I remember the first time I saw a scam baiting video. It was 2021, and I was deep into the Bored Ape Yacht Club cultural arbitrage, running five data scrapers to track wallet-to-influencer links. The video showed a guy wasting a scammer’s time for hours, pretending to be an elderly grandmother. It was funny, but it was also manual, inefficient, and impossible to scale. Fast forward to 2025, and Apate claims to have deployed 200,000 AI-powered fake victims, each one a digital grandmother, a confused teenager, a nervous investor, all designed to keep scammers on the line. And their key performance indicator? The number of times a scammer curses at the bot. Monthly swear word KPI. That’s the metric they’re selling to the world.
It’s a brilliant hook. In a bull market where every project is chasing TVL, users, or trading volume, Apate introduces a completely new unit of measurement: anger. The angrier the scammer, the more effective the AI. It’s narrative-first quantification at its most literal. But as someone who spent 2017 launching three Twitter accounts to track sentiment shifts around Golem and Status, I’ve learned that the most seductive metrics are often the most dangerous. Let’s peel back the layers.
Context: From Manual Scam Baiting to AI Agent Armies
Scam baiting has a long history. It started as a grassroots hobby—people like Jim Browning or Kitboga would spend hours on the phone with fake IRS agents, wasting their time and recording their methods. In crypto, the community adopted similar tactics during the ICO boom, creating fake “whale” accounts to lure phishing scammers. But the scale was always limited by human bandwidth. One person could only handle one call at a time.
Then came the AI agents. In 2023, we saw the first wave of chatbot-based scam baiters, often using GPT-3.5 with simple prompts. They were clunky, easily detected, and could only handle a few dozen conversations. By 2024, with the rise of agentic frameworks and cheaper inference, the dream of mass deployment became plausible. Apate appears to be the first to claim 200,000 concurrent instances. That’s not just a technical leap—it’s a narrative leap.
From the chaotic ICOs of 2017 to the structured liquidity of today, we’ve seen narratives evolve. Apate’s 200k victims is a new chapter. The journey from 2017’s community coin frenzy to the structured liquidity of today has taught me to question metrics that sound too good. But before we dive into the core, we need to understand the context of Apate’s announcement. The article was published on a blockchain/Web3 news outlet, likely a PR piece aimed at attracting venture capital and institutional clients. The target audience is not scam victims—it’s investors and government agencies tired of losing billions to fraud. The swear word KPI is designed to be visceral, to make you think, “Wow, this is real.”
Core: The Architecture of a Narrative Trap
Let’s get technical. 200,000 concurrent AI victims means 200,000 independent LLM inference threads running simultaneously. Each thread must manage a conversation history, adapt to the scammer’s tactics, and generate responses that are both believable and engaging enough to keep the scammer on the line. The compute cost is staggering. Based on my experience auditing DeFi protocols, I’ve seen how easily projections can be gamed. The same applies here.
Assume each conversation averages 10 minutes, with a token generation rate of 50 tokens per minute (a conservative estimate for a small open-source model like Llama-3-8B). That’s 500 tokens per conversation. For 200,000 conversations, that’s 100 million tokens per 10-minute window. At current inference costs on a high-end GPU like an H100, that’s roughly $0.000002 per token for a small model, or $200 per 10-minute batch. That’s $1,200 per hour, $28,800 per day, $864,000 per month. And that’s assuming a small model. If they use a larger model for better quality, costs could easily be 10x higher.
This is where the narrative trap deepens. The swear word KPI is a perfect marketing device because it’s easy to understand and emotionally resonant. But it’s also easy to game. If the AI victim is programmed to be deliberately provocative—say, by responding with “I’m not giving you my credit card, you’re a scammer!”—then the scammer will naturally curse. The KPI becomes a measure of the AI’s rudeness, not its effectiveness. It’s the same lesson I learned in 2020 during the Uniswap V2 liquidity mining experiments. I allocated €200,000 to test yield optimization, and I discovered that governance power created a new narrative layer for value accrual. But the underlying metric—TVL—was subsidized by rewards. Once rewards stopped, TVL vanished. Apate’s swear word KPI is similarly subsidized by the AI’s aggressiveness.
