Rafath Mirza

Building the future of e-commerce @Shopify 💚

[Rafath Mirza @rafathsweb]

Yooooo we just migrated all our Custom Discounts on the Kite App to Rust 🫶

Took some serious effort. Our old JS setup carried quite a bit of weight and had some pretty complex logic we’d built up over time to handle all kinds of unique discounting needs we heard from our merchants.
But damn - Rust was so worth it. We’re already seeing a 5x performance boost!

Only good things from here 💚
@nick_wesselman @dave_cameron

[Neeti @neets_skailama]

we’ve re-engineered Shopify discount functions within Kite, to now run on Rust. With this, we launch Custom Discounts 2.0. What does this mean for Kite users?

→ enhanced control on discount campaigns
→ rock-solid stability
→ blazing-fast scalability- manage super high volumes
on all 'custom discount' campaigns. Whatever the scale.

Here’s to a more powerful discount engine. 💪

special special mention: @rafathsweb and @rohhan36 who’ve worked day in, day out to make this happen @ShopifyDevs

[Boring_Business @BoringBiz_]

All of my smartest friends are either

> doubling down on AI and starting companies to create generational wealth as soon as possible

> taking their money to buy piece of land in the middle of nowhere and walking away from society as a whole

Nothing in between

[Rafath Mirza @rafathsweb]

I’ve always only overworked but this years been like never before

[Thom van Lieshout @thomvlieshout]

guys be honest, are you working less because of AI

because I'm working more than ever

[Rafath Mirza @rafathsweb]

I miss getting bullied on stack overflow 😪

[Michal Malewicz @michalmalewicz]

AI era is officially done. A handful of people will afford good models, back to stack overflow for the underclass.

[kache @yacineMTB]

LLMs are so ludicrously bad at doing anything novel or making any decisions. It's remarkable. I can't believe how far we've gotten on making something useful without actually.. solving it

[Raunak @caps_raunak]

I'm done with this AI bullshit.

You spend 10 years becoming a coder, fixing bugs at 3am. Some bot copies millions of people's code and does your job in seconds.

Everyone clapping "Wow genius!!" We are literally paying companies to replace us. Smartest way to end human value.

[Rafath Mirza @rafathsweb]

@atmoio Damn

[Rafath Mirza @rafathsweb]

“Implementing AI into your business is not supposed to generate productivity gains right away, and I think a lot of people completely miss this point. If anything, AI is a technology that takes much longer than others to show any real ROI for an enterprise”

[Boring_Business @BoringBiz_]

Regardless of what people say, I genuinely believe that we have not yet seen any real productivity gains from AI at the enterprise level

Almost all of the efficiency gains and layoffs that you see from companies are a result of overhiring over the last few years

These two letters, "AI," have become the scapegoat term for a lot of CEOs and CFOs who want to cut their budgets without admitting that they messed up

While some people agree with this and point to it as a bearish case for AI, I think the opposite is actually true

Here is why

Implementing AI into your business is not supposed to generate productivity gains right away, and I think a lot of people completely miss this point. If anything, AI is a technology that takes much longer than others to show any real ROI for an enterprise

Let's walk through an example to show what I mean

When you implement AI into a business, it has zero context around work processes on day 1. It doesn't know what the typical template format of the business looks like. It has no idea what a "client-ready" deliverable is supposed to look like from your company

At this stage, you still need employees to use this tool in their daily workflow and teach it how to add value to the business. Employees can pick up small efficiency gains in repetitive tasks, but the work needs to be continually audited or edited to turn it into a "finished product."

As employees implement it and usage increases, the AI gets to pick up context and stylistic preferences that are specific to the business. After enough time, that same tool learns to feed this information to become incrementally more useful each time

The two key pillars of this are (i) custom prompts built by employees who want to solve for a specific pain point in the business, and (ii) skills built by employees who are looking to automate an entire task from end-to-end

This is just the baseline today. As the models get better, all of the top platforms will add new features to help capture this process. In fact, many new startups are already budding today that aim to tackle this exact problem of extracting enterprise workflow knowledge

As this happens, AI startups to take over entire processes from humans and actually builds the "client-ready" finished products that can replicate an entire job.

And I think, this is when we will finally see a massive uptick in real AI-led productivity gains

As of today, 99% of companies are simply not there yet. Most businesses are just beginning to onboard AI tools. Others have just onboarded and have not yet built up that enterprise knowledge inside of their tools

I think the simple bottom line is that this technology is just getting started

[Ed Zitron @edzitron]

Da future of software baby

[Rafath Mirza @rafathsweb]

@Bloke_Baz + you work in IT

[Vivo @vivoplt]

We are in a weird period right now.

- Juniors can't get hired because "we need experience."
- Seniors are getting laid off because "AI can do it cheaper."
- Mid levels are doing the work of 2 people.

And somehow every company is still "struggling to find talent."

[Rafath Mirza @rafathsweb]

@Palak3312 C

[Govind @Govindtwtt]

we are being gaslit about AI on a societal level. Everybody is vibe coding but I haven’t seen one useful thing get produced. Everybody has agents doing something but nothing useful is getting done. Cool you had AI summarize a PDF and make a template. Nice

[Richard Artoul @richardartoul]

i'm on the verge of giving up on LLMs for code generation

they're game changers for code review, writing tests, and debugging, but I'm starting to think the juice isn't worth the squeeze for the actual code writing 95% of the time

[Tom Blomfield @t_blom]

Imagine replacing 90% of your employees with a team of geniuses who have no idea how your company operates.

Total chaos. Nothing works.

That’s what AI feels like today.

The missing piece is extracting all the domain knowledge from people’s heads and providing that as structured context to the models.

[terminally onλine εngineer @tekbog]

people misunderstand what AI does
it accelerates everything
even incompetence

[Rafath Mirza @rafathsweb]

@theo This!!!!!! Let’s also talk about how it goes into a loop for hours to “research” and implement absolute slop and when it doesn’t work just says “you’re absolutely right, I’m sorry” like bro you didn’t just betray me like that with no consequences

[Rafath Mirza @rafathsweb]

@blakeaburge Easier said

[Rafath Mirza @rafathsweb]

I’ve never related to anything more before

[Jackson Atkins @JacksonAtkinsX]

My current experience with coding models.

[Ricardo @Ric_RTP]

Microsoft just banned its own engineers from using AI.

The tool was literally costing MORE than the humans it was supposed to replace.

They lied to you about AI adoption and now the whole narrative is blowing up:

Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it.

Engineers loved it and adoption exploded. But then the invoices arrived.

Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead.

The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much.

Uber's story is even worse...

Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April.

Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems.

Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session.

The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money.

Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote:

"For my team, the cost of compute is far beyond the costs of the employees."

This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans.

Think about what this means for the entire AI narrative.

Every CEO on every earnings call for the past two years has said the same thing:

AI will make us more efficient, reduce headcount, and cut costs.

The stock market rewarded every company that said it.

Fired workers, stock goes up. Announced AI adoption, stock goes up.

But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill.

Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools.

Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible.

Both companies are spending hundreds of billions on AI infrastructure this year alone.

And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control.

The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP.

This is the gap nobody on Wall Street is pricing in.

$725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work.

What do you think?

[Corey Quinn @QuinnyPig]

Starbucks ran an AI inventory system for nine months before shutting it down because it couldn't actually count or label items.

Letting a hallucinating model dictate physical supply chain orders for three fiscal quarters? This seems production grade.