This Week in Venture Capital and Artificial Intelligence

Every week I will share and summarize 3-5 ideas and insights with the LinkedIn community. Some of the summarizations I may have covered during the week, and other things may have just been passing thoughts.

1. Longevity Is Key: The Winning Exit Strategy Might Be Not Having One At All

When deciding on your exit strategy as an entrepreneur, you should think about longevity. Ask questions like, "What do I want my legacy to be long-term?" "How far can I take it before I need to pass the baton?" "Where do we best fit within the market long-term?" "What other opportunities might arise?"

- A Happy Investor Is One Who Does Not Want To Exit -

Let's take for example Amazon. By holding onto Amazon stock through its growth journey, an early-stage VC investor would have realized significant returns, far exceeding the initial investment. This demonstrates the potential long-term benefits of investing in and holding onto shares of a successful and rapidly growing company like Amazon.

Amazon did not have a traditional exit primarily because the company followed a different growth trajectory compared to many other startups. Aside from its early-on IPO where the company provided early liquidity to investors, here are the some of the key reasons why Amazon did not have a traditional venture capital or private equity exit:

1. Strong Growth and Market Leadership Amazon quickly became a market leader in e-commerce and expanded into cloud computing (AWS), digital streaming, and AI. This growth made remaining independent and public more attractive.
2. Jeff Bezos’s Vision Bezos focused on long-term growth over short-term profitability, reinvesting profits into the company, aligning with remaining an independent public entity.
3. Ownership and Control Bezos maintained significant control over Amazon, steering the company without needing to sell or seek a buyout for liquidity.

In summary, Amazon's strategic decisions, strong growth, access to capital markets, and Jeff Bezos's long-term vision led to it not having a traditional VC or PE exit.

Why would I want to leave something that just KEEPS on benefitting me and making me money

Another great example is Aaron Judge where in April 2022, Aaron Judge of the New York Yankees turned down a $213.5 million contract extension. The contract extension that Aaron Judge turned down from the New York Yankees in April 2022 was a seven-year deal worth $213.5 million. Later, after a historic 2022 season, Judge signed a much larger deal with the Yankees for nine years and $360 million in December 2022.

These examples highlight the calculated risks some athletes and investors take in betting on performance and market value, often leading to significantly larger financial rewards and in the end, producing more intangible benefits for the market and stakeholders.

2. The Best Advice Is Usually Your Own

When I look at a founder, I look at who they're listening to. They should be listening to their self. This tells me a lot about their confidence and conviction.

It also tells me how they are running the company and if they are running a money-driven company, or mission-driven company.

Many times investors are giving you advice based on their own motivations, which many times unfortunately, is money motivated and misaligned.

This is why I care more about who and how their core founding team and board is comprised. These are the people who should be mission driving the company forward.

If they are taking advice mainly from the investors and venture capitalists, to me, that means it is highly likely the company is money-driven. This is not a good sign. This does not mean you cannot get great advice from your investors and venture capitalists, but it should not be your main source.

Investors are not the ones running your company and while they may understand what they are investing in, they may lack direct operational experience in what you are doing. This is why cherry-picking advice is the best thing you could do.

3. Question of the Day: What Is Explainable AI (XAI)

When we talk about Explainable AI (XAI), many attempt to explain it in solely its technical and functional definitions.

I think we need to explain deeper. We need to know the who, what, when, where, why and how of XAI.

1.) Who is building it?

2.) What are the cause and effects and what are we building with AI?

3.) When is it being built?

4.) Where is it being built?

5.) Why is it being built?

6.) How is it being built?

Kate Crawford 's book "The Atlas of AI" which I have read, deep dives into many of the much more intricate aspects of artificial intelligence, going beyond just technical and functional definitions. Crawford emphasizes the broader socio-political, economic, and environmental contexts of AI. Here's how her book addresses the comprehensive understanding of Explainable AI (XAI) through the lens of who, what, when, where, why, and how:

1.) Who is building it?

