Billionaire investor Charlie Munger is famously quoted as saying, “The first $100,000 is a bitch, but you gotta do it. I don’t care what you have to do – if it means walking and not eating anything that wasn’t purchased with a coupon, find a way to get your hands on $100,000”.
For most people, saving $100k in a single year is tough. In the following table, I have compiled a list of job descriptions of how long it would take them to save $100,000, assuming a 10% and a 50% savings rate:
|Profession||Avg Income (USD)||Years to $100k (10% Savings Rate)||Years to $100k (50% Savings Rate)|
Even though it takes 5-10 years of study, hard work, and possibly, a student loan to join each profession, it takes roughly another 10 years to save up $100,000 with a 10% savings rate.
Even if you live frugally and save up half of your annual salary, you’d need two years, so it wouldn’t make our challenge of achieving $100k of net worth in a single year.
This brings us to the first answer to our challenge of saving up $100k in a single year. It’s not the best option, my favorite is Way 3 (keep reading), but it is possible with brute force…
Way 1: A Single High-Paying Job
High-level tech positions earn $200k or more annually, especially when considering total compensation (base salary, bonuses, stock options, etc.).
Here’s a list of 10 tech job descriptions where it is possible to make $200k or more, depending on the company, location, and nuances of the job role:
- Machine Learning Engineer: Specializes in designing and implementing machine learning applications. They utilize data modeling and evaluation strategy to create and deploy ML models.
- Data Engineering Manager: Leads the team responsible for building and maintaining the architecture (like databases and large-scale processing systems), pipelines, and data sets used by data scientists and analysts.
- Site Reliability Engineer (SRE): Combines software and systems engineering to build and run large-scale, distributed, fault-tolerant systems.
- Cloud Solutions Architect: Designs multi-platform solutions that interface with cloud services like AWS, Google Cloud, or Azure.
- Blockchain Developer: Specializes in developing and implementing blockchain solutions and smart contracts.
- AI Research Scientist: Focuses on advanced computational techniques and methods to improve or develop new AI algorithms.
- Prompt Engineer: Expert in designing and implementing models like GPT-3 or GPT-4, ensuring they cater to specific user needs while maintaining efficiency and effectiveness.
- DevOps Director: Leads the engineering team that builds and maintains the infrastructure, ensuring smooth CI/CD processes, scalability, and system reliability.
- Cybersecurity Director: Heads the strategy and implementation of cybersecurity measures across the organization, ensuring data protection and compliance with regulations.
- Chief Technology Officer (CTO): The senior-most tech role in most organizations. Responsible for the technological direction of the company, including setting tech strategy, making high-level decisions, and managing tech teams.
Here’s the same table as before, but for those roles:
|Profession||Income (USD)||Years (10% Savings Rate)||Years (50% Savings Rate)|
|Machine Learning Engineer||$220,000||4.55||0.91|
|Data Engineering Manager||$230,000||4.35||0.87|
|Site Reliability Engineer (SRE)||$210,000||4.76||0.95|
|Cloud Solutions Architect||$225,000||4.44||0.89|
|AI Research Scientist||$250,000||4.00||0.80|
|Chief Technology Officer (CTO)||$280,000||3.57||0.71|
All those high earners will go from $0 to $100,000 net worth in a single year when assuming a 50% savings rate which is easily possible given the high income levels.
The best bet you have is to start in a fresh industry where few people have industry experience or even academic degrees because the industry is extremely young such as blockchain development, Bitcoin, and prompt engineering.
Yet it’s really tough to even get those jobs. In fact, seeking a single job is not the best vehicle to attack this challenge, in my opinion.
Way 2: A Chill Job with Side Income Freelancing
A much better second way, in my opinion, is to seek a chill job that pays for your living expenses (or find a spouse who does it) and that gives you some daily space and time to work on your side income business as a freelancer or consultant.
If the job is not too stressful, you can work on your side income daily. There are many such jobs. In fact, I’d say most normal jobs are like this. I personally know many German employees working for large automobile companies (e.g., Bosch, Daimler) that offer a cool 30h work-week that gives you more than enough time and space.
