Startup Pitch Teardown

1. Solution Claim: "94% accuracy" for decoding neural patterns into text via EEG headband

Why it's weak: This is by far the most exaggerated technical claim, as current non-invasive EEG-based brain-computer interfaces (BCIs) for text generation achieve nowhere near 94% accuracy in real-world conditions. State-of-the-art research (e.g., 2023 studies in Nature Machine Intelligence on EEG for imagined speech) reports word error rates of 30-60% even in controlled lab settings with extensive per-user calibration, slow speeds (5-20 words/min), and limited vocabularies. Consumer-grade EEG (like Muse or Emotiv headbands) is even noisier due to motion artifacts, sweat, and hair interference, dropping usable accuracy below 50% for communication tasks. Claiming 94% implies near-perfect, real-time transcription rivaling invasive BCIs like Neuralink (which hit ~80% in trials but require surgery), undermining credibility without peer-reviewed data or third-party validation.

How to strengthen: Replace with a more defensible metric backed by evidence, e.g., "Achieves 72% word accuracy in beta tests (validated by independent neuro lab at UC Berkeley), outperforming prior EEG benchmarks by 2x." Include a footnote linking to a technical whitepaper or arXiv preprint with methodology, dataset size, and error analysis.

2. Vision Claim: "Reads your brainwaves to predict what you want to type before you think it"

Why it's weak: This is scientifically impossible and hyperbolic, eroding trust immediately. Neural activity detectable by EEG occurs during thought formation (e.g., in the ~200-500ms window of cognitive processing), not before. Prediction relies on probabilistic ML models trained on prior patterns, but "before you think it" implies precognition, which violates basic neuroscience (e.g., Libet's experiments show conscious intent lags brain signals by 300-500ms, but doesn't enable pre-thought prediction). Investors will dismiss it as sci-fi hype, especially post-FTX-style overpromising scandals.

How to strengthen: Tone down to a realistic, exciting phrasing: "Decodes your intended words from brainwaves as you think them, enabling typing at thought speed (up to 40 wpm in tests)." Add a demo video GIF or benchmark comparison (e.g., "3x faster than typing, 2x faster than voice") to visualize the value without overclaiming.

3. Market Claim: "TAM: $180B" derived from 3.5 billion smartphone users

Why it's weak: The $180B figure is a massive, unsubstantiated leap from the cited $5.3B BCI market, with no clear math or segmentation. Assuming even $50/year ARPU across all smartphone users (3.5B × $50 = $175B) ignores reality: BCI adoption barriers (comfort, privacy, accuracy) limit penetration to <1% short-term, and competing inputs (keyboards, voice) dominate a "digital input" market without proven $180B BCI slice. Grand View Research's BCI forecast is medical/rehab-focused; extrapolating to consumers without sources (e.g., no McKinsey report) smells like inflated hockey-stick projections, a red flag for VCs who scrutinize TAM realism (e.g., Uber's early $1T claims faced backlash).

How to strengthen: Break it down transparently with a bottom-up model: "TAM: $180B addressable input productivity software market (Statista). SAM: $5.3B BCI (Grand View). SOM: $1.2B via 10% capture of 200M premium productivity users at $60/year." Add a simple chart showing pricing, penetration ramps (e.g., Year 1: 0.1%, Year 5: 5%), and comps like SwiftKey's $100M+ exit.