Crawford examines the individuals and organizations behind AI development and shaping its existence, highlighting the concentration of power within a few major tech companies and elite academic institutions. She discusses the implications of this concentration for bias, accountability, and diversity in AI systems.

2.) What are the cause and effects, and what are we building with AI?

"The Atlas of AI" explores the intended and unintended consequences of AI systems. Crawford addresses how AI can reinforce existing inequalities and create new forms of exploitation and control. She discusses the wide-ranging applications of AI, from surveillance and law enforcement to labor and healthcare, and the societal impacts of these technologies.

3.)When is it being built?

Crawford contextualizes the timeline of AI development, tracing its roots back to earlier computational theories and technological advancements. She discusses the rapid acceleration of AI research and deployment in the 21st century, driven by advances in computing power, data availability, and investment.

4.)Where is it being built?

The book highlights the geographical and infrastructural aspects of AI development. Crawford points out that AI systems are often built in tech hubs like Silicon Valley, but their material foundations are global, involving rare earth mining, global supply chains, and labor from across the world. She emphasizes the environmental and human costs of these processes.

5.)Why is it being built?

Crawford critically examines the motivations behind AI development, questioning the narratives of progress and innovation that often accompany AI discourse. She argues that profit motives, state power, and the pursuit of efficiency and control drive much of the AI development, rather than purely altruistic goals of solving societal problems.

6.)How is it being built?

Crawford details the technical and material processes involved in building AI systems, from data collection and algorithm design to the physical infrastructure of data centers and the labor conditions of workers in the tech industry. She also explores the ethical and philosophical considerations involved in creating AI systems, advocating for more transparency and accountability in these processes.

Conclusion

"The Atlas of AI" by Kate Crawford offers a critical, comprehensive analysis of AI that aligns with the deeper explanations sought for Explainable AI (XAI). By addressing the who, what, when, where, why, and how of AI development, Crawford's work provides a nuanced understanding of the complexities and implications of AI technologies beyond their technical aspects.

4. Regulating The Incapabilities of AI

The challenge with regulating AI lies in addressing our overreliance on it, especially in areas where it lacks sufficient capability.

In the 1920s, the average car's top speed was about 40 miles per hour. In contrast, today's average highway speeds in America are around 75 mph, with some minimum speed limits set at 45 mph. This illustrates that imposing regulations on machines that lack the capability to meet those standards is impractical.

AI is not good at preempting human propensities and shifting motivations. I believe AI should primarily be implemented and used in situations that are not contextual. Non-context situations are static environments or scenarios without much change. These could be infrastructure, retail management, quality control, inventory management etc.

Despite its capabilities, humans need to be humble and realize that AI has limitations, particularly in understanding and predicting human behaviors, cultural nuances, human motivations in dynamic and rapidly changing and complex contexts. AI's application is most effective in environments that are relatively static and where tasks can be clearly defined and standardized. For dynamic and highly contextual scenarios, human intelligence and judgment remain irreplaceable.

Overreliance on AI can lead to poor decision-making, especially in areas requiring empathy and ethical judgment. Recognizing these limitations encourages responsible and ethical AI use, promotes continuous improvement, and highlights the importance of human-AI collaboration for balanced and effective outcomes.

5. Living On The Edge: What Comes Next After Gen AI?

The next thing to Gen AI will be innovations in Chip advancement and capability. This is where Edge AI comes into play.

Edge AI: This involves deploying AI algorithms on edge devices like smartphones, IoT devices, and other local hardware rather than relying on centralized cloud computing. Edge AI aims to reduce latency, improve data privacy, and increase processing efficiency.