Let’s assume you can work 1 hour in the morning before going to your chill job and 2 hours in the evening instead of watching Netflix (between 7:30 p.m. and 9:30 p.m. so you can go to bed early). Let’s be aggressive and assume your chill job gives you one additional hour to work on your business during job time (or during a 2h “lunch” break in front of your screen).
With such a chiller job, you can easily add 4 hours per day, including Saturday and Sunday, without getting too exhausted.
Remember, our challenge only lasts one year!
Per year, you can work an additional 1460 hours in your side-income freelancing business. As your main job (or spouse) pays for the lifestyle, you can save all proceeds of this freelancing business.
To save $100,000 in a single year this way, you’d have to average $69 per hour after tax in your freelancing business:
With a bit of hustle, this is not unlikely, even during the first year. Many have done it before you. Making $69 per hour as a freelancer is just slightly above average, depending on which sources you check out:
But we are not striving for average anyway; the top one-third is good enough for our purposes. At the very least, making $69 per hour as a freelance developer is much more likely than getting a $200k per year job without prior experience.
💡 Resources: How to accomplish this is beyond the scope of this article. You can check out our freelance developer course on the Finxter Academy to learn how to approach your own 1-person freelancing coding startup, starting from zero.
Way 3: Creating a Small Saleable Business System
Now here’s to my preferred way to address this challenge without spending 12 hours per day working: create a saleable business system.
💡 A saleable business system is often valued at a multiple of the monthly profit. Many web businesses such as software as a service (SaaS), dropshipping, Amazon FBA, advertising businesses, or Kindle ebook stores are often valued at a 48x monthly multiple. So to get to a $100,000 net worth, you only need to create a saleable business system with a $2,200 monthly profit.
How would you sell it? There are many online brokers you could list your business system, such as this one:
Creating a saleable online business that creates $2k in monthly profit can be accomplished within one year — especially if you work 4h per day for 365 consecutive days on it.
How to Create the Startup Within 365 Days?
The Agile methodology, often contrasted with the Waterfall model, focuses on iterative development, allowing for more flexibility and adaptability. Let’s use a more iterative and fine-grained approach to build a $2k/month SaaS business online within 365 days, based on Agile principles.
Phase 1: Discovery & Research (Days 1-30)
Week 1-2: Problem Identification & Market Research
- Conduct interviews with potential users.
- Identify pain points and needs.
- Analyze competitors and their offerings.
Week 3-4: Idea Validation & Hypothesis Testing
- Draft a hypothesis for your SaaS solution.
- Create a basic landing page outlining your proposed solution using tools like Carrd or Unbounce.
- Gather email sign-ups or feedback to gauge interest.
Phase 2: MVP Development (Days 31-120)
Week 5-8: MVP Design & Planning
- List core features for the MVP.
- Sketch or wireframe the user flow using tools like Balsamiq or Figma.
- Prioritize features based on user feedback and business needs.
Week 9-16: MVP Development & Iteration
- Develop the MVP in 2-week sprints, releasing a usable version at the end of each sprint.
- After each sprint, gather feedback and make necessary adjustments for the next sprint.
Phase 3: Testing & Feedback (Days 121-210)
Week 17-20: Beta Testing
- Launch MVP to a limited audience for beta testing.
- Collect feedback on usability, bugs, and potential improvements.
Week 21-28: Iterative Improvement
- Develop in 2-week sprints, focusing on refining the product based on beta feedback.
Phase 4: Launch & Growth (Days 211-365)
Week 29-32: Pre-launch Marketing
- Engage with your email list.
- Create content (blog posts, videos) related to your SaaS domain.
- Set up social media ads targeting your audience.
Week 33-36: Official Launch
- Announce your SaaS product to the broader public.
- Offer promotional pricing or trials to attract initial users.
Week 37-52: Growth & Iteration
- Continue development in 2-week sprints, adding features, and refining the product based on user feedback.
- Focus on marketing activities: SEO, content marketing, PPC ads, and partnerships.
- Monitor analytics and user behavior to guide decisions.
Throughout All Phases:
- Feedback Loop: Always be collecting and acting on feedback.
- Community Building: Engage with users on platforms like Discord or online forums.
- Continuous Learning: Stay updated with industry trends and competitor movements.
