 |
|
|
|||||
I started building Simple in 2019 with a vision that one day, a digital product could help people fix their health as effectively as a human. Five years later, we turned this vision into a company with 160M in ARR, and a team of more than 150 people across multiple countries. If you only look at the highlights, my story can look like a straight line of an entrepreneurs journey. However, getting there required me to rebuild my own thinking and habits. You see, I have ADHD, and a mind that constantly scans for what can go wrong. For years, I treated that as a bug. It only became my superpower once I learned how to direct it. That isn’t an easy journey, but these lessons helped me master my mind and turn a bold idea into a sustainable, fast-growing business. Consistency beats intensity When you see most weight-loss products, theyre usually based on the principles of intensitywhether thats a 30-day challenge or extreme dieting. They sell well, but they rarely stick. Ive tried most of these methods myself7-day water fasts, restrictive eating, vegan, keto, and much more. However hard I tried to push through, nothing worked in the long term. In Simple, we tried a different approach where consistency beats intensity. That means designing features like daily check-ins and context-aware prompts around this idea of helping users sustain effort. The same principle changed how I work. Early in my career, discipline meant 18-hour days, which led me to rock bottom. Discipline doesnt mean doing it all. It means focusing on what actually matters. It means saying no when necessary, doing the tasks that you find boring, and avoiding the temptation to fix everything at once. Your anxiety is helpful if you learn when not to listen to it When my cofounder left the company in 2021, about a year and a half after we started, I suddenly became responsible for everything at once. Frankly, it wasnt what I expected. If you have an anxious brain, you probably know this well: your mind runs endless what if scenarios. I was constantly thinking about what could go wrong, and I couldnt relax. Overtime, I realized that most of my fears had no real basis, but a few were extremely useful early warnings, so my job was to learn the difference. I wrote down everything that was bothering me, then asked myself these three questions: 1. Is this a real problem, or just me spiraling? 2. If its real, can I do something about it in the next 24 hours? 3. If yes, what is the smallest concrete action? You need to believe that it will work, regardless of how irrational it seems When we first pitched Simple, there was little evidence that an app could coach health as well as a human. Given the fact that it was prior to the AI boom, not many believed we could do it. The early version product focused on intermittent fasting. It worked, but we knew it was only one piece of the puzzle. Moving from a simple fasting tracker to a full weightloss coach (and eventually to a holistic AI health coach) required out-of-the-box decisions. If you want to innovate, many people will disagree with you, but you should still move forward. We had to redirect resources from a working funnel toward a vision that didnt yet exist in our metrics. If you dont radiate a basic conviction that things will work (even while you are brutally honest about risks), nobody will bet their career on your idea. Discipline and high standards are an ultimate form of self-love For a long time, I thought self-love meant giving myself more rest or treating myself gently. Some of that is important, but in moderation. The more honest definition of self-love I came to is this: Loving yourself is also discipline, confidence, and high expectations. Its wanting the best for yourself, and asking the maximum from yourself. When youre scaling a company fast, its easy to become the weak linkyoure sleep-deprived, which means that youre slow to make decisions. You avoid hard conversations, and you keep the wrong people in the team too long. When youre not consistent in your standards and habits, not only do you betray yourselfyou also betray your team, because youre not showing up as a leader when they need you to. Decisions that concern other people will hurt, but you still have to make them One of the hardest parts of scaling Simple was making changes to the leadership team. Some hires were clear mistakes, while others were great at an earlier stage but became a brake on the company later. Firing or moving on from such people can be emotionally painful because you invest trust and hope in them. What helps me with this is to separate the person from the role. You can value their contribution, and still accept theyre no longer the right fit. Giving them more time wont turn a bad hire into a great fit. Itll only make the situation more expensive, so rip off the band-aid, but dont forget to show your appreciation. Your company scales at the same speed you do In 2023, I realized our biggest bottleneck wasnt our market, investors, or team. It was me. I placed my attention on growth and marketing, and I struggled to see what the company really needed to improve. I vividly remember the day I realized, because it was the day Simples growth trajectory drastically changed. I cut back on experiments and focused on the product and science behind it. Within a year, we repositioned Simple from a tracking app to a weightloss coach, and our AI coach became a central part of the product. At the same time, retention improved, and so did our financial metrics. Around the same time, I wrote a phrase in my notes that I come back to often. The universe gives me exactly as much energy as I need to handle my goals. If the goals become bigger, more energy will come. And since then, Ive learned that every new stage of company growth is also an invitation to become a new version of myself.
