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Great post!

Explains the pain area with incumbent IAM systems and proposed Identity Fabric solution very succinctly. 

IMO, Invention is a new thing. Innovation is a new way of doing an existing thing that amplifies impact through broader reach. By that token, Google Glass was an Invention. But, since it didn't enjoy broad reach, it was not an Innovation.

Invention (per my definition) / Innovation (per your definition) is going outside the box. Regulation / Compliance (per general understanding) is staying inside the box. Regulation-compliant invention / innovation is an oxymoron (at the point of occurence of invention / innovation).

Some of the greatest business value has been created when startups did invention / innovation that leveraged regulatory gap - if not outright broke prevailing laws / rules - at their inception, gained scale, became Too Big To Be Shut Down, and got regulators to modify regulation ex post facto to fit the new capabilities enabled by their invention / innovation e.g. AirBnB, Uber, UPI. Which is not saying anything negative about regulators - Regulators are not Entrepreneurs. 

Yet another Spy v. Spy use case in genAI! (H/T MAD magazine)

IME, as I highlighted in my blog post Hiding Your Secret Sauce, when somebody volunteers a secret sauce, the real secret sauce lies somewhere else! 

I've been asked many times about the difference between stored procedure, CRON job, script, and RPA on the one side and AI Agent on the other. No matter how much spiel I give about genAI, some people simply refuse to believe that Agentic AI is the next level in automation technologies. Going forward, I'll describe the above incident: No stored procedure, CRON job, script or RPA has even been tasked to run a vending machine business - or bungled it as badly as Claude AI Agent!

Maybe n00bs in the payments industry won't know this but, for 20+ years:

(1) Traditional payment fraud detection and prevention systems do not always rely on after-the-fact analysis. They include algorithms like "Velocity Check", which act on individual payment transactions before the fact. To give an example, a coworker swiped his credit card at Denver airport, then boarded a flight, landed at Dulles airport, when he tried swiping his credit card, it was rejected because "how can anyone travel 2000 miles in 3 hours?" Obviously, this was a false-positive but it does illustrate the availability of realtime features in trad FD&P systems.

(2) Recommending the best payment method for the individual has been the staple of many traditional payment apps. For example, given a merchant, which is the best credit card to use depending on the highest rewards for that merchant spend category. 

(3) PSPs have provided "Intelligent Payment Routing" based on availability, least cost, etc. 

If these are all the features that AI has up its sleeve, I'm not very gung-ho about its value add for payments. 

Great post. Totally resonates with what I wrote in section titled 5. Makes GIGO Obsolete of my blog post Ten Revolutionary Things About AI - Part 1. That said, Agentic AI takes autonomous decisions based on data, so the stakes of bad quality data are arguably higher in the case of AI projects compared to previous technologies like ERP, CRM, etc. 

Nice post. 

I specify the criteria to pick stocks to ChatGPT but, after I do that, ChatGPT does its own thing including access whatever sources it accesses. Given that workflow, keen to know I can "Be sure your sources are compliant and transparent". 

Why give reward points in altcoin and jump through the hoops of having it listed on a cryptoexchange when you can give them directly in Bitcoin. But I wonder if it has anything to do with Coinbase itself being a cryptoexchange and whether other brands will be able to do the same. 

Adding to my post Blockchain Loyalty Programs - Sizzling Or Fizzling?

Nice post. 

Based on my firsthand experience of pitching outcome-based pricing model off-and-on for 30+ years across a wide variety of software and in multiple geographies, my conspiracy theory is that:

Customers want outcome-based model but only until they get it.

More details can be found in the following posts on my company blog:

1. Output Versus Outcome Based Model
2. Three Truths About Outcome Based Model

(hyperlink to these posts removed to comply with Finextra Community Rules but they should appear on top of Google Search results when searched by their respective title + "GTM360")

I'm guessing this is the root cause for your "What about this? You can't count that. Etc." experience.