The Network Garden - Mark Sigal's Blog

Some thoughts on the Product Demo

Over the years, I have built a lot of products across a bunch of different segments, and one truism is that getting the product demo right is a big deal when it comes to connecting with customers, partners and investors.

Why? When done right, the product demo has the impact of engaging the audience and making them believers.

To be sure, this is no "sleight of hand," in that the demo is tangible, specific, logically ordered, and tells a story that strives to be engaging and authentic.

The product has to actually live up to the brand promises of the product demo.

Towards that end, there is a grounding aspect, in terms of how product design can be shaped by creation of the product demo, and vice versa.

For example, one can work backward from the product demo to the specific use case(s) being demonstrated, and infer the features required, the user flows, the benefits they deliver, and how the functionality is accessed by the user.

One can also work forward from the demo to the out-of-the-box experience for the user, training, product trials, support, future enhancements, and the like.

But the product demo is also about PHILOSOPHY, to which I’d put forth that one way to think about a product demo is akin an Infomercial.

The infomercial is for a set of gourmet cooking utensils, where the story is all about the terrific yummy dishes being cooked up using them.

There's, of course, a performative aspect in product demos for there's knowing, set up and the payoff, and hitting one's marks in terms of delivery, earnestness and energy.

Such Infomercials go one of two ways. They start by showing you the terrific dish — oh, it’s so delicious — and then pivot to the recipe and the actual making.

Or, they start by talking about the recipe and how easy and fun it is to make, and the payoff is the food coming out of the oven.

In other words, begin with the payoff. Or, finish with it.

Food for thought.

Apple, AI and the Me-Verse

“My dad believed in two things: That Greeks should educate non Greeks about being Greek and every ailment from psoriasis to poison ivy can be cured with Windex.” - Toula, My Big Fat Greek Wedding

So, too, it goes with AI, the idea that everything can be made better, cured or transformed with AI.

Or, more ominously, obsoleted by AI to the point of the inevitable Extinction Event.

Depends on the day of the week.

This type of thinking feeds a false dichotomy between: A) The continuous real time measurement of the immediacy, acceleration and arc towards Artificial General Intelligence (AGI); and B) The still transformative Generative, Agentry, Pattern Recognizing and ever Optimizing little brother to AGI that exists today.

Understanding Artificial General Intelligence

Think of AGI as a system of systems that are capable of understanding and reasoning across a broad range of tasks, which are understood by the AGI as both one and many/all.

As such, AGI will not only be able to replicate or predict human behavior, but also embody the ability to learn and reason across diverse scenarios, from creative endeavors to complex problem-solving, including an ever-widening range of “generative” scenarios.

It is not hyperbole to say that AI’s path to AGI could be worthy of a "Manhattan Project"-style endeavor in terms of what it represents and how it will shape both our competitive advantage and global security for generations to come.

That stated, the context of RIGHT NOW is useful when wrapping one's head around the not yet AGI version of AI where Large Language Models (LLMs) are the lingua franca of the moment.

Towards that end, I like this framing by Benedict Evans in ‘Looking for AI use-cases’ equating LLM’s to the new SQL, but not the new HAL9000 in terms of their import and impact:

“On this basis, we would still have an orders of magnitude change in how much can be automated, and how many use-cases can be found for LLMs, but they still need to be found and built one by one. The change would be that these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000.”

I’d argue that where we are right now is akin to the BASIC stage of the PC, HTML and the early Web, and when SQL emerged.

Give me more MFC, more iOS and less Browser plugins and less Flash, and I'll be a happy camper.;-)

Put another way, the LLM is part of a continuum on the path to AGI, an AGI future which feels inevitable within the next five years, probably less.

Apple Intelligence: Apple at its Best

Apple is at its best when it follows a market, doesn't invent it but makes it better by synthesizing and deeply integrating the pieces which are themselves "jobbed" better than its peers.

Think of how much Apple products like MacBook, iPhone, Apple Watch and AirPods build upon superior mechanical engineering and material sciences prowess -- befitting a holistic, vertically integrated product strategy across hardware, software, services, marketplaces and developer tools.

Material sciences may seem like an odd segue-way into talking about AI, but it speaks to Apple's pragmatism when it comes to right material, right component, right process and right sized integration to drive the user to well-grooved experiences.

Apple Intelligence is Apple integrating the right amount of AI into its way of thinking and doing.

It builds upon the fact that Apple is uniquely positioned to understand my personal contexts since all of my devices are part of the Apple Ecosystem. 

It is my personal, anonymized and secure walled garden, making Apple Intelligence the ME-verse.

Watch the WWDC 2024 Keynote, which was excellent, especially the developer offerings, but also consider the following Apple Intelligence "windex spritzes":

1. Language: Apple Intelligence can easily rewrite your work, you can tune it work styles and the functionality is easily called upon.
2. Images: Genmojis are your Animoji using generative AI; Image Playgrounds (with Styles) and Image Wand functions catch Apple up to what Google, Samsung have been shipping for some time.
3. Siri (Natural Language): Putting aside the question of whether the very uneven experience that Siri delivers will ever be fixed -- Home Pods, Home Pods mini -- the idea of using Siri to Invoke Tasks based on voice or written narratives sounds good. Note: I am a big believer that even an incremental leap in Natural Language systems is logarithmic in its impact.

Apple Intelligence integrates with my stuff, meaning deeper, smarter searches of text, images and video, and critically, it's built around an architecture that is decidedly on-device, in a way that Siri never was ("I'm having trouble connecting to the Internet").

There is am App Intents construct (and API), which enables me to give or limit access that Apple Intelligence and supporting Apps have to my devices' Books, Browsers, Cameras, Document Readers, File Management, Journals, Mail, Photos, Presentations, Spreadsheets, Whiteboards and Word Processors.

Apple Intelligence is marketed as AI for the rest of us, underscoring the fact that there are so many ways to sprinkle in AI when one thinks about jobs to be done:

1. Instant Rich Summaries
2. Directed Generative Images (generalize to media)
3. Watcher Services
4. Task Manager with deep wrapper/handle
5. Turn up the volume and dimensional depth on existing algorithmic features (memories)

Scott Galloway captures why this is such Brand-Product-Market fit in 'Second Mouse.AI':

“Apple Intelligence” is more than a great brand move; it encapsulates the company’s strategy. Take something invented elsewhere; make it more consumer friendly, easier to use, and more reliable; mix in world-class industrial design; and print billions. Artificial intelligence is for tech bros and data scientists. Apple Intelligence is AI for the rest of us. Shrewd."

What this says about Apple's focus on its knitting in a market that it's lazily and unfairly been labeled as late to is well noted by Ben Thompson of Stratechery in 'WWDC, Apple Intelligence, Apple Aggregates AI':

“What this means is that Apple Intelligence is by-and-large focused on specific use cases where that knowledge is useful; that means the problem space that Apple Intelligence is trying to solve is constrained and grounded — both figuratively and literally — in areas where it is much less likely that the AI screws up. In other words, Apple is addressing a space that is very useful, that only they can address, and which also happens to be “safe” in terms of reputation risk. Honestly, it almost seems unfair — or, to put it another way, it speaks to what a massive advantage there is for a trusted platform. Apple gets to solve real problems in meaningful ways with low risk, and that’s exactly what they are doing.”

The next wave is coming into sight, and it's destined to be an illuminating source of creation, design, iteration, evolution, change, growth and disruption.

Apple's approach to AI with Apple Intelligence is a good model for existing companies to think about how they can harness AI in an additive fashion.