People often say you’ll know you’ve achieved product–market fit when you see it. You’ll be drowning in customer requests, struggling to onboard new users, and your company will feel like it’s bursting at the seams.
But here’s the thing: my last startup felt like it was coming apart at the seams too, and we never got past the seed stage. And there are plenty of examples of later–stage startups that imploded due to unsustainable business models. So what gives?
Every project is unique. That’s why firsthand experience is invaluable. If you’re young or new to the startup scene, it’s tempting to think your experience mirrors those success stories you read about. Your growth is flatlining for months? Airbnb had the same issue and then skyrocketed! Need to pivot? Slack did it too—and so on. For every tale of success, there's a corresponding story of failure. So, whom should you believe?
We live in an age where you can find evidence to support just about any opinion. Even in the realm of science, which we hold to a higher standard, there are contradictory studies that can take years, if not decades, to reconcile. The same goes for startup advice. For every expert telling you to do something, there’s another one advising the opposite. Both may be reputable and claim to have “proof,” since, after all, they made their millions following their own advice.
But when you’ve actually been through a failure or joined a successful startup—or both, ideally—you start to recognize the real markers of success. That’s why I’m a big believer in being a doer over just being a thinker. If all you do is analyze without taking action, you won’t gain the practical experience needed to make those tough, coin–flip decisions. You’ll be plagued by doubt way more often than you’d like.
However, when you start getting your hands dirty and try things out for yourself, you’ll quickly see what actually works for you. You’ll have the chance to put theories to the test and, little by little, you’ll build up confidence from the real–world experience you’re accumulating.
Because now—now, you’ve seen it with your own eyes.
Typically, our weekly digest focuses on software advancements but the hardware sector has also seen noteworthy developments. Jony Ive, Apple’s former Chief Design Officer, is rumored to be in talks with OpenAI to create the “iPhone of artificial intelligence,” the company’s very first consumer product, with more than $1 billion in funding from Softbank CEO Masayoshi Son.
The real question is whether the term “iPhone” refers to an actual phone or to a revolutionary device with an impact on computing similar to that of the original iPhone. Given that the smartphone market is now well–established and mature, with minimal competition and no recent groundbreaking entrants, I’m inclined to think it will be the latter.
I have another rationale for this. I believe that a phone may not be the ideal platform to leverage OpenAI’s core technological strengths, which are designed for real–world applications like vision, sound, voice, and language. To unlock their full potential, these models need continuous sensory input. And while mobile phones were the first computing devices to offer round–the–clock web access, they have limitations when it comes to truly continuous experience. When a phone is tucked away in your pocket, it can’t see or hear. It’ll never be as proactive as a device that is perpetually worn on your body… such as, say, smart glasses.
Meta Smart Glasses
Last week, Meta, in collaboration with Ray–Ban, unveiled the successor to its two–year–old smart glasses. The updated version continues to be promoted as an everyday wearable, designed to capture photos and videos from a first–person perspective. Like its predecessor, the new smart glasses come equipped with built–in speakers and microphones.
So, what’s new? Meta has announced that in an upcoming release next year, the smart glasses will get multimodal capabilities. This will enable users to engage with their environment using Meta AI. During the event, the company demoed features like playing tennis while asking the glasses whether an out–of–bounds ball was a fault or not.
This is a basic example of a new software and hardware paradigm: ambient computing. Ambient computing focuses on the unobtrusive incorporation of technology into our environment to automate tasks and improve our daily lives. Imagine communicating with an assistant like ChatGPT that sees what you see and hears what you hear in real–time, continuously. This approach to information is fundamentally different from using apps on your phone and even diverges from the user experience with stationary smart speakers like Alexa, which have limited access to real–world context.
I brought up Alexa for a reason. Although I’m keen to try them, I haven’t yet experienced Meta’s smart glasses firsthand, so my thoughts are speculative. I suspect that even with the integration of a multi–modal large language model, this product may face challenges similar to those encountered by Amazon. I own an Echo smart speaker and mainly use it for basic tasks like setting alarms, reminders, playing music, and checking the weather—nothing transformative. This limited scope of use is one reason why Alexa hasn’t established a sustainable business model, incurring an annual loss of about $10 billion. It was only with the advent of ChatGPT that a mass–market product of this genre truly took off, rapidly becoming the fastest-growing consumer app ever. This raises an intriguing question: Will smart glasses follow the trajectory of Alexa or that of ChatGPT?
Answering this is far from straightforward. The success of ChatGPT doesn’t necessarily guarantee that LLM–based glasses will follow suit. The primary use–cases for each are markedly different, in my view. ChatGPT has gained popularity largely for its content–generation capabilities. For example, it assists people with homework, which explains the notable drop in traffic when schools are out, or helps professionals in writing tasks, which is why OpenAI is investing in an Enterprise plan. But smart glasses are likely to be employed in more casual settings: during social interactions, during travel, or on public transport. Given that GPT–4 with vision capabilities has only been widely accessible for a brief period, we’re still in the dark about its sustainable, long–term applications—whether in the home or in public spaces.
Last week, Twitter was abuzz with discussions about Rewind Pendant, a wearable device designed to capture and transcribe real–world conversations. The transcriptions are encrypted and stored solely on your phone, aligning with the company’s privacy–first ethos. They also provide features that aim to ensure that individuals are not recorded without their explicit consent.
Rewind is a macOS app designed as a searchable archive for your personal and professional life. It monitors your laptop, so if you’re in a meeting, the app can summarize it for you by listening and watching along with you. Importantly, it avoids becoming a privacy concern by performing most of the analysis locally on your device, without relying on cloud storage. When paired with the Pendant wearable, Rewind becomes a personalized AI that includes everything you’ve seen, heard, or spoken, even when you’re on the go. This represents another example of ambient computing, similar to Meta’s smart glasses, as these sensory inputs are eventually processed by a large language model.
The concept is divisive. Your stance will largely depend on your perspective on privacy; it could either be seen as invasive or beneficial. This paradox echoes the dilemma that Google Glass encountered years ago: while the device offered utility, its nerdy design deterred users wary of social judgment. In the case of the Rewind Pendant, the device looks sleeker, but its core function as a recording machine may make people hate you for wearing it so you face social scrutiny for choosing to use it.
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