There is strong evidence that the tooling part of Chamath’s claim has materialized, but little hard evidence that it has already transformed non‑English‑speaking workers into core, top‑productivity startup contributors at scale.

Why it looks directionally plausible / partially true

• Major collaboration platforms now provide near real‑time spoken translation, removing a large part of the language barrier in meetings. Google Meet and DeepMind launched real‑time speech translation features in 2025, translating spoken language during calls into participants’ preferred languages, with support for multiple European languages. (blog.google) Cisco is acquiring EzDubs to add voice‑preserving real‑time translation into Webex, explicitly to let people converse across languages. (indianweb2.com) Similar capabilities exist across specialized meeting tools like Interactio, Wordly, and others marketed for business meetings, trainings, and events. (wordly.ai)
• General‑purpose AI systems such as OpenAI’s GPT‑4o support real‑time multilingual voice conversations and translation in over 50 languages, explicitly pitched as removing language friction in everyday and professional use. (lifewire.com) Dedicated products (e.g., Transync AI, emotii.ai, Relay’s TeamTranslate) target workplaces and multinational teams, advertising that they "eliminate language barriers" and let every worker be "heard, understood, and empowered." (apps.apple.com)
• In adjacent high‑skill domains there is empirical evidence that generative AI is disproportionately helping non‑English speakers. A 2025 study of 2M+ biomedical papers finds AI‑assisted writing adoption has grown about 400% in non‑English‑speaking countries (vs. 183% in English‑speaking ones), modestly increasing productivity and narrowing publication gaps—clear evidence that AI is mitigating English language disadvantages in knowledge work. (arxiv.org) Research prototypes like WordDecipher specifically target digital workplace communication for non‑native English speakers, helping them craft socially appropriate English messages in email and chat. (arxiv.org)

Why we still can’t classify the prediction as clearly right or wrong

• Chamath’s prediction is not just that tools exist, but that they “basically become some of your best folks”—i.e., that non‑English speakers, relying on AI to bridge language, are now functioning as core, highly productive startup/tech team members at a level comparable to English‑fluent peers.
• The available evidence shows rapid deployment of translation and language‑assist tools and early proof that they reduce language friction and help non‑English professionals (scientists, remote workers, call‑center agents, etc.). (nypost.com) However, there are no large‑scale labor‑market studies or broad startup case‑study datasets demonstrating that non‑English‑speaking workers who rely primarily on AI (rather than learning English) are now systematically regarded as top, core contributors in startups or tech firms.
• Many startups still explicitly require English proficiency in job postings, and qualitative reporting focuses more on potential and early pilots than on a documented, widespread shift in team composition and top‑performer profiles.

Because the mechanism (AI offloading language) is clearly emerging but the outcome (non‑English speakers broadly becoming some of the best core team members thanks to these tools) isn’t yet measurable or well‑documented, the prediction can’t be cleanly scored as right or wrong with current evidence—hence ambiguous.