Technical Analysis

Prediction markets are gaining popularity by allowing traders to speculate on specific events through binary outcome contracts. While they offer unique ways to express market views and hedge risks, they also involve high speculation, regulatory uncertainty, liquidity concerns and significant forecasting risks. 

Read More about Navigating the Rise of Prediction Markets and how event contracts, regulation, speculation and risk management are shaping the future of prediction-based investing. 

------------------------------
Megha Kumari
------------------------------

My responses to the questions asked are: 

Q#1 Have you engaged in prediction markets? 

No.

Q#2 If so, how do you apply prediction markets in practice to evaluate economic trends or hedge investment risks?

I do not seek out prediction market data or statistics.

However, I can see how these statistics might be applied to investment decisions. 

Q#3 What has worked for you in managing speculation, liquidity challenges, and regulatory uncertainty while using event contracts?

See response to Q#2 

Q#4 In your opinion, do prediction markets have value for judging economic trends or hedging investment risks? Why or why not?

See response to Q#3.

=====================================================================================

On a more serious and related subject ... I recently read an article from the University of Chicago Booth School of Business entitled,  "How Many Questions Can the World Afford to Ask AI?"

The main premise of this research is ...

How many (frivolous) questions (like prediction market bets) can be directed at AI systems, ...

given the planet's limited physical resources (copper and electricity production being examples), constraints, ... 

and the fact that the use of AI agents is a function of a market where the economic costs and benefits are not a consideration in allocating these resources.

It references more than a dozen famous scientific sources and principles to support their analysis and conclusions.

=====================================================================================

One interesting experiment (to me) was

An experimenter asked an AI agent to try to score the most points in a contest.

The AI agent chose a strategy to eliminate all other competitors and win.

=====================================================================================

If you are interested in reading the article, the link is  How Many Questions Can the World Afford to Ask AI?

Regards

------------------------------
BARRY JOHNSON
------------------------------

Thank you for the article, Barry. I'm pretty sure the free market will end up taking care of any problems associated with too many questions being asked of AI. Eventually, people will be charged for access to the various AI models. Right now, it's pretty much free, but I view that as a loss leader, just like many internet sites were before they started charging for access or for "premium" services.

Dr Bob.

Thanks for your insights and estimates. I agree.

Even though there are numerous examples of populations and countries that have depleted the indigenous scarce resources that were capable of providing for future growth and prosperity [petroleum to provide energy and power, and rare earth minerals to provide semiconductor fabrication] dominating current markets, I, too, have faith in free markets where prices eventually achieve equilibria that balance resources with consumption.

Since the first Chatbot in 11/25, subscriptions have raced up from "free trials" to other "free" competitive offerings today within 6 months, and prices are increasing rapidly as Generative AI products increase the power, utility, demand, and PRICE. 

This example supports your judgments.

Everett Rogers' Theory of Technological Diffusion (also called Innovation Theory) from the 1950's explains how, why, and at what rate new ideas, products, and new technologies spread through markets.

As Rogers explains, market adoption follows a standard bell curve. Populations can be grouped into 5 distinct social groups based on their psychological willingness to accept change. 

Geoffrey Moore expanded on Rogers' theory in high-tech industries. He identified a structural gap (he called "the chasm") between early adopters (Visionaries) and the early majority (Pragmatists).

Moore argued that many startups fail because they cannot adapt their messaging and product reliability to satisfy pragmatic consumers who demand stable, complete solutions.

Today, I estimate the diffusion rates have progressed into Stage 1 Innovators (2.5%) OR Stage 2 Early Adopters (13.5%). Concurrently, cumulative market share forms an S-shaped curve that flattens out ("the long tail" of decreasing utility) as the market reaches saturation.

There may be some useful insights in this boring history lesson for

(1) choosing a Chatbot, and

(2) making investment decisions when investing in AI competing companies.

Regards

------------------------------
BARRY JOHNSON
------------------------------

Dr Bob

Here I go again.

It's the Morningstar fair value estimate for the SPCX IPO on Friday, June 12. 

I also posted this on the other thread. I wanted to make sure you saw this. I find your comments very informative.

--------------------------------------------------------------------------------------------------------------------

https://www.morningstar.com/stocks/spacex-what-investors-need-know-about-its-enormous-upcoming-ipo#what-are-spacex-shares-worth

Regards