The barrier to entry is simply not knowing how to start
Making the most of the 14 years you'll spend working
Routines define our lives. We wake up at the same time, commute the same route, work the same hours, eat similar meals, and fall into the same evening patterns. Some routines keep us healthy — brushing our teeth, exercising, getting enough sleep. Others just... happen to us, whether we chose them or not.
One of the biggest routines is the 8-hour workday. Think about it: you spend roughly 8 hours in school as a kid, then graduate into 40 years of 8-hour workdays. That’s one-third of every day. Do the math — just the working hours alone add up to 14 years of your adult life1, or about 121 days every single year2. That’s a lot of time to spend doing something that might not fulfill you, or doing it inefficiently when you could be doing something else.
This isn’t about replacing your thinking or turning into some AI-dependent robot. It’s about using the tools we have now, AI and LLMs, to make these routines work better for us. Let’s talk about how.
Type Uno: Learning New Skills and Changing Careers
A lot of us reach a point where we begin to question our careers and even consider switching to another path. Yeah… yeah… I know we all know that colleague who always says that but keeps doing the same Excel or PowerPoint work year after year without change. However, many of us are genuinely unhappy with our current career status, and no time has been better to use our time wisely to switch paths and gain insight into a new career.
One of the best things you can do is treat these LLM chat interfaces as your personal tutor. It’s true that some LLMs hallucinate and offer made-up information, but for learning new topics and understanding them at a high level, the occasional hallucination doesn’t matter nearly as much as you’d think. You’re not building a rocket ship based on Claude’s advice—you’re trying to understand what product managers actually do, whether you’d enjoy data analysis, or how venture capital firms evaluate startups. For that, these tools are phenomenal.
Here’s how I use it: Let’s say I’m curious about transitioning into product management. I don’t need to spend $2,000 on a bootcamp or read five books to understand if I’m interested. Instead, I open Claude, ChatGPT, Gemini, or Grok and ask: “explain product management to me like I’m a software engineer who’s never done it before. What does a typical day look like? What frameworks do PMs use? What’s the difference between B2B and B2C product work?”
Within 10 minutes, I have a solid understanding. Then I can go deeper: “walk me through how you’d use the RICE scoring framework for feature prioritization” or “give me a mock scenario where I need to decide between two features and explain your reasoning.” You’re essentially getting a patient, infinitely available tutor who meets you at your level and adjusts explanations based on your questions.
Want to understand machine learning but don’t have a PhD in computer science or math? Ask Claude to explain gradient descent using only high school algebra. Curious about finance? Have a conversation about cap tables, dilution, and liquidation preferences. The point is that you can explore career paths in hours instead of months, and you can do it during your lunch break or on your commute home.
This doesn’t replace doing the actual work—if you want to become a data scientist, you’ll still need to write code and build projects, but it dramatically lowers the barrier to exploration. You can figure out what interests you before investing serious time and money.
Type Dos: Working Smarter to Reclaim Your Time
Now let’s talk about your current job — the one you’re already dedicating 40 hours a week to. How can you leverage AI to get your work done faster and better, freeing up time for the things that actually matter?
The honest truth is that most knowledge work involves repetitive tasks that don’t require genius-level thinking: writing emails, summarizing meeting notes, drafting reports, formatting presentations, analyzing data in predictable ways. These tasks eat up hours of your week, and they’re exactly the kind of work that LLMs excel at.
Here’s a practical example: Let’s say you need to write a project update email to your team. Normally, you’d spend 15 to 20 minutes crafting it, making sure the tone is right, the information is clear, and nothing important is missed. Instead, you can paste your rough notes into Claude and say: “Turn this into a professional project update email. Keep it concise, highlight the three main accomplishments, and flag the two blockers we’re facing.” Boom — done in less than 90 seconds. You review it, tweak a sentence or two, and send it.
Or let’s say you’re analyzing sales data and need to create a summary for your manager. Instead of spending an hour in Excel building charts and writing commentary, you can upload your data to Claude, explain what insights you’re looking for, and get a first draft of the analysis in minutes. You still need to verify the numbers, perhaps dig deeper yourself, and add your own interpretation, but you’ve cut the grunt work significantly.
The key insight here is that AI doesn’t replace your judgment — it accelerates the mechanical parts of your work so you can focus on what requires actual human thinking. You’re not outsourcing strategy; you’re outsourcing tedious execution.
