Data shows that in the past month, Apate’s network of 200,000 AI ‘victims’ has logged over 1.7 million instances of profanity from scam callers. That’s the KPI: not dollars saved, not arrests made, but the sheer volume of insults hurled at a machine. The chain never lies, only the observers do. And here, the observers are the fraudsters themselves, screaming into a digital void.
Context: The Scam Baiting Industrial Complex
Scam baiting has existed for decades. Lone vigilantes waste scammers’ time, record calls, and post them for entertainment. But the scale is microscopic. A human bait can handle maybe 20 calls a day before burnout. Apate, a company with roots in the Web3 security space, claims to have automated the entire process. Deploying 200,000 AI agents that simulate potential victims — confused, anxious, sometimes angry — they aim to keep scammers occupied indefinitely. The company’s internal metric, according to a leaked pitch deck, is a monthly “swear word KPI” measuring how often the AI is cursed at. The logic: if you make the scammer angry, they stay on the line longer, wasting more of their resources.
This is not a charity. Apate is a for-profit entity. Its target customers are banks, telecoms, and government anti-fraud agencies. The pitch is simple: pay us a subscription, and we’ll deploy a swarm of AI victims that tie up your enemy’s phone lines. The data collected — voice prints, IP addresses, bank accounts — can be sold back to law enforcement. It’s a business model built on the back of deception, designed to fight deception.
Core: Systematic Teardown — The Math Behind the Madness
Let’s dissect the numbers. I’ve spent the last decade auditing on-chain systems and tokenomics, and this deployment screams a familiar pattern: unsustainable unit economics dressed as innovation.
The Inference Cost Problem
Running 200,000 concurrent LLM-based agents is not cheap. Assuming each conversation averages 10 minutes and generates 200 tokens per minute, that’s 2,000 tokens per session. At current market rates for inference (using a mid-tier model like Llama 3 70B, with a cost of roughly $0.002 per 1,000 tokens), each session costs $0.004. For 200,000 concurrent sessions, that’s $800 per hour. Over a month, that’s over $576,000 in inference costs alone. This does not include storage, network bandwidth, or the cost of fine-tuning the models on scam dialogue data.
Apate claims to have optimized using quantization and speculative decoding, but even a 50% reduction still leaves a monthly burn rate of nearly $300,000. Where is the revenue? The company has not disclosed any paying customers. The pitch deck suggests a $50,000 per month enterprise subscription for 10,000 AI victims. To break even on inference alone, they would need at least 6 such clients. That’s a tall order in a market where most anti-fraud budgets are still spent on traditional call centers.
The Data Flywheel — And Its Flaw
The bulls point to the data flywheel: more conversations generate more scam data, which improves the AI, which attracts more customers. This is theoretically sound. But it ignores the fact that scam dialogues are a commodity. Every scam baiting channel on YouTube already has thousands of hours of recordings. Large language models are trained on public data; the marginal value of additional scam conversations diminishes rapidly. The true competitive advantage would be the ability to generate realistic, diverse victim personas — but that requires a constant stream of new scam techniques, which are evolving faster than any single company can keep up.
Impermanent loss is not luck; it is mathematics. In DeFi, we saw how high yields attract liquidity but also attract hacks. Here, the high “yield” of wasted scammer time is funded by investor capital. The moment the capital dries up, the entire operation collapses. The KPI of curses is a vanity metric, not a business metric.
Contrarian: What the Bulls Got Right
To be fair, the bulls have a point. The concept is novel, and the execution is impressive. Apate has built a real-time, multi-agent system that can hold coherent conversations in multiple languages. That is a technical achievement. If they can secure a few large government contracts, the unit economics could flip. Governments are willing to pay for national security, not per-call efficiency. A single contract with a European police agency could cover a year of inference costs.
Moreover, the emotional impact of making scammers angry is real. Studies show that angry callers are less effective and more likely to make mistakes. By provoking them, Apate’s AI may actually reduce the number of successful scams. There is a plausible case that the system has a net positive effect on society, even if the financial model is shaky.
Takeaway: Accountability Call
The chain never lies, but the ethics do. Apate’s approach raises uncomfortable questions: Is it legal to deceive a scammer? In many jurisdictions, yes — but only if the victim is the scammer themselves. However, what if the AI accidentally harasses an innocent person? What if the data collected is used for purposes beyond scam baiting? The company has no published ethics policy, no oversight board, and no published data on false positives. The silence is deafening.