Category:
E-Commerce
As employers have wrested back control of the job market, it has been a sharp contrast to the post-pandemic years when workers seemed to hold the power. In 2025, employees fretted about their job security and the sweeping impact of artificial intelligence on their work livesnot to mention corporate Americas continued commitment to keeping them in the office for longer. Here, weve compiled some of the most popular Work Life stories from this yearon the issues that consumed you most. The 996 schedule This year saw the return of hustle culture in Silicon Valley, as AI startups popularized a grueling work schedule that became popularized in China. The 996 schedule refers to a 72-hour workweekin other words, working from 9 a.m. to 9 p.m., six days a weekand has grown more common in Silicon Valley as founders and tech leaders scramble to outrun the competition. But experts say this could stoke burnout at a time when workers are already stretched too thin. The steady drumbeat of RTO The return to office is here to stay, despite how workers may feel about it. Business leaders like Jamie Dimon have been among the most vocal supporters of in-office work, dismissing employee concerns and the concept of work from home Fridays. Hes not alone: Amazon employees were forced to return to the office five days a week, while the federal government put an end to remote work this year. The truth behind quiet quitting Were still talking about quiet quitting. While older generations might think Gen Z workers are lazy or lack motivation, Fast Company contributor Jeff LeBlanc argues quiet quitting is a rational response to workplaces that lack fairness, structure, and alignment with employee values. Leaders who cant retain Gen Z talent should wonder whether theyre the problem, he writes. The question isnt whether Gen Z is willing to work hard. The real question is: Are leaders willing to evolve? The rise of job hugging In a tough job market, many employees are actually job hugging rather than quiet quitting. But doing so can actually hurt workers who are unhappy with their job situationor speed up their burnout. Cognitive reframing can helpfocusing purely on the positive aspects of a draining role, such as a friendly team, and tuning out the rest, writes Alex Christian. Sometimes, however, the only solution is to wait it out and hope that the economy turns around. The fractional leadership boom In the years since the pandemic, many senior leaders have been reevaluating what they want out of work. Enter the fractional role, which has enabled experienced C-suite leaders to set their own schedule and work across multiple companies. Fractional leaders have become more common at companies that dont need someone in the position full-time, allowing people in these roles to find more balance. The plight of middle managers Middle managers have had a challenging few years. As the pressures on them mount, many are headed for a crash, according to meQuilibriums Jan Bruce. With Gen Z increasingly rejecting the manager track, there could be a shortage of qualified leaders in the years to come, she argues. So what can companies do differently? Explicit policy decisions can help managers protect and promote their own mental and physical well-being, Bruce writes. This might look like mandatory disconnect periods, sabbaticals, or easing access to acute mental healthcare resources. Making sure managers have consistent, supportive check-ins with their own supervisors can help reduce isolation. The importance of office friends Workplace friendships are not what they used to beand its not good for business. Friendships at work can help boost employee performance and well-being, writes Fast Company contributor Mark C. Crowley. In fact, leaders should create an environment that encourages connection and invests in those friendships. Creating a culture where connection is valued doesnt just improve employee moraleit strengthens retention, creativity, and performance, he writes. By fostering friendships, leaders dont just build better teams; they create desirable workplaces. The productivity gains from AI We all know AI is reshaping how we work. But as the technology permeates the workplace, it might just be revealing how much of what we do is busywork. Were witnessing a productivity revolution without a purpose revolution, write Fast Company contributors Tomas Chamorro-Premuzic and Alexis Fink. Tools are improving, but the work remains hollow. Instead of using AI to invent better ways of working, many companies are simply using it to churn out more of the same, only faster.
Category:
E-Commerce
Theres bad news for those using digital surveys to try to understand peoples online behavior: We may no longer be able to determine whether a human is responding to them or not, a recent study has shownand there seems to be no way around this problem. This means that all online canvassing could be vulnerable to misrepresenting people’s true opinions. This could have repercussions for anything that falls under the category of information warfare, from polling results, to misinformation, to fraud. Non-human survey respondents, in aggregate, could impact anything from flavors and pricing for a pack of gum, to something more damaging, such as whether or not someone could get government benefitsand what those should be. The problem here is twofold: 1) humans not being able to tell the difference between human and bot responses, and 2) in instances where automation is regulating action based on these responses, there would be no way to use such polling and safeguard against potentially dangerous problems as a result of this indistinguishability. The study by Dartmouths Sean J. Westwood in the PNAS journal of the National Academy of Sciences, titled The potential existential threat of large language models to online survey research, claims to show how we can no longer trust that, in survey research, we can no longer simply assume that a coherent response is a human response. Westwood created an autonomous agent capable of producing high-quality survey responses that demonstrate reasoning and coherence expected of human responses. To do this, Westwood designed a model-agnostic system designed for general-purpose reasoning, that focuses on a two layer architecture: One that acts as an interface to the survey platform and can deal with multiple types of queries while extracting relevant content, and anothercore layer that uses a reasoning engine (like an LLM). When a survey is conducted, Westwoods software loads a demographic persona that can store some recall of prior answers and then process questions to provide a contextually appropriate response as an answer. Once the reasoning engine decides on an answer, the interface in the first