A common mistake I see "business experts" making is applying old mental models to new paradigms.
As I explained in yesterday's newsletter, when a new paradigm comes along, it means that the whole assumption underlying the existing model has or is ceasing to be valid.
With that, the old model is slowly, then suddenly, swallowed by the new paradigm, which carries that new underlying assumption.
The interesting thing is that the process, while disruptive, appears almost (at least to the human mind) as a continuum.
In other words, as more and more people find out about generative AI, the more they rely on it, the more we get to the point of completely forgetting that once we used search to find what we were looking for (just like many of us have forgotten about floppy disks and CDs).
Of course, a key point here is that the process doesn't always look the same, and while we can spot a pattern there, this pattern does not follow a deterministic law.
In short, it doesn't have to happen in the same way each time, and in some cases, the new entrant might not be successful in supplanting the incumbent.
We often hear about cases of disruptions (like Netflix vs. Blockbuster, Apple vs. BlackBerry).
However, there are many other cases where the incumbent survives and also thrives in the new era (take Walmart's successful transition to e-commerce or Disney's triumphant - and yet quite expensive - transition to streaming).
In many cases, what comes up at the other end, from the disruptor's side, is to stumble upon industries that the disruption could have never come up with because it was too far behind (not in technical terms), but rather from a business paradigm perspective.
Today's Sponsor: RYSE
 |
Last chance to invest before this company becomes a household name
What if you had the opportunity to invest in the biggest electronics products before they launched into big box retail, would you?
Through retail distribution deals with Best Buy, Ring changed doorbells and Nest changed thermostats. Early investors in these companies earned massive returns, but the opportunity to invest was limited to a select, wealthy few.
The game has changed, and for once investors have the option to invest in a company that’s gearing up for a massive retail rollout.
RYSE is set to debut in 100+ Best Buy stores this month, and you're in luck—you can still invest at only $1.25/share before their name becomes known nationwide.
They have patented the only mass market shade automation device, and their exclusive deal with Best Buy resembles that which led Ring and Nest to their billion-dollar buyouts.
| Learn how you can become a shareholder and earn bonus shares for a limited time |
*Investment Disclaimer: This newsletter is for informational purposes only, not intended as investment advice. Past performance does not predict future results, and all investments carry the risk of loss.
Take the case of Amazon, which wanted to turn into a marketplace, and to do that, it built AWS. Sure, AWS was also a deliberate effort, but the question is, would Amazon have figured that out without a marketplace strategy?
Or take the case of Netflix, which, by turning to streaming, also figured it could offer a different consumption format (binge watching vs. paced releases) and content format (TV Series vs. Movies), than Hollywood?
Going back to generative AI, tools like ChatGPT are such a paradigm shift that we see the emergence of what I like to call the "hindsight paradox" or the paradox for which, in the transition phase, we see the new thing (ChatGPT) as an improvement over the old thing (search).
But this is more out of the necessity of the expert brain to fool itself into believing that there is some familiarity of the new, compared to the old (in the end, we want new stuff, as long as it seems like the old, to fit it to old mental models, in a classic Maslow's Hammer Effect).
This creates an error, especially in the domain of business experts looking at the newly formed paradigm according to an old use case.
For instance, this year, Neeva announced the shutdown of its AI-based search engine; they explained:
The wrong assumption here is that of believing you can take over an industry by simply proposing a new monetization model without changing the underlying business assumption.
Indeed, if you want to propose a new monetization model at scale, you can't just say, "I'll offer you something slightly better, which right now is free, as a subscription."
This, to me, is another issue that comes with expertise, where you assume that the average user might want something they didn't ask for.
Instead, the reason why a tool like ChatGTP has been so successful, also with premium members paying $20/mo, is because it covers a much much wider use case than search.
In addition, I argue that if ChatGPT were to use way more expensive business plans, spanning between $100-$200/mo, many business users would still pay for it.
Therefore, as argued above, the lack of success of Neeva as a consumer-facing search engine wasn't due to the friction users experienced because of "Google's monopoly."
It was due to offering a little better (according to whom?) search product with a better (also here according to whom?) business model.
The expert starts from the assumption that what it's offered is better by default, where the user completely misses why it's so.
Just to close on this, a tool like ChatGPT reported explosive growth, even after creating massive friction, in the initial stage (and still today) as servers could not handle traffic, thus blocking access to the tool for millions of users.
And yet, users kept going back!
Why? Well, because it was just something users found (at least) 10x better, not according to some fixed use cases set by the product developer, but by figuring out all the cool things for which ChatGPT could be used (of which, probably, the OpenAI's team had no idea of).
Of course, Neeva, as a company, is not shutting down; instead, it might be getting acquired by Snowflake to focus on enterprise search.
Consumer-facing businesses are the hardest to build just because you need to figure out something extremely compelling, and in many cases, that's something you could not have thought of!
Indeed - and that's the key point I want to emphasize today - great product people don't offer pre-packaged use cases to users. Instead, they enable users to come up with a use case that other users find compelling!
In short, a use case that can scale at a consumer level is not a designed one (or at least on very rare occasions); it's instead a discovered one!
Thus, only with a solid discovery framework, where you keep an open eye on what users are really doing with your product and whether other users are finding that compelling, you stumble upon a product that can scale...
Recap: In This Issue!
- Applying old mental models to new paradigms is a common mistake made by business experts. New paradigms emerge when the underlying assumptions of existing models become invalid.
- The transition from old to new paradigms appears as a continuum, but it can be disruptive. Disruptive cases include Netflix vs. Blockbuster and Apple vs. BlackBerry, while some incumbents survive and thrive in the new era, like Walmart and Disney. Disruptors often stumble upon industries that the disrupted couldn't foresee due to a different business paradigm.
- Generative AI, such as ChatGPT, represents a paradigm shift that challenges traditional search methods. Yet, the "hindsight paradox" occurs during this transition phase when the new paradigm (ChatGPT) is seen as an improvement over the old (search).
- That happens especially in the field of business experts applying old use cases when evaluating new paradigms. One example is Neeva's shutdown of its AI-based search engine, which highlights the challenges of convincing users to switch to a slightly better choice rather than a much more compelling use case. My argument is that Neeva's lack of success as a consumer-facing search engine had nothing to do with Google's monopoly in creating friction in adopting a new search engine but a lack of a viable business model.
- Proposing a new monetization model without changing the underlying business assumption is unlikely to take over an industry. ChatGPT's success stems from its broader range of use cases compared to traditional search.
- For instance, ChatGPT experienced explosive growth despite initial server limitations not only because users found it significantly better but also because it enabled them to discover many new use cases compared to search.
- Consumer-facing businesses are the most challenging to build because they require something extremely compelling. In that respect, great product people don't offer pre-packaged use cases to users; instead, they enable users to come up with their own compelling use cases. Use cases that can scale at the consumer level are often discovered rather than designed. This means that to create a product that can scale, it is crucial to have a solid discovery framework that involves observing what users are doing with the product and assessing its appeal to other users, which creates "scale opportunities."
And again, I want to remark here, "use cases that can scale at the consumer level are often discovered rather than designed!"
So, what's the point of building a successful consumer business? Well, it's all about understanding how to drive scaling laws toward consumer adoption!
Ciao!
With ♥️ Gennaro, FourWeekMBA
Business Engineering Printed
 |
| Business Engineering Digital Book + Workshop Here |
|
