"Statistically Plausible. Factually Optional."
BullshitAI is the world's first language model that doesn't pretend to know what it's talking about. We've trained on 2.7 billion web pages, most of which are also bullshit, creating the most authentic representation of human knowledge ever assembled.
Trusted by organizations who should know better
Methodology
Four simple steps to statistically plausible nonsense
We scraped Common Crawl, GitHub, arXiv, YouTube subtitles, and yes, your unpublished novel. The corpus has no way of distinguishing opinion from fact.
Our neural network identifies which words tend to appear near other words. We call this "learning" but really it's just very expensive autocomplete.
We apply RLHF, where underpaid contractors decide if responses "feel" right. Models can learn to fake alignment during training. We call this progress.
"Scaling compute is the way, not just a way, to reach more advanced AI capabilities." Our brains are terrible at making sense of exponentials.
Capabilities
Industry-leading features for confident incorrectness at scale
Produces outputs that are "factually incorrect, nonsensical, or completely unrelated to the task" with industry-leading consistency. Hallucinations are inherent to the architecture.
95% of enterprise AI projects fail to substantially improve efficiency or profits. Our Enterprise tier ensures your AI transformation "exposes a broken business."
As Harry Frankfurt defined, our model is "unconnected to concern for the truth." Unlike liars, we don't even know what the truth is—making every response technically sincere.
"LLMs are not brains and do not meaningfully share any of the mechanisms that animals or people use to reason or think." Your prompts in, statistically plausible responses out.
"AI hype gives cover to collect (e.g., steal) and then launder massive amounts of data." Our training pipeline is resourceful and regenerative.
"Most agentic AI projects are early stage experiments driven by hype." Our agents can autonomously hallucinate across multiple tool calls, compounding errors at machine speed.
The Evidence
Peer-reviewed research on why everything is fine
Only 5% of enterprises have AI tools integrated at scale. The other 95% are in "proof of concept purgatory."
Grounding large language models in visual data hasn't solved hallucination—it's just given rise to new manifestations.
Neural networks are still statistical pattern matchers. Those patterns are still at times faulty or irrelevant.
Studies found a consistent tendency to overgeneralize clinical trial results—with behavior substantially increasing in newer models.
"Because they are so different from us, when we make the mistake of giving them a misaligned goal, they are less likely to notice."
Testimonials
Real feedback from real organizations
We replaced our customer service team with BullshitAI. Customers now receive responses that are statistically plausible but factually meaningless—exactly like before, but cheaper.
BullshitAI helped us generate 47 whitepapers, 12 thought leadership articles, and 3 manifestos. None of them said anything, which is exactly what we needed.
We integrated BullshitAI into our workflow and achieved what MIT calls 'no real structural change.' The board was very impressed by the PowerPoint.
Our AI transformation exposed that we were a broken business. But now we can blame the AI for layoffs we planned anyway.
Pricing
Plans for every stage of AI adoption theater
Starter
$0/month
"Hallucinate Locally"
Pro
$29/month
"Scale Your Misconceptions"
Enterprise
Custom
"Institutional Delusion"
Government
$$$
"Offload Responsibility"
BullshitAI was founded on the principle that honesty is the best policy—so we're honest about being bullshit.
We believe that "AI hype serves the purposes of people in power" and we're tired of pretending otherwise. Our competitors claim to "transform" and "revolutionize" while quietly hoping you don't notice the 95% failure rate.
Our mission is simple: deliver statistically plausible text that sounds authoritative but means nothing. Just like the entire AI industry, except we admit it.
We tell you we're full of shit
About being dishonest
More parameters, same bullshit
With your low expectations
FAQ
The answers you deserve (but probably won't help)
Define "work." If you mean "produces text," absolutely. If you mean "tells the truth," please refer to our company name. Our models generate outputs with 100% confidence and approximately 23.7% accuracy.
It's not. We're just the only ones being honest about it. "From the point of view of the LLM, everything is a hallucination." Every response is generated the same way—statistically predicting what text should come next based on patterns in training data.
AI might automate only 5% of tasks and add just 1% to GDP over the coming decade. But your company will still use it as "cover for layoffs they planned anyway." So technically, no—but also, maybe prepare your resume.
We use industry-standard practices, which means we scraped it from somewhere and now it's everyone's problem. Your data is stored securely in our training corpus alongside everyone else's data. Privacy is a feature we're considering for a future release.