Moreover, the data flywheel effect that Apate likely pitches to investors is real but fragile. Every interaction generates data about scammer tactics, speech patterns, and script variations. This data can be used to train better models, creating a defensive moat. But the moat is only as good as the data’s uniqueness. If multiple anti-fraud companies deploy similar systems, the data becomes commoditized. And if the models are based on open-source LLMs, the barrier to entry is low. I saw this in 2017 with community coins: the narrative strength of a project often preceded technical adoption, but when the hype faded, the underlying technology was easily replicated.
From the chaotic ICOs of 2017 to the structured liquidity of today, we’ve seen narratives evolve. Apate’s 200k victims is a new chapter. The journey from 2017’s community coin frenzy to the structured liquidity of today has taught me to question metrics that sound too good. But the core of Apate’s proposition is the idea that they are building a “scam baiting infrastructure” that can be sold to governments, banks, and telecoms. That’s a B2G play, and it’s actually plausible. Governments in Singapore, Hong Kong, and the UK are desperate to combat scam call centers. The problem is that these clients demand rigorous proof of effectiveness, not just a high swear word count.
Let me give you a concrete example. In 2022, after the Terra/Luna collapse, I pivoted my fund to focus on modular blockchains. I invested €50,000 into Celestia because the narrative shifted from yield to scalability. I learned that the most successful investments are those that align with a structural shift, not a flashy KPI. Apate’s structural shift is the rise of AI agents, but their execution is still tied to a vanity metric. The real question is whether they can demonstrate that their AI victims actually reduce fraud losses. That requires A/B testing with real scam calls, something that is extremely difficult to do without cooperation from law enforcement.
Contrarian: The Blind Spots Everyone Misses
Here’s where I get uncomfortable. The contrarian angle is that the swear word KPI might be the least important metric. The real value of Apate’s system is not in baiting scammers—it’s in the data they collect. Every call is a treasure trove of IP addresses, phone numbers, bank accounts, and script patterns. This data could be used to train a global fraud detection model that predicts scams before they happen. But if Apate is selling the service as an engagement tool, they’re leaving money on the table. Or worse, they’re not securing that data properly.
Another blind spot: legal risk. In many jurisdictions, recording conversations without consent is illegal, even if the target is a scammer. The EU’s GDPR and the upcoming AI Act may classify this as high-risk AI. In China, the content generated by these AI victims would need to pass strict censorship, which might render the system useless. I’ve seen startups collapse under regulatory pressure. In 2024, I watched a promising AI-crypto project fail because it couldn’t navigate the Hong Kong licensing regime. Apate could face similar hurdles.
From the chaotic ICOs of 2017 to the structured liquidity of today, we’ve seen narratives evolve. Apate’s 200k victims is a new chapter. The journey from 2017’s community coin frenzy to the structured liquidity of today has taught me to question metrics that sound too good. But there’s another layer: the potential for misuse. If Apate’s technology is open-sourced or leaked, it could be used to create AI victims that target innocent people for harassment or extortion. The very same system that baits scammers could be weaponized against journalists, activists, or political opponents. The “swear word KPI” could become a “harassment KPI” in the wrong hands.
Takeaway: The Next Narrative
So where does this leave us? Apate’s announcement is a classic bull market story: a bold, audacious claim that captures the imagination. But as a narrative hunter, I see the structural flaws. The high cost, the gameable metric, the regulatory uncertainty—all point to a project that may be more about raising capital than solving fraud. The real next narrative, I believe, is verifiable on-chain AI agent effectiveness. Imagine a smart contract that tracks how many scam calls an AI agent has handled, with proof of work submitted to a decentralized data registry. That would be a transparent, trustless system that investors could actually audit.
We’ve come a long way from 2017’s community coin frenzy to the structured liquidity of today. But the human tendency to follow narratives remains. Apate’s 200,000 fake victims are a compelling story, but the numbers don’t lie. The question is: will the market see through the hype before the next cycle turns?
