It’s hard, complex, multidisciplinary yet exceptionally rewarding

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Photo by Hush Naidoo on Unsplash

My professional software experience has for the most part been in the B2B sector, working products spanning video-conferencing, distributed file systems and SQL engines. My most recent experience at Kheiron is my first foray into AI and healthcare. In a previous article, I covered the differences between AI products and software ones. This article is concerned with the differences I observed in developing software for the healthcare sector.

First a bit of context about the sector, mostly driven from my own impressions with empirical evidence when available.

The healthcare market is massive. In the US alone, this market is estimated to be a ~$3T market employing about 2.4 people. This market is inclusive of health providers, IT, payers, pharma and more. …

Most AI products are primarily concerned with making inferencing decisions. An AI model receives some input which will run through the model resulting in some decision, oftentimes called a prediction or inference. Depending on the nature of the AI model, the precision of the decisions it makes can have profound effects on its usage and ultimate success.

Consider a recommendation engine, perhaps one that recommends what movies to watch based on your viewing history. The AI model in this case is making a predictive decision based on your own viewing history. We’re all fairly accustomed to these recommendation engines on sites like Netflix (movies) and Amazon (products). We also find similar recommendation engines at work on popular social networks that decide what news I get to see, which ads are targeted to me and so forth. …

A case study of

I have been waiting for a pure-play artificial intelligence (AI) company to file for an IPO to get an in depth look at the company’s business model and how varied it is to traditional software products. My interest lies in trying to understand the impact of the long tail of AI on the company’s business model and financials. I had written about this topic earlier in a couple of previous posts. The first offered a glimpse on how building AI products is fundamentally different than traditional software. …

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Image source: Annie Sprat

Equity is a significant portion of a startup’s compensation package, in fact it is arguably one of the top reasons why people join startups. In spite of the popularity and importance of equity, I’ve seen that the way it is used can vary quite significantly between companies. While I do not posit that there is a right approach to equity compensation, I do believe that there are certain outcomes that should be universally applicable. These are outlined below.

Equity >> Cash

This one should be non-controversial. As an employee joining a startup, I want to participate in the potential economic growth of the startup; equity gives me that. Equally important, a startup needs to preserve its cash, therefore extending its runway and ability to operate without raising more money (and further diluting its current investors and employees). It therefore, seems quite reasonable that a startup would prefer to compensate its employees with equity over cash. Therefore, the balance of equity to cash in an employees compensation package, should tilt more towards the equity component. …

If there is a single unifying factor that I have found across all startups, both ones I worked at or advised, it is the following: There will always be more work than the resources available to do it. Always.

There are two consequences to the above. The first, is being ruthless with prioritizing work. You have to pick your bets very carefully and where you choose to invest your scarce resources — a topic for another post. The second, is the importance of excelling in recruiting. …

On May 11th 1997 Deep Blue, a computer program, beat Gary Kasparov; chess grandmaster and arguably one of the greatest players of all time. That was the first time that a computer had defeated a chess grandmaster under tournament conditions. That moment represents a watershed moment between man and machine, one that offers a tremendous amount of opportunity and collaboration between both.

A month after the match, Kasparov wrote the following in his book, Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins,

“The scrum of photographers around the table doesn’t annoy a computer. There is no looking into your opponent’s eyes to read his mood, or seeing if his hand hesitates a little above the clock, indicating a lack of confidence in his choice. As a believer in chess as a form of psychological, not just intellectual, warfare, playing against something with no psyche was troubling from the start.”

How to build generalizable AI products

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Image source: Unsplash

In a previous article, I outlined my recent experiences with building AI products and their differences relative to traditional software. In the second part of this series, I will take this one step further and discuss how AI products (models) are dynamic entities that constantly need to be optimized along with the implications of this phenomena.

Let’s imagine for a moment that we are working on building a novel AI product. Our product will receive as input an image of a car crash and generate an output decision: whether the car is a total loss or not. One can imagine that this model could be used by insurance companies to make a quick assessment of car insurance claims they receive. …

On the difference between ML and software products

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I recently joined a series-A startup — Kheiron Medical — that is a pure-play machine learning (ML) company. Kheiron develops AI-based products that are used by radiologists to detect breast cancer in women.

One of my motivations for making this move was to gain a better understanding and learn about ML companies and how they differ from traditional software development, which is my background. In this post, I will cover the differences between traditional software development and ML from a developers perspective. …

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Joining a startup can be an exhilarating experience. It allows you to actively participate in building new products from the ground up along with building a company. The latter in my opinion being one of the more exciting and rewarding parts of the journey. There is also another motivating factor to joining a startup; wealth creation. When we think of wealth creation in startups, we typically envision a growing company, one whose valuation balloons (unicorns) culminating in a flashy IPO.

Just how common is that and is an IPO the only liquidity event for startups nowadays? In this article, I will be exploring these questions and navigating the landscape of both public and private (venture) markets. Most of this article, including all the charts, are based of this most excellent report by Michael Mauboussin & Dan Callahan @ Morgan…

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I recently came across the tweet below, which is certainly one I can relate to. Every startup that I know is facing a dire shortage in engineering talent. But, is hiring the only way out of this conundrum?


Karim Fanous

Tech leadership at various early stage startups: Qumulo, Dremio and now Kheiron Medical

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