import torch
import torch.nn as nn
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.primitives.asymmetric import padding
from cryptography.hazmat.primitives import hashes

class PrivateInferenceClient:
    """
    Client-side implementation for private AI inference
    Keeps sensitive queries encrypted and processed locally
    """
    
    def __init__(self, model_path: str):
        # Load model locally - never sends raw data out
        self.model = torch.load(model_path)
        self.model.eval()
        
        # Generate client-side encryption keys
        self.private_key = rsa.generate_private_key(
            public_exponent=65537,
            key_size=2048
        )
        self.public_key = self.private_key.public_key()
    
    def encrypt_query(self, query: str) -> bytes:
        """Encrypt query before any external processing"""
        encrypted = self.public_key.encrypt(
            query.encode(),
            padding.OAEP(
                mgf=padding.MGF1(algorithm=hashes.SHA256()),
                algorithm=hashes.SHA256(),
                label=None
            )
        )
        return encrypted
    
    def local_inference(self, input_data):
        """Process sensitive data entirely on-device"""
        with torch.no_grad():
            # All processing happens locally
            output = self.model(input_data)
            
            # Decrypt results if they came encrypted
            if isinstance(output, bytes):
                output = self.private_key.decrypt(
                    output,
                    padding.OAEP(
                        mgf=padding.MGF1(algorithm=hashes.SHA256()),
                        algorithm=hashes.SHA256(),
                        label=None
                    )
                ).decode()
            
            return output

# Usage example:
client = PrivateInferenceClient("local_model.pt")
sensitive_query = "therapeutic mushroom dosage for anxiety"
result = client.local_inference(sensitive_query)
print(f"Private result: {result}")

The Privacy Panic: From 'Nothing to Hide' to 'Hide Everything'

Remember when we all collectively shrugged and said 'I have nothing to hide' as Facebook monetized our childhood photos? Good times. The AI era has refined that panic into a sharper, more specific dread: the fear that our search for 'unusual rash pictures' or 'how to write a breakup email to my co-founder' is being logged, analyzed, and potentially used to train a model that will one day judge our grandchildren.

Enter Moxie, the tech industry's cryptographer-in-chief. Having already given the middle finger to telecom metadata and unencrypted SMS, he's now looking at the AI inference stack—the process where your prompt hits a company's server and magic happens—and seeing a buffet of personal data. As his blog post on 'Private Inference' outlines, the standard model is a privacy nightmare. Your queries are the new oil, and every AI company is a tiny, digital ExxonMobil.

How It Works (Or, How to Confuse a Server)

Confer's proposed magic trick is called 'private inference.' In overly simplistic terms (the only kind I understand), it uses fancy cryptography—likely homomorphic encryption or secure multi-party computation—to let your device send an encrypted prompt to the AI model. The model then does its work on this encrypted gibberish, producing an encrypted gibberish answer, which only your device can decrypt.

The server running the AI model is like a chef preparing a meal blindfolded, with mittens on, not knowing if they're making a salad or a soufflé. They do the work, but they never actually see the ingredients or the final plate. It's brilliant, computationally expensive, and the exact opposite of 'move fast and break things.' It's more 'move carefully and encrypt things.'

The Business Model Conundrum: Who Pays for the Blindfolded Chef?

Here's where the sarcasm gets a workout. The entire AI-as-a-service economy is predicated on a simple trade: you get (sometimes) useful answers, they get your data to improve models and target ads. Confer proposes a different deal: you pay for the compute, and they get... nothing? Well, presumably they get your money. But in Silicon Valley, user data is often considered more valuable than user currency. It's a more fungible, creepy form of capital.

So, Confer's real innovation might be asking users to open their wallets instead of their data vaults. A radical concept! It's like a paid search engine from 1998. Whether enough people value privacy over free access to a slightly dumber, more politically correct version of GPT-4 remains to be seen. My guess? The Venn diagram of 'people who care about encryption' and 'people who want AI to write their D&D campaign' has a significant overlap, so there's hope.

The 'Signal for AI' Pitch: A History of Uphill Battles

Moxie didn't just make Signal; he spent a decade dragging the entire messaging industry, kicking and screaming, toward encryption. He watched as WhatsApp, iMessage, and even Facebook Messenger eventually adopted Signal's protocol, often while giving him zero credit. It was a quiet, infrastructural revolution.

Confer seems to be the same playbook, but for AI inference. The goal isn't necessarily to become the dominant AI chat app (though that would be nice). It's to build the protocol and prove that private inference is possible. Then wait for the giants—OpenAI, Google, Anthropic—to eventually adopt it, or a variation thereof, once the regulatory pressure or user demand gets too loud to ignore. He's not selling a product; he's selling a future standard. And he's doing it while the rest of the industry is still trying to figure out how to make an AI that doesn't suggest adding glue to pizza.

The Absurd Questions We Can Finally Ask in Peace

Let's dream for a moment. With Confer, what previously taboo queries become fair game? The mind boggles:

• Medical Mayhem: "Describe all symptoms of a disease that would get me three weeks off work, but not require hospitalization."
• Creative Desperation: "Write a Tinder bio in the voice of a haunted Victorian teapot. Make it seductive."
• Corporate Espionage (Lite): "Draft an email to my boss subtly taking credit for Brian's work. Use corporate jargon like 'synergize' and 'leverage.'"
• Existential Dread: "If I trained an AI solely on my own text messages, would it agree that my life is a series of minor disappointments leading to a major one?"

Finally, we can use AI for its intended purpose: being our weird, unfiltered, confidential id. No more sanitizing prompts for the server logs.

The Inevitable Pushback: "But How Will We Improve the Models?!"

Expect to hear this whine from every AI CEO not named Moxie. "If we can't see the prompts, how will we find and fix biases? How will we improve safety? How will we know what users really want?"

To which the privacy-aware response is: "The same way you improve any other tool—through opt-in feedback, controlled studies, and not by constantly eavesdropping on every interaction." It's a shift from surveillance-based development to something resembling... ethics. A novel approach! It might even force AI companies to be more transparent about their models' capabilities upfront, rather than secretly patching them based on our embarrassing midnight queries.