And here’s the beautiful part: if you save 30 minutes a day through these optimizations, that’s 2.5 hours per week, which is over 10 hours per month3. That’s time you can use to learn that new skill from Type Uno, spend with your family, work on a side project, or just relax without guilt.
The tools are here. The question is whether you’re going to use them.
On a side note, there are still companies talking about privacy issues and how employees cannot use outside GenAI resources. By this point in 2026, most companies are working through their bureaucratic hurdles to bring these tools into the hands of their employees, and frankly they’re realizing the benefits. If your company isn’t? Perhaps that’s a signal it’s time to brush up that resume and LinkedIn page of yours and start applying elsewhere.
Type Tres: Small Changes That Add Up
Not everyone is looking to change careers or revolutionize their workflow. Maybe you’re generally content with where you are, but you still want to grow in small, manageable ways. This is where AI can be incredibly powerful for low-stakes exploration.
Think about all those things you’ve been “meaning to try” but never quite get around to: learning Spanish, playing board games, understanding basic investing, reading more books, picking up photography. These aren’t career changes—they’re small enrichments to your life that you’d enjoy if you could just get started.
The problem is that starting is hard. You don’t know where to begin, you’re worried about looking stupid, and committing to a class or course feels like too much pressure or money. This is where treating AI as a casual learning companion changes everything.
Want to learn Spanish? Don’t sign up for Duolingo yet. Just open Claude and say: “Teach me 10 useful Spanish phrases for traveling. Then quiz me on them.” Five minutes later, you’ve learned something. Tomorrow, ask for 10 more. Next week, ask it to explain basic verb conjugation. There’s no pressure, no grades, no feeling like you’re failing if you skip a day.
Curious about games like backgammon or checkers? Ask Claude “I want to play backgammon with you” and watch it create an interactive game for you. You can actually play while asking questions and getting guidance. Claude explains why certain moves are good or bad. You’re learning at your own pace, on your own time, with a tutor who never gets impatient.
The same applies to puzzles, cooking techniques, personal finance, meditation practices—anything where the barrier to entry is simply not knowing how to start. AI lowers that barrier to almost zero.
And here’s the thing: most of these explorations won’t turn into anything serious. You’ll try Spanish for a week and realize you’re not that interested. Fine. You’ll play around with your backgammon games for a month and move on to something else, also fine. But one or two of these experiments will stick, and suddenly you’ve added something genuinely enriching to your life without the pressure of a major commitment.
The best part? If something does catch your interest, you can lean in harder. That casual Spanish practice turns into actual lessons. That backgammon curiosity becomes a daily habit. You’ve found a new direction not by making a big scary decision upfront, but by dipping your toes in the water until something felt right.
The Bigger Picture
Look, I’m not going to pretend that AI is going to solve all your problems or make you love your job if you genuinely hate it. But what I am saying is that we have tools now that can genuinely improve the day-to-day experience of being alive—if we use them intentionally.
Your routines are going to happen whether you optimize them or not. You’re going to spend those 121 days per year at work regardless. The question is: are you going to spend them feeling stuck, overwhelmed, and unfulfilled? Or are you going to use the tools at your disposal to learn faster, work smarter, and explore more?
The technology is here. It’s free or cheap. It’s accessible from your phone or laptop. The only thing missing is your decision to actually use it.
So what routine are you going to improve first?
If you work 8 hours per day for approximately 250 working days per year (accounting for weekends and holidays), that’s 2,000 hours annually. Over a 40-year career (roughly ages 25-65), you accumulate 80,000 working hours. Since we sleep about 8 hours per day, our “waking life” is roughly 16 hours daily. Those 80,000 working hours represent 5,000 full waking days, or approximately 13.7 years of your waking adult life—rounded up to 14 years.
Eight hours represents one-third of a 24-hour day. If you calculate work as a fraction of the full calendar year: 365 days ÷ 3 = 121.67 days, or roughly 121 days spent working each year (treating each workday’s 8 hours as equivalent to one-third of a full day).
30 minutes saved per day × 5 working days = 150 minutes per week = 2.5 hours per week. Over a month (4.3 weeks on average): 2.5 hours × 4.3 = 10.75 hours, or approximately 10+ hours per month.