Thinking, Fast and Slow | Revisited
- perrydouglas9
- Apr 7
- 10 min read
Updated: Apr 8
The ai-Socratic Conversational Method for VC Investing
Perry C. Douglas
April 7 2025
Recently, I participated in a software development session with my team, and our discussions reminded me of the New York Times bestseller Thinking, Fast and Slow by renowned psychologist and Nobel Prize winner in Economics, Daniel Kahneman. The book has been described as “A masterpiece…This is one of the greatest and most engaging collections of insights into the human mind I have read,” says William Easterly of the Financial Times. This book is essential for anyone seeking genuine knowledge and wishing to adhere to the scientific method in decisioning. “Thinking, Fast and Slow“ seamlessly combines knowledge with wisdom, applying intelligence in the most human-centric way.
It’s lucid and profound with intellectual depth, yet accessible and insightful to anyone willing to invest the time and effort. “A tour de force of psychological insights,” remarks Christopher F. Chabris of The New York Times.
I had forgotten that I read this book long ago, so I went back to my bookshelf and started rereading part one once again. It explains the two systems that drive the way we think and make our judgments.
System 1 (S1) is fast, intuitive, and emotional
System 2 (S2) is slower, deliberative and logical.
The two systems are effectively a choice that we make about how we want to think. It also highlights the significant limitations of quick thinking and the importance of consciousness in critical thinking and making effective strategic decisions.
The book was written in 2011, but its basis is extremely relevant to the AI age. I quickly discovered that the two systems are comparable to LLMs/ChatGPT / (S1) vs. the six steps to applied intelligence (6ai) / (S2).
S1 can be associated with quick, narrow, and summarized responses due to its training on large amounts of data. The system merely aims to demonstrate how smart it is. However, these S1, LLM-based type systems do not delve deeply and cannot process meaning in context to the real world, leading to unreliability and a tendency for infinite hallucinations.
S2 is associated with moving slower with more thoughtful engagement and employing the Socratic Conversational method of questioning. Geared more toward learning first and acquiring more clarifying and insight-generating information before seeking to answer.
Kahneman describes the human executive decisioning process as heuristic, and LLMs play into that. Human nature seeks out the quick and easy, the "laziness factor in human nature," he says. Given the option between S1 decisioning and S2, most will pick S1 because it’s less work, without consideration for the quality of the outputs.
“Judgments and decisions are guided directly by feelings of liking and disliking, with little deliberation or reasoning. Laziness is built deep into our nature,” he explains.
By going with Fast (SI/LLMs), we are not learning or getting better at our endeavours or tasks. We unknowingly harm our intelligence capacity and capabilities for ingenuity, creativity and innovation.
If we don’t recognize this and continue with quick and easy choices, as the saying goes, easy come, easy go! Understanding the limitations of any piece of technology is critical for proper task-specific utilization. Otherwise, we misuse it at our own peril.
Furthermore, if we are not consciously aware of and regulate our human condition — our emotional human nature — we become irrationally confident about what we think we know. However, discovering what we don’t know, i.e., discovering the Black Swan, is where and how value is created. S1/ai is conducive to that value-creating process.
Part II — The ai-Socratic Conversational (SCai) Method
SCai facilitates critical thinking, analysis and insight generation by doing a deeper dive into the user’s problem. It is a process of empathetic conversational probing, clarifying, and information acquisition, which has been proven more effective in extracting more meaningful and precise responses.
Challenging assumptions and belief systems generate more logical thoughts and lead to better-constructed questions, focused and drilling down on what the user wants to achieve. Encouraging multiple perspectives, finding the gaps in logic, and looking at the underlying assumptions behind the idea. How does this idea fit within the broader context of the said industry or domain and build the necessary strategies to maximize the idea’s value potential?
SCai investigates potential opportunities and risks by exploring the whitespaces of market opportunity.
SCai is embedded in applied intelligence, the process-IP that guides users through the Q&A process for a higher dimensional level of information retrieval, extracting and processing the most relevant and useful insights for strategy development and planning.
Accordingly, S2/6ai is more aligned for optimal conscious contextualization decisioning…constructing a series of steps to arrive at coherent, warrantable outputs instead of being peppered with quick, shallow, and repetitive responses. Which leads to unreliability and gives the user more fact-checking work to do. What’s the point of that?
