of
startup compensation

Equity is the most valuable component of
startup compensation
Wealth inequality is driven more by
ownership than by salary

Equity is the most valuable component of
startup compensation
Wealth inequality is driven more by
ownership than by salary
Democratizing ownership is key to
reducing inequality

The Future
Compensation Analysis
What’s a market equity offer?
Data-Driven Valuations
How should we price our round?
Startup Network Effects
Who drives the most value?

Life is hard.
If you need help, ask for it.

Thanks!
Follow me: @jerrytalton
We’re hiring: carta.com/jobs

Overview of Quantopian: where we are and where we are headed. Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024 https://alandix.com/academic/papers/synergy2024-epistemic/ As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.

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Epistemic Interaction - tuning interfaces to provide information for AI support

Equity, Inequity, and Machine Learning

1. Equity, Inequity, & Machine Learning Jerry Talton Director of Data & ML

2. Equity

3. ● Equity is a stock or security representing an ownership interest ● Equity usually vests over time (e.g., four years with a one year cliff) ● Liquidity events necessary to convert privately-held equity to $$$ (IPOs, M&As, tender offers) ● Tax implications of equity vary wildly depending on type of security and length of ownership (ordinary income, long-term capital gains, AMT) Equity 101 https://carta.com/blog/equity-101-equity-basics-for-founders/

4. ● Founders awarded common stock; investors buy preferred stock; early employees get options to buy common stock; later employees get RSUs ● Preferred stock may have liquidation preferences, antidilution provisions, observation rights, board voting rights, etc. ● Options have a strike price, a fair market value (or 409A valuation), and a post-termination exercise period (historically 90 days) Equity 101

5. Relationship status:

6. Relationship status: it’s complicated

7. What is Carta?

8. We issue electronic securities

9. We track the cap table

10. We track the portfolio

11.

12.

13.

14.

15. The Ownership Graph

16. “Come for the tool, stay for the network.” @cdixon

17. AverageInitialShares AverageInitialValue Initial Equity Grant by Employee Number 1 150 300 (Equity data anonymized & aggregated)

18. Equity Events as a function of Job Tenure (Equity data anonymized & aggregated)

19. Average Initial-to-Total Equity Ratio by Employee Number (Equity data anonymized & aggregated)

20. Post-Termination Exercise Rate by FMV-to-Strike Price Ratio (Equity data anonymized & aggregated)

21. Inequity

22. The Gap Table ● Joint work with #ANGELS investment collective ● 6k companies; 15k founders; 180k employees; $45bn in equity ● Excluded companies whose ToS exempt them from aggregate analysis ● Analyzed options, RSUs, and common stock ● Used 409A FMV and strike price to calculate option value ● Binarized* names to male & female using US Social Security data ● 13.6% of shareholders removed due to ambiguity (<85% gender associated) https://carta.com/blog/gap-table/

23.

24.

25.

26.

27. Equity is the most valuable component of startup compensation

28. Equity is the most valuable component of startup compensation Wealth inequality is driven more by ownership than by salary

29. Equity is the most valuable component of startup compensation Wealth inequality is driven more by ownership than by salary Democratizing ownership is key to reducing inequality

30. Machine Learning

31. The Future Compensation Analysis What’s a market equity offer? Data-Driven Valuations How should we price our round? Startup Network Effects Who drives the most value?

32. And one more thing…

33. Life is hard. If you need help, ask for it.

34. Life is hard. If you need help, ask for it.

35. Thanks! Follow me: @jerrytalton We’re hiring: carta.com/jobs

Equity, Inequity, and Machine Learning

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