Entry Level Investing

Entry Level Investing is a Substack focused on exploring the evolution of investing and the impact of AI on businesses, markets, and infrastructure. It covers topics from AI innovation techniques and investment strategies to market trends, offering in-depth analysis of disruptions, opportunities, and the future of AI-driven industries.

AI and Machine Learning Investment Strategies Market Trends and Analysis Startup Ecosystem Tech Industry Disruptions Venture Capital Funding Corporate Governance Public and Private Markets Natural Language Processing Open-Source Business Models

The hottest Substack posts of Entry Level Investing

And their main takeaways

Why are tech companies more sensitive to interest rates?

16 implied HN points • 12 Mar 24
💰 Finance Investing Valuation Tech Companies
  1. Tech companies, especially high-growth but unprofitable ones, are highly impacted by interest rates during investing.
  2. The value of tech companies is largely based on future projections, making them sensitive to discount rates which determine present value.
  3. In a market with expensive capital, tech companies' future profits are heavily discounted, while in a market with cheap capital, their value can increase significantly.

Is 2023 the very best or the very worst time to be investing in AI?

100 implied HN points • 29 Aug 23
🕹 Technology AI Investing Startups Market Analysis Venture Capital
  1. Interest rates impact tech company valuations, especially high growth tech companies sensitive to changes.
  2. VC deal activity and funding have decreased in 2023 due to compressed valuations in public markets.
  3. AI investments are increasing as a bright spot in the market, with investors turning to AI startups amid a grim funding environment.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

The Modern ML Stack is broken, but it won't be for long

184 implied HN points • 20 Feb 23
🕹 Technology AI Data Tech startups
  1. AI infrastructure is essential for organizations to participate in the AI revolution.
  2. The current ML infrastructure landscape is messy, and there is a need for consolidated solutions.
  3. Entrepreneurs have a huge opportunity to build enduring businesses by focusing on end-to-end ML application offerings and addressing the challenges in the AI infrastructure space.

What do funding rounds look like for AI startups?

33 implied HN points • 18 Oct 23
💼 Business Funding Rounds AI Startups Venture Capital Framework
  1. Every subcategory of Company has its own KPIs and benchmarks for venture funding rounds.
  2. Application Layer AI companies are assessed similarly to traditional Enterprise SaaS solutions but with some leeway on revenue targets.
  3. Tooling Layer businesses tend to fundraise one stage behind Application Layer businesses due to higher capital investment and time requirements.

Why do startups have Boards of Directors?

16 implied HN points • 30 Nov 23
💼 Business Startups Boards Governance Investors Management
  1. Public companies have boards to protect shareholders from decisions made by management at their expense.
  2. Private equity firm boards are an extension of the firm itself, making major decisions about their companies.
  3. Startup boards, even when founder-controlled, provide good governance, lead to better outcomes, and offer valuable insights and support.

Attempting to untangle the messy math behind the "Power Law"

84 implied HN points • 28 Feb 23
💰 Finance Investing Venture Capital Valuation Business Models
  1. The concept of the 'Power Law' in venture funding states that a few successful investments drive fund returns, not a normal distribution.
  2. Startup valuations are often based on revenue multiples, deviating from traditional valuation methods like free cash flow analysis.
  3. Overvaluation, excessive spending, and failure to grow into valuations can lead to down rounds, hurting startups and investors.

When will AI do my job for me?

67 implied HN points • 10 May 23
🕹 Technology AI Automation Predictions
  1. AI is likely to disrupt white collar workers more than the working class for the first time.
  2. AI excels at replacing repetitive, language-heavy tasks, starting with law but expanding to other fields.
  3. Professional service fees may decrease as AI tools become essential, leading to challenges in finding future leaders in those industries.

How are companies ACTUALLY using AI today?

16 implied HN points • 27 Jul 23
🕹 Technology AI Productivity Tools
  1. AI is being used in productivity tools to make tasks more efficient and abstract away repetitive work.
  2. Creativity tools help in inspiring new creations and reducing friction in creative processes for artists.
  3. Embedded AI enhances existing software applications by integrating AI-powered insights directly.

Are we in the early innings of an AI M&A spree?

16 implied HN points • 13 Jul 23
💼 Business AI M&A Technology Acquisitions
  1. Recent high-profile AI acquisitions suggest a growing trend in M&A activity in the AI space.
  2. Large horizontal technology platforms are likely to be key players in acquiring AI startups due to their access to cash and existing distribution advantages.
  3. Companies like Apple, Amazon, Salesforce, Oracle, Adobe, and Snowflake are potential contenders for making significant AI acquisitions in the coming months.

Open-source is not a panacea

16 implied HN points • 29 Jun 23
🕹 Technology AI Open Source Monetization Ethics Models
  1. Open-source AI is gaining momentum and innovation, but it's not a complete solution.
  2. There are ethical concerns with open-source AI models, including safety risks and data security.
  3. Challenges exist in monetizing open-source model businesses and navigating copyright licenses.

Talking Human: The Rise of Natural Language Processing (NLP)

16 implied HN points • 29 Jan 23
  1. Advances in Natural Language Processing (NLP) are essential for better understanding customers and improving interactions.
  2. NLP allows us to communicate with computers in a human-like way, opening new possibilities for virtual assistants, chatbots, and more.
  3. Key trends driving demand for NLP include the digitization of social interactions, remote customer engagements, rising global labor costs, and the need to leverage data for better products.