The S1/ChatGPT way can be impressive nonetheless, but ineffective for those who are seeking to achieve more meaningful outcomes. The S1/ChatGPT way tells you nothing.
But do keep in mind that LLM systems were never designed to engage in the more effective Socratic system of questioning and learning.
S2, on the other hand, is human-centric and relies on human agency, multiple agents working within the mind. Similarly, SCai, in the machine environment, engages AI agents effectively for high operational functionality.
AI agents are a self-contained, autonomous entity that carries out specific tasks. SCai embeds multiple agents, all working at the same time, carrying out multiple tasks. Providing an integrated and complete set of capabilities rather than disembodied capabilities. The agent’s ability to communicate, cooperate, and negotiate with other agents makes them highly useful and practical and more in sync with how problems are sorted and solved with authentic human intelligence in the real world.
6ai is more aligned with how success happens because true success requires a willingness to put in effort and real work. The superficial ChatGPT types, in their present form at least, can’t hold a candle to S1/6ai types. So don’t believe the hype about LLM-based systems!
6ai is for serious people who want to make real impacts in the world and are willing to respect the process to get them there. Nothing has fundamentally changed about achieving success, regardless of the noise about AI.
Part III — 6ai-Investor
Why Every Serious Investor Needs 6ai-Investor Software
The importance of “Thinking, Slow and Fast” can’t be underestimated, particularly with venture capital investing.
For more than a decade, private markets have enjoyed a remarkable period of sustained growth, more than doubling from US$9.7 trillion in 2012 to an estimated $24.4 trillion by the end of 2023.
Market growth has been driven by an increase in high-net-worth individuals, intergenerational wealth transfers, and the democratization and accessibility to information technology tools empowering a new generation of do-it-yourself investors.
These investors are not intimidated by fund managers or investment professionals; they seek knowledge, and successful upstarts like WealthSimple demonstrate that there are options for individual investors. WealthSimple has been particularly good at showing the financial benefits of cutting out middle-men fund managers and advisors in the public markets domain.
In an April 1st, 2025, marketwatch.com article, BlackRock CEO Larry Fink said that the private capital marketplace continues to accelerate with investors finding their own way. But also “highlighted the challenge investors face when trying to [do it themselves].”
This suggests that a significant market opportunity and fit exists, for companies that can fill that gap by offering similar do-it-yourself investing solutions in the private capital space.
VC fund manager capital, however, acts as a conformer to the ideas of the more powerful partners or key decision-makers in the organization’s management ecosystem. Money usually chases the hottest tech trends, which plays to Big Tech, i.e., generative AI, instead of the pursuing of the best business idea. Almost always, VCs (who are usually analysts) think they know better than the founder and just mess things up. Often stifling authentic entrepreneurship and engaging in short-term thinking while building phony valuations...setting themselves up for the nearest exit.
Professor Julia Ott, Associate Professor in the History of Capitalism, and co-director of the Robert L. Heilbroner Center for Capitalism Studies at The New School for Social Research. Professor Ott’s work casts a shade of skepticism about the value that Venture Capital (VC) firms bring to startups, saying that many VCs do not add value, with many harming entrepreneurs.
Others have expressed similar sentiments, including Vinod Khosla, a well-known (co-founder of Sun Microsystems) venture capitalist, stating “that 90% of VCs add no value to startups and 70% even harm them.”
The big question: why would individual investors continue to waste time, pay high fees, give up liquidity and accept poor returns when technology can provide a better alternative?
The chart above depicts the typical investment journey for entrepreneurs and the investment decisioning phases involved for investors. Different periods require different investment evaluation processes, skills, multi-criteria characteristics analysis.
The purpose of startup valuations is to provide insight into a company’s ability to use new capital to grow, meet customer and investor expectations, and achieve the next milestone. Proper evaluations must check off additional factors, including team expertise, product fit and new demand creation, sales, marketing and distribution, assets, business model, total addressable market, competitor performance, market opportunity, goodwill, and future probability analysis against marketplace realities.
This is a lot to think about and employ. For serious investors, intelligent analytical, selection and strategy tools would be extremely helpful.
For example, Sequoia Capital’s 50-year study of entrepreneur success says that “founder’s strengths have proven to be the most reliable factor for determining a startup’s long-term success.”
