Before we begin, let me be clear: yes, this is a subjective list. It’s not meant to end the debate — but to start it. These seven papers (sorted by date) stand out to me mostly because of their impact in today’s world. Honestly, each one deserves a blog post (or even a book!) of its own — but let’s keep it short for now. If your favorite doesn’t show up here, don’t worry, stick around for the bonus section at the end, where I’ll call out a few more that came this close to making the main list. So let’s dive in!
1. “On Computable Numbers, with an Application to the Entscheidungsproblem” (1936)
Author: Alan Turing
It’s the 1930s, and a “programmable machine” sounds like something out of a sci-fi novel. Then along comes Alan Turing, laying the groundwork for what computers can theoretically do. He sketches out a hypothetical “Turing Machine,” proving that, if something is computable at all, a machine (in principle) can handle it.
The big idea
Turing’s simple model — just a tape, a head for reading/writing, and a finite set of states, turned into the granddaddy of all modern computation. It defined what’s solvable (and what’s not) in a purely mechanical sense, basically giving us the “rules of the game” for digital problem-solving.
Why it matters today
Every single programming language, every single piece of code out there, is playing by Turing’s rules. Even when we talk about quantum computing, we’re still referencing the boundaries Turing described. That’s a huge testament to the power of one paper published in the mid-1930s.
Learn more
- https://www.cs.virginia.edu/~robins/Turing_Paper_1936.pdf
- https://en.wikipedia.org/wiki/Turing%27s_proof
- https://www.youtube.com/watch?v=dNRDvLACg5Q
2. “A Mathematical Theory of Communication” (1948)
Author: Claude Shannon
Now that Turing showed us what machines can (and can’t) do, how do we actually move information around? Enter Claude Shannon, who basically invented information theory so we could talk about bits, entropy, and noisy channels in a rigorous way.
The big idea
Shannon took the abstract notion of “information” and turned it into something a little bit (pun intended) more measurable. This helped us figure out how to pack data more efficiently (compression) and how to protect it from errors (error-correcting codes), whether we’re sending signals into space or streaming Netflix on a Friday night.
Why it matters today
Every single time you send a text, stream a video, or call your mom on FaceTime, you’re using Shannon’s ideas. Without them, you’d be dealing with a lot more scrambled audio and jumbled data, trust me.
Learn more
- https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf
- https://en.wikipedia.org/wiki/A_Mathematical_Theory_of_Communication
- https://www.youtube.com/watch?v=b6VdGHSV6qg
- https://www.youtube.com/watch?v=kP0zi5lX-Fo
3. “A Relational Model of Data for Large Shared Data Banks” (1970)
Author: Edgar F. Codd
So, we can compute and communicate — awesome. But eventually, we’re buried under mountains of data. Edgar F. Codd saw this coming and introduced the relational model, which is basically the reason we’re able to store and query data.
The big idea
Codd said, “Let’s store data in tables and manipulate it with logical operations.” This might sound obv
