 In the past decade and a half, I have seen entire industries being disrupted by upstarts using absolutely none of the traditional resources businesses needed in the past to keep growing. Examples: Uber vs. Taxis, Twitter vs. the Press, Netflix vs. Cable. Uber doesn’t own a single car, Twitter doesn’t have a newsroom, Netflix doesn’t own a content distribution network. How are they doing it? They’re all platforms, they use technology to connect and manage supply and demand in real time. Platforms are powerful because they eliminate gatekeepers and create community feedback loops called network effects. Example: more demand attracts more drivers, more drivers increases geographic coverage, more coverage gets faster pickups, fast pickers means more users join the platform, and so on. In turn, driver downtime goes down, which reduces prices, which leads to more scale. In the past, dominance was achieved through supply economies of scale via increased production efficiencies. Today, it’s all about creating demand economies of scale using technology on the demand side to create ever larger networks of value and scale. Building a platform: This isn’t about creating a good or service and channeling it to customers! First, a platform starts with enabling a direct interaction and the exchange of value between participants – producers and consumers. The platform also enables the quantification of value by creating a sort of value unit between participants. Example: for AirBnb the value unit is the listing information created by the seller that is served to buyers based on their search criteria. Second, platforms are really good at attracting users and encouraging interactions. The most difficult is to generate pull because it’s pretty much a chicken-and-egg-type problem: users won’t come to a platform unless it has value and a platform won’t have value unless it has users. The solution to that is called the feedback loop. Example: Facebook realized it had no value unless users had a meaningful number of connections. So instead of trying to get more people to the platform, they focused on getting the existing users find their friends and help them connect. Third, platforms use the data they collect about producers, consumers, the value units created and the goods or services exchanged in order to get the best matching between supply and demand possible, and most importantly, in real time: the more data a platform has and the more effective it becomes to use this data to foster more effective matching and more interaction the more successful it will become. That’s the feedback loop. Iterate to remain: Once a platform has established its core interactions, it’s time to iterate by layering more peripheral interactions on top of them. Let’s by the way take this with a grain of salt, as we need to remember that platforms aren’t entirely planned – most of the activity is controlled by the users which means platform builders must allow themselves to expect serendipitous discoveries by closely monitoring usage behavioral data to unlock new value … Uber X and Uber Pool are great examples, cloud kitchens and Doordash are also coming up to change the way we eat (see my note about cloud kitchens here). 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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 We’re already 6 weeks into 2019 and we’ve been super busy working on changing the way you see healthcare at Sikka – meanwhile I have not found the time. Here’s what I am tracking in 2019 besides digital health and ai --- passionate about food tech and autonomous vehicles, especially the secondary disruptions they’re going to foster (example – autonomous vehicles and real estate values, food tech and home/apartment design) Cloud Kitchens Cloud Kitchens and its sister company Cloud Retail are the two arms of the new venture from Uber co-founder and former chief executive, Travis Kalanick, which was formed with a $150 million investment. Cloud Kitchens are commercial kitchens designed for on-demand food prep only: they don’t have dining rooms and thus not only avoid the costs associated with them but also are able to focus on their food assembly lines and methodologies to maximize yield. Cloud Kitchens distribute their foods on platforms like DoorDash, Uber Eats or Postmates. That’s the obvious but the real disruption isn’t there – its inside the cloud restaurants’ digital storefronts and the user data they generate: in near real-time they can A/B the menu items and adapt to user tastes and preferences. We’ve already seen that with startups like Stitchfix, here the data is applied to food tastes and demand. Basically the menu product is evolving as you know what your customers want to order. Right now we see 10100 (Kalanick’s investment firm) building a network of these cloud kitchens to service the supply-side of the delivery platforms such as Uber Eats. The goal is to build the infrastructure and platform software to enable them to open delivery-only location in minimal capex and time. This is doing two brilliant things: first, we can imagine that the role of the Chef can evolve from one-kitchen one-menu location, to one-cloud, multi-kitchen locations. In a way we’re evolving from theme-based dine-in chains to customer-centric, taste, quality and creativity-based food choices, all of this available at the tap of a mobile button. Whereas we saw for the past 5 years delivery platforms getting their food items by begging established brick-and-mortar, dine-in locations in order to seed the funnel and establish demand, more and more today we see the same locations now begging for real-estate on the very same platforms as demand is shifting to the cloud. As Cloud Kitchen networks develop we are going to see further vertical integration thus reducing the need for dine-in facilities (those are going the way retail is going today) – and once established they’ll invest in Chefs and will complete the full redefinition/disruption of the category. Driverless Cars The autonomy ecosystem: we know that autonomous vehicles, whether it’s trucks or cars are set to disrupt the auto/trucking industry (and directly associated like insurance cos) as we are going to see a shift from individual ownership to fleets of driverless cars. I am pretty sure that people who are born today will never have to learn to drive. This creates massive new opportunities. So let’s have a quick look at the secondary disruptions. (and if you want more info about all the other stuff that will change beyond the car… from public infrastructure, the automotive value chain, and energy, to finance (including insurance), the justice system, and shopping too – and most importantly when do we think this will actually happen: check here. By Frank Chen (@withfries2) is awesome.
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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