This Agile-based approach allows for continuous feedback and adjustment. It emphasizes learning and adapting over strict planning, which is crucial for a startup in its early stages. While this approach provides a structured guideline, the real world often requires flexibility and resilience. Adjust the plan as necessary based on results and feedback.
Bonus Ways to $100k
When I shared this tutorial with the Finxter community of 150k readers, I received some interesting replies. One reader, let’s call him Jim, shared some interesting gems on how he obtained his first few six figures in wealth:
- Initial Experience with Wealth: Jim’s first encounter with a significant sum of money was through sheer luck, playing slot machines, and participating in a Baccarat training session with only $4 to their name.
- Stock Market: Jim’s second significant gain came from observing stock symbols at a broker’s office during lunch breaks, indicating an interest in the stock market.
- Corporate Games: The third gain was due to a mishap with 401K and the IRS. Jim chose an option that was later deemed inappropriate. However, he received all the taxes, penalties, etc., back and invested them in ADRs and other financial instruments, resulting in a substantial profit.
- Technical Expertise: Jim obtained significant proficiency in programming languages like FORTRAN and COBOL, and experience with various computer systems. This expertise made them a sought-after problem solver in his domain. However, this expertise didn’t necessarily translate to financial gain!
- Value of Curiosity: Jim emphasizes that his driving force was curiosity, not the pursuit of wealth. He used technology to validate his ideas but noted that it didn’t directly lead to monetary gains.
- Keys to Success: Jim believes that a combination of broad interests, education, and initiative are the keys to success. He also mentions the importance of recognizing barriers to entering a desired profession.
- Wealth Among Programmers: Jim notes that he doesn’t know any wealthy programmers, but that doesn’t mean they don’t exist.
- Advice on Earning $100k:
- Focus on what you enjoy and what makes sense.
- Utilize all your skills, not just the ones that seem immediately valuable.
- Don’t get caught up in what others are doing; their path might not be suitable for you.
- Avoid accumulating debt by trying to keep up with trends and popular lifestyles.
- Understand that success often comes after failures; it’s essential to learn from mistakes and focus on fixing them.
- The first $100k might be right under your nose, but you might miss it if you’re always looking elsewhere for opportunities.
Before we move on, feel free to get my book to learn how to create effective coding startups — it contains tips on how to build an MVP, iterate on your solution, build and ship software continuously: 👇
The Art of Clean Code
Most software developers waste thousands of hours working with overly complex code. The eight core principles in The Art of Clean Coding will teach you how to write clear, maintainable code without compromising functionality. The book’s guiding principle is simplicity: reduce and simplify, then reinvest energy in the important parts to save you countless hours and ease the often onerous task of code maintenance.
- Concentrate on the important stuff with the 80/20 principle — focus on the 20% of your code that matters most
- Avoid coding in isolation: create a minimum viable product to get early feedback
- Write code cleanly and simply to eliminate clutter
- Avoid premature optimization that risks over-complicating code
- Balance your goals, capacity, and feedback to achieve the productive state of Flow
- Apply the Do One Thing Well philosophy to vastly improve functionality
- Design efficient user interfaces with the Less is More principle
- Tie your new skills together into one unifying principle: Focus
The Python-based The Art of Clean Coding is suitable for programmers at any level, with ideas presented in a language-agnostic manner.
How to Get Startup Ideas?
Where could you get ideas for such a software-as-a-service (SaaS) business? Check out our Finxter article here:
💡 Recommended: 10 OpenAI SaaS Ideas to Scale a One-Person AI Company
Also, feel free to consider joining the Finxter Academy with dozens of high-value practical exponential tech courses on AI, blockchain dev, and coding, such as this one on prompt engineering:
Prompt Engineering with Python and OpenAI
You can check out the whole course on OpenAI Prompt Engineering using Python on the Finxter academy. We cover topics such as:
- Semantic search
- Web scraping
- Query embeddings
- Movie recommendation
- Sentiment analysis
👨💻 Academy: Prompt Engineering with Python and OpenAI
Jean is a tech enthusiast with a love for AI and machine learning innovations, particularly LLMs. Beyond contributing insightful articles to our blog, Jean has worked as a Python, Rust, and Go coder for one of the leading tech firms in the world.