And here is another study…further confirming Sequoia’s findings:
“The study found a significant relationship between venture growth and entrepreneurs with high personal initiative, focused on specific competency. The common research areas cited in the literature are such as entrepreneurs’ leadership, entrepreneurial orientation, management skills, competencies, human capital, personality traits and circle of network. Qualities associated with a high need for achievement contribute to the success of a new venture.”
Therefore, multiple and important characteristics must be examined in context and priority, coupled with the overall dynamic and the multi-variable nature of tech startup investing. Make it challenging for both investors and entrepreneurs alike. Making things challenging for both investors and entrepreneurs. Not taking advantage of relevant software tools would be nonsensical.
The overriding philosophical approach of the 6ai-Investor (6ai-I) solution is rooted in Daniel Kahneman’s System 2 way of thinking and making judgments. Coupled with the Socratic method of inquiry...the conversational questioning approach popularized by the ancient Greek philosopher Socrates. Creates a relevant to the times, powerful investor solution.
Increasingly, however, venture capital is being structured into more managed products. More funds and funds-of-funds with slick marketing for early-stage financing of companies. Creating another industry just for professionals to earn more lucrative fees.
VC fund investing sells ‘diversified’ investing to ‘manage risk’, effectively being run like any old mutual fund. However, 95% of fund managers don’t beat the averages/indexes, but unit holders continue to pay high fees for poor performance.
In a Harvard Business Review article by Diane Mulcahy, titled Six Myths About Venture Capitalists, based on her comprehensive analysis of more than 20 years of experience investing in nearly 100 VC funds. Her research advises aspiring entrepreneurs against falling victim to common myths about venture capitalist and their funds.
She warns that VCs are not the primary source of start-up funding; “Actually, angel investors fund 16 times as many companies than VCs.” It’s also a myth “that VCs take big risks with start-ups, (they’re insulated against risk by hefty annual fee streams.)” Another myth that “Most VCs offer great advice and mentoring, and that they generate spectacular returns, is simply false.”
Mulcahy goes on to say, that “since 1997 less cash has been returned to VC investors than they have invested.” So she advises individual investors to stay away from VC funds, especially the bigger ones: “Bigger is not better (Research shows that fund performance declines as fund size increases.)”
Let’s do some quick math for fun. Pretend you’re a VC fund manager with a 500 million dollar venture fund, you invest $10 million for 10% of a company. It sells, and you exit at $300 million. That sounds great, but how much does the fund actually make? At 10%, that’s $30 million, but you have a $500 million fund…you haven’t even made 10% back.
Your goal as a manager is to do at least 3x…$1.5 billion. Also, your fund is illiquid with a 5 to 10-year holding period. What’s the point for the investor? Investors can buy the Nasdaq instead.
<20% of VC firms account for most industry returns, with an even smaller subset of partners (<10%) driving those results. And those funds you can’t get into, you’re stuck selecting from the dreck funds. So the odds are stacked against you when investing in VC funds.
VC funds can be highly misleading because they typically reflect only those firms that have initial public offerings or are acquired by another company. Those that do not achieve a good return are more likely to stay private or go bankrupt.
VC Adventure published that 65% of investment rounds fail to return 1x capital, and only 4% return greater than 10x capital. The difference between the best-performing and average-performing funds is incredibly wide. The typical venture fund of almost any size, having somewhere between 20–40 investments, the chances of any one of them being a true outlier and returning a strong multiple is slim.
Considering the above explanation and looking at the chart below, why would any rational-thinking investor invest in VC funds? For an average 10-year return of 15.1% and a 5-year return of 17.1% when the Nasdaq does better and other indices close behind? Why would you give up liquidity and take increased risk for that?
“Venture capital (VC) investments carry more risk than most investments in the broad public market and their returns are much more modest than commonly thought, according to a new paper by NBER Research Associate John Cochrane. He concludes that VC investments are not dramatically different from publicly listed small-growth stocks.”
There is a better, more practical and commonsense way: the 6ai-Investor solution offers investors a single piece of software that anyone can use to do their own analysis, the SCai way, developing their insights and building their own investing strategies.
So, instead of paying high VC management and other convoluted fees to analysts turned fund managers, 6ai-I will help you do it yourself, and better!
If one can align their thinking to System 2 coupled with the six-step ai process, sidestepping the AI hype, and be willing to put in a little effort to build their capacity, they’ll soon realize how easy the right technology tools make it to succeed at VC investing.
6ai-I - Do better by doing it yourself!
6ai www.6aitech.com: Empowering Strategy for Human Progress.
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