Bonus content at the end: Claude code with Kimi K2.5 for 0$ Locally.

At 2:13 a.m. yesterday, my AI agent did something it had never done before.

It failed.

And then, without asking me for permission, without apologizing, and without burning a hole in my credit card… it tried again.

It didn't just retry the same prompt. It read the error log. It realized it had hallucinated a file path. It wrote a script to check the directory, found the correct path, updated its own code, and ran the build again.

I didn't wake up to a broken error message. I woke up to a finished project and a boring log file:

"Task failed at Step 4. Diagnosed root cause. Implemented fix. Verified. Task complete after 17 iterations."

If you are non-technical, this might sound like a small detail. If you are an engineer or a founder, you know this is a miracle.

Because until January 2026, allowing an AI to run for 17 iterations wasn't a strategy. It was a bankruptcy filing.

We have spent the last three years treating AI like a slot machine: one prompt (pull the handle), one answer (hope for a jackpot). But the era of the "One-Shot" AI is over.

The era of the Infinite Loop has begun. And it is being driven by a brutal economic shift that the tech giants are terrified to talk about.

Here is why your relationship with AI is about to change forever.

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The "Smart But Expensive" Trap

To understand why 2026 is different, you have to understand the "OpenAI Tax" we all lived under from 2023 to 2025.

For years, the industry operated on a simple, unspoken rule: Intelligence is expensive.

If you wanted the best results, you used the biggest models (GPT-5.2, Claude 4.5 Opus). These models were brilliant, but they charged you by the token. Every word they read and every word they wrote cost money.

This created a perverse incentive structure for everyone — from freelance developers to Fortune 500 CTOs.

We optimized for brevity, not quality.

We spent hours crafting the "perfect prompt" to get the right answer on the first try, because we couldn't afford to let the model experiment.

Imagine hiring a brilliant Harvard-educated consultant, but telling them: "I will pay you $500 per sentence. So please, don't brainstorm. Don't draft. Don't think out loud. Just give me the final answer immediately."

That consultant would fail. They would give you a shallow, safe answer. They wouldn't take risks, and they certainly wouldn't double-check their work, because double-checking costs extra.

That is how we have been using AI.

We built "Agents" — AI systems designed to perform multi-step tasks — but we crippled them. We gave them budgets so tight that if they hit a single snag, we killed the process.

We wanted Agents, but we could only afford Chatbots.

The Day the Price Collapsed

Then came the opening weeks of 2026.

Two specific things happened that broke the "Smart = Expensive" equation.

First, DeepSeek V3.2 stabilized its pricing and performance. We are looking at a model that rivals top-tier proprietary systems but costs pennies on the dollar. Specifically, input tokens are effectively free compared to 2025 standards.

Second, Moonshot AI released Kimi k2.5, an open-source model explicitly architected for "agent swarms"

The specs on these models are technical, but the implication is economic.

DeepSeek and Kimi didn't just lower the price. They lowered it so much that the marginal cost of "thinking" dropped to zero.

When a resource becomes free, you stop rationing it. You start wasting it.

And "wasting" compute is the secret to agentic intelligence.

The "Raffle Ticket" Theory of Intelligence

Why does cheap compute matter more than a higher IQ?

Because solving hard problems — writing code, researching legal precedents, designing a marketing funnel — is probabilistic.

Even the smartest human (or AI) might miss a detail on the first try.

In the old world (Closed AI), you paid $10 for one "High IQ" attempt. You had one ticket to the raffle. If the model hallucinated, you lost.

In the new world (Open Source 2026), the model might technically be 5% "dumber" on paper. But because it runs locally or on ultra-cheap APIs, you can afford to buy 1,000 tickets for the same price.

You can let the AI:

  1. Write the code.
  2. Write a test for the code.
  3. Fail the test.
  4. Rewrite the code.
  5. Repeat this loop 50 times.

Statistically, the "Infinite Loop" method crushes the "One-Shot Genius" method every time.

Kimi k2.5 was built for exactly this. It features a Mixture-of-Experts (MoE) architecture that activates only a fraction of its parameters (32B out of 1T+) for each step. It's designed to run fast and cheap.

It allows us to brute-force quality through persistence.

For the Engineers: The "Vibe Check" is Dead. Long Live the Unit Test.

If you are a developer, you know the pain of "Vibe Coding."

You generate a script with Claude or GPT. You look at it. It looks right. The vibes are good. You paste it. It crashes.

The problem wasn't the model's coding ability. The problem was the lack of a feedback loop.

With models like DeepSeek V3.2 and Kimi k2.5, we are moving from "Generative AI" to "Verifiable AI."

I recently switched my personal coding workflow. Instead of asking the AI for code, I ask it for a Loop.

