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Jack Raines says that by summer 2025, nontechnical users could already build…

2026-01-26 · 16:04 UTC ·Jack_Raines ·4 min read

Brief

Jack Raines frames AI coding assistants as both a practical productivity tool and a career necessity, describing how he experimented with Cursor and Claude Code despite having no prior experience with APIs or Python. His examples—a cross-platform contact rolodex and an email-based newsletter translator—support his broader claim that LLMs have made software creation accessible to nontechnical users. He argues coding was the first breakout enterprise application because code offers huge training datasets and clear correctness signals, and he extends that logic to spreadsheets and financial modeling, where formulas and references can also be checked systematically. The key shift, in his view, is productization: the technical building blocks existed as early as 2023-2024, but embedding a chat interface directly inside Excel removes the integration burden from users. His Series A waterfall example, completed in 4 minutes after about 10 refinement prompts, illustrates how AI can compress a 25-30 minute modeling task while shifting human effort from construction to verification.

Why it matters

Jack Raines says that by summer 2025, nontechnical users could already build useful software with AI coding tools: he used Cursor to create a contact rolodex merging Twitter, LinkedIn, phone, and email data, and used Claude Code to build an email-based Spanish newsletter translation service at translate@translatemynewsletter.com.

Key details

  • He argues coding became the first major enterprise LLM use case because models had massive training corpora of code plus an objective pass/fail structure, making outputs easy to validate; he claims coding assistants were already good enough in June 2025 despite clunky terminal-based UX in Claude Code.
  • Raines identifies Excel and financial modeling as the next strong AI workflow because spreadsheets also have formulaic right/wrong states, abundant examples, and repetitive human effort; he notes the enabling pieces already existed earlier, including Microsoft’s Office JavaScript API, Claude’s API around 2023, and Anthropic tool-calling additions in 2023-2024.
  • In a concrete Excel test, he asked Claude inside Excel to build a Series A waterfall for a $50 million exit with 1x non-participating liquidation preferences, placed starting at cell J25; after roughly 10 follow-up prompts, the model produced the analysis in about 4 minutes versus an estimated 25-30 minutes manually.
  • He concludes that AI sharply reduces the value of entry-level spreadsheet execution skills in investment banking and consulting, and may compress the market for standalone 'AI for Excel' or 'AI for finance' startups as foundation models integrate directly with Excel and external data providers.
Source evidence

title: @JackRaines: Over the summer, I spent a lot of time messing around with Claude Code and Curso...
author: JackRaines
contenttype: twitterarticle
published: 2026-01-26T16:04:19+00:00
sourceurl: https://x.com/JackRaines/status/2015818088898301980

word_count: 994

Over the summer, I spent a lot of time messing around with Claude Code and Cursor. The reasons for t

Over the summer, I spent a lot of time messing around with Claude Code and Cursor. The reasons for that were three-fold:

  • If you’re investing in early-stage startups, knowing the current capabilities of large language models is table stakes. Coding assistants were the most developed LLM “products” that had seen skyrocketing adoption, it would be ignorant not to test them out myself.

  • AI coding assistants had made simple software creation and computer data manipulation accessible to someone “nontechnical” like myself for the first time in history.

  • I thought there was real career risk to not knowing how to use these tools, and, similarly, real career advantages in getting up to speed quickly.

The first thing I built, in Cursor, was a rolodex that consolidated all of my contacts across Twitter, Linkedin, phone contacts, and email. I had no idea what I was doing, initially. I had never used API keys or even written a simple python script, so it took hours for me to get used to the Cursor’s IDE interface. But, after a weekend of hacking away and troubleshooting with ChatGPT, it worked. I still use that rolodex.

The second thing I built, with Claude Code, was a translation tool allowing me to forward any newsletter/blog to “translate@translatemynewsletter.com” and receive a Spanish translation of that newsletter in my inbox a few moments later.

The UX, particularly for someone who had not previously coded, wasn’t great in June 2025, especially with Claude Code, because I had to access it through my computer terminal. But it worked. And coding assistants have only continued to improve; to the point that employees at the big AI labs aren’t even “ writing ” code anymore. AI can just do it.

Why is AI so good at coding, and why was coding the first real “enterprise” use case that took off? At risk of grossly oversimplifying this whole thing, it’s because there were virtually unlimited troves of coding data to train models on, and code has objective “right” and “wrong” structures. Miss a parenthesis in a python script, and it won’t run. Get the structure right, it runs. Train on billions (trillions?) of examples of code, and suddenly AI can write code quite well. Humans stopped writing code in 2025. Yes, sure, it takes time for this trend to flow through the full economy, but coding assistants are the worst they’ll ever be, and they’re actually quite good.

My thought, at the time, was “What comes next?” meaning what other computer-based workflows have billions of examples of “correct” and “incorrect” scripts that can be verified formulaically, have millions (or more) of users, and subject users to tedious time-sucks as they seek to get these formulas correct. Financial modeling (and Excel use, more broadly) was the next low-hanging fruit. The real issue wasn’t technical capabilities. Microsoft’s Office Javascript API has been around for a while, Claude’s API went live in ~2023, and Anthropic added tool calling functions in 2023 and 2024, so you could have “built” this functionality a year or two ago. But the product wasn’t there yet. Coding assistants blew up so quickly because 1) there was a lot of training data and 2) a “right” and “wrong” state of code, but also because the UX just didn’t matter as much. Accessing Claude Code through the terminal wouldn’t have intimidated a software developer because… they were used to that interface. But (and this is ignoring compliance issues) no second-year JPMorgan IB associate is going to tie together the APIs needed to build an automated financial modeling tool. The tech was already there, the product wasn’t. But now? A chat window within Excel opens on the right side of the screen, and you can ask Claude to manipulate, analyze, and create anything you want directly in Excel.

A few months ago, I would go back and worth with ChatGPT on figuring out the right Excel formulas to do XYZ thing. I would upload screenshots of spreadsheets, it would give me a formula, I would paste it, it might work, and we would iterate until it was done. It “worked,” but the process was clunky. Now? The AI can just iterate directly in Excel in real time.

For example, I uploaded a sample Series A Pro Forma and gave Claude the following prompt:

“Create a waterfall showing how the funds will be distributed on a $50 million exit, assuming 1x non-participating liquidation preferences from all VCs. Generate your graphic in Cell J25 and work right / down from there.”

After ~10 back and forth messages to tweak formatting, remove hardcoded cells/only reference cells in the model, etc, it spit out the following waterfall analysis. The whole thing took about four minutes.

This is, obviously, a simple example, but it still cut a ~25-30 minute task down to ~4 minutes, and actually freed up more time to spot check that the right cells were referenced, inputs weren’t hard-coded, etc. Basically, you can get to a testable final product much, much faster.

The value of “modeling skills,” at least defined as the “ability to do Excel fast,” drops to zero when you can ask AI to build a fully-fleshed out version of the thing faster than a human can type, taking out the “value” of a lot of entry-level white collar labor (IB analysts, consultants, etc) as well, at least as the work is currently done. This also, by the way, probably kills a lot of the “AI for Excel / AI for finance” startups that popped up over the last few years, particularly as Claude continues to integrate with data providers. One of my banker buddies thought the same:

My next question is what are the other “financial modeling” or “coding” pursuits that have 1) logic that must be followed to generate outputs and 2) a lot of training data to pull from? Protein synthesis? Something else? Idk. But this is only going to happen faster and faster.

Posted: 2026-01-26T16:04:19.000Z

Engagement: 325 likes, 21 retweets, 16 replies