How to Ignore Most Startup Advice and Build a Decent Software Business

How to Ignore Most Startup Advice and Build a Decent Software Business

Video: https://www.youtube.com/watch?v=74AsJ7RET20

It’s a great time to be a software developer. Platforms are steadily becoming more mature, useful tools are released almost daily and things that seemed hopelessly futuristic only a few years ago are suddenly commercially viable. Despite this, the software world is awash with bullshit. The success of the largest technology companies has led to a very skewed set of lessons. This narrow focus is amplified by the venture capital industry and the fact that nobody really knows what’s going to happen next.

The good news is, none of this actually matters. The basics of creating something useful and selling it for money remain the same. In this talk, I’m not going to give you “one weird trick” or tell you to ~* just follow your dreams *~. But I’ll share some of the things we’ve learned from building a successful software company around commercial developer tools and our open-source library spaCy.

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Ines Montani

July 26, 2018
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  1. How to Ignore Most Startup Advice and Build a Decent

    Software Business Ines Montani Explosion AI
  2. Open-source library for industrial-strength Natural Language Processing in Python

  3. Open-source library for industrial-strength Natural Language Processing in Python Company

    and digital studio, bootstrapped with consulting
  4. Open-source library for industrial-strength Natural Language Processing in Python Company

    and digital studio, bootstrapped with consulting First commercial product: radically efficient data collection and annotation tool, powered by active learning
  5. Open-source library for industrial-strength Natural Language Processing in Python Company

    and digital studio, bootstrapped with consulting First commercial product: radically efficient data collection and annotation tool, powered by active learning You are here!
  6. Open-source library for industrial-strength Natural Language Processing in Python Company

    and digital studio, bootstrapped with consulting First commercial product: radically efficient data collection and annotation tool, powered by active learning Extension platform with a SaaS layer to help users scale up annotation projects ANNOTATION MANAGER You are here!
  7. Open-source library for industrial-strength Natural Language Processing in Python Company

    and digital studio, bootstrapped with consulting First commercial product: radically efficient data collection and annotation tool, powered by active learning Extension platform with a SaaS layer to help users scale up annotation projects ANNOTATION MANAGER Coming soon: pre-trained, customisable models for a variety of languages and domains You are here!
  8. The “startup playbook” 
 isn’t the only way. it’s possible

    to be profitable early it’s possible to keep the team small you don’t have to do anything sneaky,
 you can just make something good
  9. You need to run at a loss. MISCONCEPTION #1

  10. Reasons to run at a loss network effects scale operations

    predatory pricing enterprise sales
  11. Bigger isn’t necessarily better. software is more expensive to build

    
 at scale, not less most businesses aren’t “winner takes all” being in a “winner takes all” market 
 kinda sucks anyway
  12. Source: xkcd.com/1827

  13. The good news is: so many opportunities! people are drawn

    to “tournaments” and “winner takes all” markets this leaves many other high-value opportunities untouched optimize for median (not mean!) outcome
  14. You need to hire lots of people. MISCONCEPTION #2

  15. Good teams can be surprisingly small you don’t need to

    pass the “bus test” excellence requires authorship, not redundancy or design by committee building the right stuff matters much more than building lots of stuff
  16. generalists specialists

  17. generalists specialists complementary

  18. T-shaped skills tree-shaped skills

  19. You can’t make good decisions without testing all of your

    assumptions. MISCONCEPTION #3
  20. inverse of survivorship bias: 
 “We didn’t do X and

    we failed, therefore X would have saved us.” “It turned out nobody wanted our product... I wish we’d spent more time validating 
 our ideas! Next time I’m running a 100% 
 data-driven startup!”
  21. 0 % 5 % 10 % 15 % 20 %

    25 % not the right team wrong business model product not a hit no market need outcompeted Top 5 reasons startups fail based on 300 “autopsies” Source: autopsy.io
  22. Source: hyperboleandahalf.blogspot.com

  23. Our company Twitter makes us look clueless and insecure. We

    need to stop retweeting random crap. Do you have numbers to back that up? What? No. Then how do I know you’re right? By thinking?
  24. You can’t replace logic 
 with data. decisive data is

    the exception, not the rule decisions are mostly based on reason you’ll win if you’re mostly right build things you think are good
  25. The true value lies in your users’ data. MISCONCEPTION #4

  26. Prodigy Annotation Tool: prodi.gy $ prodigy ner.teach product_ner en_core_web_sm /data.jsonl

    --label PRODUCT $ prodigy db-out product_ner > annotations.jsonl
  27. 
 Sell products, not promises. fundraising logic: potential > reality

    focus on what you can really charge people money for right now other objectives not worth adding friction and making your product worse
  28. Monetize the money ship value, charge money users appreciate software

    that works users are not interchangeable test subjects,
 they’re people and they remember things profit is the best KPI
  29. Thanks! Explosion AI
 explosion.ai Follow us on Twitter
 @_inesmontani
 @explosion_ai