"Write a Python script to scrape this site. Then, write a test to verify it gets the data. Run the test. If it fails, read the error, rewrite the script, and try again. Do not talk to me until the test passes."

In 2024, that prompt would have cost me $4.00 in API credits and might have timed out. In 2026, running that loop on a localized Kimi or DeepSeek instance costs me fractions of a cent.

The model doesn't need to be perfect. It just needs to be stubborn.

For the Founders: Your Margins Just Exploded

If you are building an AI product, this shift is your Series A.

For two years, the biggest risk to any AI startup was unit economics. "Cool demo, but how do you make money when every user query costs you $0.15 in OpenAI fees?"

The "Infinite Loop" changes the math.

  1. Privacy is now a feature, not a hassle. Clients are paranoid about data. With models like Qwen Image Edit (which challenges Google's Nano Banana) and local versions of Kimi, you can deploy the "brain" inside the client's own cloud. No data leaves their building. That is a sales closer.
  2. The "Intern" vs. The "Consultant." Stop trying to sell your customers a "Super Genius AI." Sell them a "Tireless Intern." Sell them a system that will try 100 times to get the right answer while they sleep. Reliability sells better than IQ.
  3. The Moat is the System, Not the Model. If everyone has access to cheap, smart open-source models (and they do), having the "best model" is no longer a competitive advantage. The advantage is in Flow Engineering. Who can build the best loop? Who can build the best error-checking scripts? Who can curate the best context? The value has moved from the Engine (the LLM) to the Car (the application).

Bonus: How to Run This Setup for $0 (The "Ollama" Hack)

The biggest barrier to "Infinite Loop" coding used to be tooling. The best agentic tool right now is Claude Code (Anthropic's CLI agent), but by default, it locks you into the expensive Opus 4.5 or Sonnet 4.5 models.

If you let Claude Code run an infinite loop on Opus 4.5, you will owe Anthropic a small fortune by morning.

But thanks to the new ollama launch command (released Jan 2026), we can swap the engine. We can keep the Ferrari (Claude Code interface) but put a nuclear reactor (free Kimi k2.5) inside it.

Here is how to set up your own infinite loop agent for free:

Method 1: Ollama

Install Ollama (macOS) and sign in:

brew install ollama

ollama signin

or visit https://docs.ollama.com/quickstart

Step 1: Get the Model

First, pull the "cloud" version of Kimi k2.5. This is the quantized version optimized for consumer GPUs (works on Mac M3/M4 or NVIDIA 40-series).

ollama pull kimi-k2.5:cloud

Ollama's cloud policy is generally more privacy-focused than public web ChatGPT, though you should verify their latest data retention policy for cloud models.

Step 2: launch Ollama Server

ollama serve

Step 3: The Magic Command (Another Terminal)

Instead of running Claude Code directly (which defaults to Anthropic's API), use Ollama's new launcher to wrap it. This tells Claude Code to treat your local Ollama instance as the API endpoint.

ollama launch claude --model kimi-k2.5:cloud

Note: This works because Ollama now natively shims the Anthropic API structure. Claude Code thinks it's talking to the cloud, but it's talking to localhost.

Method 2: The Cloud Hybrid (Kimi API)

If your laptop is too slow, you can use Moonshot's API directly (which is significantly cheaper than Anthropic's).

  1. Get your API key from the Moonshot/Kimi platform.
  2. Export the base URL and Key before running Claude Code:
# Set these in your shell (e.g., ~/.bashrc, ~/.zshrc)
export ANTHROPIC_BASE_URL="https://api.moonshot.cn/v1"
export ANTHROPIC_AUTH_TOKEN="sk-your-kimi-key"
export ANTHROPIC_API_KEY="" # Important: Must be explicitly empty

Why this matters: Once you do this, you stop hesitating. You can type /loop fix this bug into your terminal and walk away. The agent will spin up, write code, fail, retry, and fix it—and it won't cost you a cent.

You can use OpenRouter for more models (free or paid) by just replacing the base URL and the api key (AUTH_TOKEN).

The Future: From Prompting to Management

What does this mean for you, sitting at your desk today?

It means the skill set is changing. Again.

We spent the last few years learning to be Prompters. We learned to whisper the right incantations to the machine.

Now, we need to learn to be Managers.

You don't whisper to an Infinite Loop agent. You give it a goal, a set of tools, and a criterion for success. And then you walk away.

Your job is no longer to write the perfect request. Your job is to evaluate the output.

The proprietary models — the GPTs and Claudes — will always have a place. They will be the "Senior Partners" we call in for the hardest, most novel problems.

But for the daily grind? For the coding, the data cleaning, the drafting, the summarizing?

The open-source swarms have won.

The thinking isn't just cheaper. It's free. And when thinking is free, the only limit is how many loops you are willing to run.