 1. Email makes you feel productive, but you are not. Just observe yourself when you check your email … you’ll probably realize that it’s happening when you’ve hit a “stuck” point on whatever you’re working on. You’re probably at that point in need of your micro-dopamine “hit” so you can get a false sense of achievement, and then you feel better and you go back to your original task. Rings a bell? This would be fine if it happened to you once an hour or more, but I guarantee you it’s happening to you many, many times in an hour. If you’ve got an iPhone, look at the “screen time” and see the number of your mail app “picks” per hour. This should shock you. And the result is that a task that should take you a certain amount of time does take you way longer because of your email “addiction” 2. Email is the most inefficient way of internal communication Email forces you to be reactive. Period. It starts with the first batch of emails you check and respond to at the beginning of your day, then emails beget more emails, as eventually you’ll end up encouraging useless email “conversations”, always ending emails with a question that needs an answer, rather than trying to bring an issue to a quick resolution. Most often this reaches a level of absurdity when you realize that your discussion involves two or more people that seat almost right next to each other in the same office. 3. The case for banning email from work We all agree that email stresses people out and it’s a colossal time suck. I will argue that face-to-face is still the best way of communicating efficiently: it’s the highest bandwidth form of communication and it includes visual sub cues that are essential for thorough understanding, from body language and facial expressions, leaving an impression; to bonding with coworkers. As such, any leader who wants to foster cultural shift from an individual mindset to that of a community will come to the same conclusion as I have: ban internal email! Replace with face-to-face and instant messaging. My experiments and research on those who have done it have experienced the following changes:
Sounds like a shoo-in to me! Let me know what you think! DM me @philippemora
My name's phil mora and I blog about the things I love: fitness, hacking work, tech and anything holistic. Head of Product and VP Engineering. thinker, doer, designer, coder, leader
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 Over the past few months, I have been asked quite a few times about two things: my thoughts on building a startup in the healthcare industry and how to approach a brand new industry from scratch. Obviously I don’t have a magic recipe however I thought I would share a little bit of my thoughts so far, after 4 years …. Why? because I feel that first, I am at a junction (I’ll elaborate in future posts) but also because
I am going to make this short: only three points. To get this started, more than ever, I am a big fan of setting clear strategies for success, in particular being absolutely intent on creating the right teams at any level, tying products to services in an efficient and value driven way and most importantly execute, execute, execute! (and be kind, compassionate and have lots of fun too). Now, here are my few points: First of all, I think healthcare SaaS will require you to build high-touch services in order to deal with extreme variability in your customer’s operational environments - for example, make sure you build across a large product surface area (if you’re “just an app” you will fail) and transactional depth (scheduling, payments) to immediately show utility, value and ROI and make a really airtight case for your risk-adverse buyers at any level. Second, about the other end of the funnel — it’s critical that the customer-facing front line is stacked not only with industry insiders who understand the culture of your users and customers, but most importantly, these insiders also must be absolutely fluent in technology and be able to understand and diagnose issues quickly and efficiently to your internal dev and product stakeholders. In other words, if your customer service folks are awesome with doctors but have no idea about the ins-and-outs of your API stack, prepare for a lot of hurt and wasted opportunities. As a result, extend your engineering and product teams to include field application engineers and run away from low quality, cheap call centers. On the engineering side, hire world class engineers and data scientists who have a deep knowledge of the latest and greatest techs but also have the patience and courage to want to deal with age-old legacy systems that you will have to — without any doubt — integrate with. And lastly, make sure you’re super specific in defining your target market segment because this will immediately define your sales and service delivery model, pricing strategy and sales team profile. But most importantly, you will have to clearly anticipate extra long sales cycles and heavy buyer education needs … As a result, you must be clear (at least in your mind) to favor and prioritize evangelist customers ahead of revenue: they will help you communicate a strong vision and having excited customers who can see the long term “end game” is super important for your survival. As a conclusion, I think that healthcare tech is not for the faint of heart … Check your hubris at the door, be humble and work hard. And most importantly, always aim at building products and services that will delight your customers and change their lives! Let me know what you think! DM me @philippemora
My name's phil mora and I blog about the things I love: fitness, hacking work, tech and anything holistic. Head of Product and VP Engineering. thinker, doer, designer, coder, leader  I did this presentation in June last year at a conference in India and I really wanted to share it so here we go: from code to data, or how users today are training products like we train dogs to perform specific tasks. It’s a long narrative story so I am going to spread it across a few weeks’ posts. (Read The End of Code Part One here and Part Two here) The end of code So, we have talked about the trends we see in the tech marketplace, going from data aggregation to making sense of data in order to enable full personalization of a product that, in a way, self evolves in the hands of the user as they continuously interact with it. We also discussed that these tech product trends are making users evolve rapidly in their relationship and expectations, and with it, the tech business models are rapidly changing from pure user network effects and economies of scale to data network effects and AI-driven systems of intelligence. Now let’s talk about the implications this may have for the skillsets required to implement and successfully deploy those business models.  