Edge computing can address challenges in chip advancement by enhancing performance, reducing latency, and improving resource efficiency. By bringing computation and data storage closer to the data source, edge computing reduces latency, which is beneficial for real-time applications like autonomous vehicles and augmented reality. Localized processing at the edge minimizes data transmission, boosting performance, especially in bandwidth-limited scenarios, and specialized hardware in edge devices further optimizes performance without needing the latest chips. The distributed architecture of edge computing offers scalability and flexibility, balancing loads and improving resilience, while dynamic resource allocation optimizes hardware use. Additionally, local processing reduces data transmission costs and enhances energy efficiency, addressing rising costs and environmental impacts. Improved security and privacy are achieved by keeping data closer to its source, reducing breach risks and ensuring compliance with data regulations. Implementing a grid infrastructure with large chip centers amplifies these benefits by acting as regional hubs, balancing high performance with practical chip deployment, and creating collaborative networks. This hybrid approach with traditional cloud resources enhances efficiency and performance. While edge computing may not entirely solve chip advancement issues, it significantly mitigates impacts by optimizing computing resource use, enhancing performance, reducing latency, improving cost efficiency, and providing flexibility and scalability.

6. Rumor Has It

One of the big reasons founder fail is because they operate on rumors. Misinformation, bad advice, and just overall a lack facts all resulting in missed opportunities and poor decisions.

Founders often face significant challenges due to the prevalence of rumors, misinformation, and bad advice, which create an environment where making well-informed decisions is difficult. Rumors can spread quickly through informal networks, causing unnecessary panic or leading to hasty decisions. For example, a rumor about a competitor launching a game-changing product might push a founder to prematurely pivot their business strategy without sufficient evidence. Additionally, the digital age has amplified the spread of misinformation. Founders might receive inaccurate data regarding market trends, customer preferences, or regulatory changes, leading to strategic missteps. Basing a product development roadmap on incorrect consumer insights can result in creating a product that fails to meet actual market needs.

Bad advice from unqualified advisors also poses a significant risk. Founders often receive advice from well-meaning but inexperienced individuals, including friends, family, or mentors. Following such advice can steer the business in the wrong direction, wasting resources and efforts. Conflicting advice from various sources can create confusion and indecision, making it hard to choose the right course of action. For instance, balancing suggestions for rapid scaling with those for slow, sustainable growth can be challenging without a clear understanding of the unique context of the business.

The lack of factual information further complicates decision-making. In many emerging markets or innovative sectors, reliable data is scarce, forcing founders to make decisions with limited factual backing. This can lead to missed opportunities as they may not fully understand market potential or customer needs. Conversely, an overload of data without proper analysis can result in analysis paralysis, where the fear of making the wrong decision leads to inaction.

Poor decisions stemming from misinformation and bad advice can lead to significant consequences. Founders might miss lucrative opportunities or misallocate resources, investing heavily in the wrong areas based on incorrect market data. Frequent missteps can also harm a founder's reputation among investors, customers, and employees, eroding trust and making it harder to attract investment, retain talent, or build a loyal customer base. Navigating this challenging landscape requires a critical approach to information, reliance on verified data, and a network of credible advisors to make well-informed decisions.

I hope you enjoyed this week's newsletter stay tuned for next Saturday's edition.

1. ) Upcoming - Stay tuned for:

-- Show release with Serg Masís on my podcast titled Explainable AI

2. ) Upcoming publication

-- On IdeaScale, a well-known global innovation platform I write for

You can find out more about these by tuning into my LinkedIn profile daily and peaking my CV that is listed below from time to time. Also if you would like to be a guest on my 2 podcasts, Capital Unscripted, or Explainable AI, or if you would like to be a contributor to any of the medias I write for or am partnered with, -- includes AI Accelerator Institute , Idea Scale, or AI news -- then please reach me by inboxing me.

I hope you enjoyed this week's newsletter stay tuned for next Saturday's edition.

How to contact me:

-- Other than LinkedIn, if you want to know more about me or hear more from me you can view my CV here: www.paulclaxton.io

-- You can also schedule a meeting with me here by going to the bottom of my business card and following the instructions. Business card

#aiforgood #ai #artificialintelligence #vc #venturecapital #startups #entrepreneurship #aisecurity #datasecurity #datainsights #aiadvancements #aiinnovation #ainews #airegulation #aiconsumers