layer outputs a mimicked human response. The system is also designed to accommodate tools for bypassing antibot measures like reCAPTCHA. Westwoods system has an objective that isnt to perfectly replicate population distributions in aggregatebut to produce individual survey completions [that] would be seen as reasonable by a reasonable researcher. Westwoods results suggest that digital surveys may or may not be a true reflection of peoples opinions. There is just as likely a chance that surveys could, instead, be describing what an LLM assumes is human behavior. Furthermore, humans or AI making decisions based on those results could be relying on the opinions of simulated humans. Personas Creating synthetic people is not a new concept. Novels, visual media, plays, and advertisers use all sorts of creative ideas to portray various people in order to tell their stories. In design, the idea of Personas have been used for decades in marketing and User Interface design as a cost-cutting and timesaving trend. Personas are fictional composites of people and are represented by categories like Soccer Mom, Joe Six-pack, Technophobe Grandmother, or Business Executive. Besides being steeped in bias, Personas are projections of what the people creating them think these people would be and what the groups they might belong to represent. Personas are a hidden problem in design and marketing, precisely because they are composites drawn from real or imaginary people, rather than actual people — the values ascribed to them are constructed by other peoples interpretations. When relying upon Personas instead of people, its impossible to divine the true context of how a product or service is actually being used, as the personas are projected upon by the creator, and are not real people in real situations. Thus, the problems with using Personas to design products and services often arent identified until well after such products or services come to market and fail, or cause other unforeseen issues. This could be worse when these human-generated Personas are replaced with AI/LLM ChatBot personas with all the biases that these entailincluding slop influences or hallucinations that could make their responses even more odd or potentially even psychotic. Quant versus qual Part of the larger problem of not understanding peoples needs with surveys started when research shifted to statistical data collection based on computation, also known as quantitative methods, rather than contextual queries based on conversations and social relationships with others, or qualitative methods. As Big Data came online, people began to use quantitative methods such as online surveys, A/B testing, and other techniques to understand customer/user behavior. Because machines could quickly compile results, quantitative research seems to have become an industry standard for understanding people. It is not easy to automate qualitative methods, and replacing them with quantitative methods can forfeit important context. Since almost a generation has gone by with the world focused on computational counting, its easy to forget about the qualitative data methodsfound in social sciences such as Anthropologythat use contextual inquiry interviews with real people to understand why people do what they do, rather than trying to infer this from numerical responses. Qualitative research can give context to the quantitative data and methods that rely upon machines to divine meaning. They can also work outside of big data methods, and are grounded in relationships with actual people, which provides accountability to their beliefs and opinions. The process of talking with real people first contextualizes that content, leading to better outcomes. Qualitative methods can be quantified and counted, but quantitative methods cannot yet easily be made to be truly broadly contextual. One difference between using qualitative and quantitative methods has to do with transparency and understanding the validity of peoples responses. With older human-made Personas, there are obvious assumptions and gapsits crude puppetry and projection. But when people become manufactured by Chatbot/LLMs that utilize a corpus of knowledge mined from massive volumes of data, there can be fewer ways to separate fact from fiction. With chatbots and LLMs, the artificial entity is both the creator of the person, potentially the responder to te person, and either the interpreter of that fake chatbot persons responses, or being interpreted by an LLM. Thats where it can get dangerous, especially when the results of this type of slop tainted research are used for things like political polling or policing. Westwoods research has shown that: Rather than relying on brittle, question-specific rules, synthetic respondents maintain a consistent persona by conditioning answers on an initial demographic profile and a dynamic memory of previous responses. This allows it to answer disparate questions in an internally coherent manner,generating plausible, human-like patterns It can mimic context, but not create it. Back to the basics When GenAI is moving towards conducting the surveys, acting as respondents, and interpreting the surveys, will we be able to tell the difference between it and real people? A completely automated survey loop seems fictional, until we see how many people are already using Chatbots/LLMs to automate parts of the survey process even now. Someone might generate a persona, then use that to answer surveys that AI has designed, that someone else will then use a Chatbot to access AI to interpret results. Making a complete loop could be terrible: someone may then use AI to turn the Chatbot created, Chatbot answered, and AI interpreted survey responses into something that impacts real people who have real needs in the real world, but instead has been designed for fake people with fake needs in a fake world. Qualitative research is one path forward. It enables us to get to know real people, validate their replies, and refine context through methods that explore each answer for more depth. This type of work AI cannot yet do as LLMs currently base answers on statistical word matching, which is unrefined. Bots that replicate human answers will mimic a type of simulated human answer, but to know what real people think, and what things mean to them, companies may have to go back to hiring anthropologists, who are trained to use qualitative methods to connect with real people. Now that AI can falsely replicate human responses to quantitative surveys, those who believe that both quantitative methods and AI are the answers to conducting accurate research, are about to learn a hard lesson that will unfortunately, impact all of us.
Category:
E-Commerce
All news |
||||||||||||||||||
|
||||||||||||||||||