Software has already eaten the world and today we’re all surrounded with machines that convert actions, thoughts, and emotions into data - the new raw material (“data is the new oil”) - for armies of coders to manipulate. Life itself seems to be ruled by a series of instructions that can be discovered, exploited, optimized and perhaps re-written as companies use code to understand us as intimately as possible from sleep patterns to spending habits and everything in between. As a result, today, the ability to code has become not just a desirable skill but grants insider status to those who speak it fluently: “if coders don’t run the world, they run the things that run the world” (Bloomberg). However, coders should not get used to it: our machines are today starting to speak a different language, one that even the best coders don’t fully understand. Indeed, over the past several years, machine learning in Silicon Valley has aggressively become front and center, and for very good reasons. See, in traditional programming, an engineer writes explicit, step-by-step instructions for the computer to follow. But with machine learning, engineers don’t code computers, they train them. Example if you want to teach a neural network to recognize a cat, you don’t tell it to look for whiskers, ears, fur, and eyes. You simply show it thousands and thousands of photos of cats, and eventually it works things out. If it keeps misclassifying foxes as cats, you don’t rewrite the code. You just keep coaching it. This approach is not new, the math behind it has been around for decades—but it has recently become extremely cheap due to a massive reduction in storage and processing power costs, as discussed previously. As a result, today, machine learning already powers a lot of apps, example Facebook uses it to determine which stories show up in the News Feed, Google Photos uses it to identify faces, Microsoft’s Skype Translator using machine learning to convert speech to different languages in real time. Self-driving cars use machine learning to avoid accidents. As machine learning changes drastically user experiences, the world of coders is also definitely changing rapidly: the days of writing linear programs are basically over. Example, after a neural network is trained on speech recognition, as a programmer you can’t go back inside and look and see how the learning happened - because when you look inside a neural network you see a multilayered set of billions of data points that constantly evolve in their relationships and simply generate guesses about the world. Indeterminacy and un-parsable machine language has direct implications for coders The current shift is giving users a more rewarding relationship with technology, an experience that is more personal. But this also means that experiences can no longer be reduced to a series of comprehensible instructions, indeed, machine learning basically goes in the exact opposite direction: indeterminacy. As for the past two decades, learning to code has been one of the surest routes to reliable employment - today the world is increasingly run by neural-networked deep-learning machines, this requires a very different workforce .... Engineers have to be able to create a combination of handwritten linear code that uses the power of machine learning to adjust it - and to train these systems. This is still a rare combination of skills, because the job requires both a very solid high-level grasp on math and a very good intuition ... In addition to all the “old” programming skills. Data-centric and inferencing evolutional culture In previous posts we have discussed that the days of programming systems linearly with predictable outcomes are numbered and that this cultural shift in software engineering is accelerating: machines today are increasingly not “programmed”, they are trained, and data inferencing, not coding, is now the new center of inertia of the DevOps teams. Today’s software products are being built as systems of intelligence by data scientists who know classic software engineering. In that order… This means that the skillset we are requiring for building our next generation of products is shifting towards data-first designs. What are the implications for startup software teams structure? First of all, we’re looking at blurring lines between data science and devOps with the addition of machine learning for personalization (as we discussed earlier) and data at the everything center of inertia. It’s almost certain that product managers and traditional marketing are going to disappear, to be replaced by full stack designers who go all the way from design ideation to code and the use of data science techniques towards data marketing science. With the addition of the feedback loop created by usage data, users are directly training their software to perform the way they want, generating inferences at the aggregate of a population of users, which means that no single users will experience the same product identically. This will in turn change the way with qualify and support software. In other words, with Indeterminacy and un-parsable machine language combined with data being the customer (and vice versa) comes a new way of looking at how products are built, maintained and supported. In order to cope with such a dramatic culture shift, we’re looking at enhancing team skillsets. We discussed this in the first section: lifelong learning today is a given and we believe that we have reached this cross-roads were we must learn new stuff while maintaining our existing technology advantage. Or fall behind very quickly. Let me know what you think! DM me @philippemora
My name's phil mora and I blog about the things I love: fitness, hacking work, tech and anything holistic. Head of Product and VP Engineering at Sikka Software. thinker, doer, designer, coder, leader.  I did this presentation in June last year at a conference in India and I really wanted to share it so here we go: from code to data, or how users today are training products like we train dogs to perform specific tasks. It’s a long narrative story so I am going to spread it across a few weeks’ posts. (Read The End of Code Part One here) A fundamental platform shift: data network effects As discussed previously, today we are undergoing one of the largest platform shifts in a generation as applications move to the cloud and are consumed on iPhones, Echoes and Teslas, while being built on new stacks and fueled by AI and data. And with it, the way tech companies conduct business is also changing .... and with it the employee skillset necessary for tech companies to stay ahead is also being rapidly disrupted. But before we look at these implications, let’s have a quick look at how the tech business models are evolving from network effects to data network effects by inserting AI-driven systems of intelligence in the current business models. Traditional tech business models To establish themselves as powerhouses companies like google, facebook, amazon, or microsoft all were built on economies of scale and network effects.
Going full stack Today the market is favoring “full stack” companies, i.e. SaaS offerings that offer application logic, middleware, and databases combined. Technology is becoming an invisible component of a complete solution (Example no one cares what database backs your favorite mobile app as long as your food is delivered on time). In the consumer world, Apple made the integrated or full stack experience popular with the iPhone which seamlessly integrated hardware with software. This integrated experience is coming to dominate enterprise software as well. Cloud and SaaS has made it possible to reach customers directly and in a cost-effective manner. As a result, customers are increasingly buying full stack technology in the form of SaaS applications instead of buying individual pieces of the tech stack and building their own apps. Today’s SaaS stack: systems of engagement and systems of record 
The new tech business model: data network effects At the core of data network effects are AI-driven systems of intelligence, which typically cross multiple data sets by inserting themselves in between systems of record and systems of engagement. Success is then achieved by using customer and market data to train and improve models that make the product better for all customers, which spins the flywheel of intelligence faster. Ultimately the product becomes tailored for each customer (that’s the “personalization” aspect we talked about in the intro). To be continued!  Let me know what you think! DM me @philippemora
My name's phil mora and I blog about the things I love: fitness, hacking work, tech and anything holistic. Head of Product and VP Engineering at Sikka Software. thinker, doer, designer, coder, leader. |
i blog about the things I love: fitness, hacking work, tech, Experiences and anything holistic.
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