Google had the chatbot ready before OpenAI – what happened and why it matters
A Reddit post doing the rounds makes a bold claim: Google had a capable chatbot (LaMDA) ready before ChatGPT but chose not to ship it over reputational risk. Then, after OpenAI’s breakout success, Google rushed Bard to market and paid dearly for a single factual error in its first public demo.
Whether you work in product, data, or compliance, this isn’t just industry gossip. It’s a cautionary tale about launch strategy, risk appetite, and how mismanaging expectations can be more damaging than imperfections in the tech itself.
Read the original discussion: Reddit thread.
From LaMDA to Bard to Gemini: the key timeline and missteps
According to the Reddit post’s account, Google had LaMDA – its conversational AI – “ready” months before ChatGPT. Leadership hesitated, worried about the brand risk of wrong or harmful answers. Then ChatGPT upended the market and Google flipped to emergency mode.
- 30 Nov 2022 – OpenAI launches ChatGPT, hits 1 million users in 5 days, 100 million in two months.
- Dec 2022 – Google declares “Code Red”, reassigns teams and brings founders into strategy meetings.
- 6 Feb 2023 – Google announces Bard and posts a demo; one of Bard’s answers contains a factual error about the James Webb Space Telescope.
- 8–9 Feb 2023 – Reports of the error spread ahead of a Paris event; Alphabet’s stock drops 9% in a day, then another 5% the next day (about $160 billion in total over two days, per the post).
- Later – Bard becomes Gemini; quality reportedly improves.
| Date | Event | Reported impact |
|---|---|---|
| 30 Nov 2022 | ChatGPT launch | 1m users in 5 days; 100m in two months |
| Dec 2022 | Google “Code Red” | Founders brought into meetings |
| 6 Feb 2023 | Bard demo posted | Included a factual error |
| 8 Feb 2023 | Paris event; Reuters highlights error | Alphabet stock -9% (~$100b) |
| 9 Feb 2023 | Aftermath | Further -5% (~$160b over two days) |
Reputational risk vs speed: the core trade-off in AI launches
Large language models (LLMs) are probabilistic systems. They “hallucinate” – confidently generate incorrect or fabricated content. Alignment is the process of shaping models to behave safely and helpfully within stated norms. Per the Reddit post, Google worried that a highly visible failure would undermine trust in Search and the broader brand.
“We knew in a different world, we would’ve probably launched our chatbot maybe a few months down the line.”
The post argues that OpenAI took the opposite tack: launch fast, improve publicly, and let usage fuel iteration and mindshare. Microsoft, meanwhile, integrated the tech and captured upside with less direct reputational exposure.
What the Bard demo error tells us about expectations and comms
The Bard demo’s mistake – claiming the James Webb Space Telescope took the first exoplanet photo – was simple to verify and easy to ridicule. The irony stung because Google’s reputation is grounded in trusted information retrieval.
“I want people to know that we made them dance.”
Per the post, markets reacted sharply, wiping tens of billions from Alphabet’s value in 48 hours. The broader lesson: launching an AI assistant invites the public to test your brand’s confidence. One high-profile error can dominate the narrative if you haven’t framed the product stage and limitations clearly.
Lessons for UK organisations building with AI
For teams here in the UK, the post underlines practical steps to balance innovation with governance:
- Set expectations publicly. Label early releases as experiments and show known failure modes. Don’t promise “facts”; promise “assistive suggestions”.
- Build guardrails before hype. Red-team your model for safety, bias and misuse, then re-run after every fine-tune or prompt change.
- Plan staged rollouts. Start with internal pilots and selected customers before press events. Keep feature flags ready to disable risky behaviours quickly.
- Document data and decisions. Keep an audit trail for prompts, training data sources, and eval results. This helps with UK GDPR accountability and responding to regulators.
- Mind sector regulations. Finance, health, and public sector use cases will face higher scrutiny. Engage compliance early and keep human-in-the-loop for critical workflows.
- Instrument everything. Track error rates, escalation volume, and user-reported harms so you can quantify progress and justify launch decisions.
If you’re operationalising LLMs with everyday tools, start small. For example, connect an assistant to your spreadsheets to automate data prep and analysis, but keep clear human checks in place. Guide: How to connect ChatGPT and Google Sheets.
Did “launch fast and fix publicly” win?
The Reddit post concludes that Google “lost the lead” by hesitating, then compounded the damage by rushing. That’s one reading. Another is that the industry learned two truths at once:
- Speed matters for user mindshare and iteration data.
- Framing, guardrails and product marketing matter just as much.
In other words, moving fast isn’t enough; you need a narrative and governance model that buys you forgiveness when the model inevitably makes mistakes.
Where Google landed: Gemini today
The post notes that Bard became Gemini and “it’s actually pretty good now”. Beyond that, details aren’t disclosed here. The headline point remains: early missteps don’t define the long-term arc, but they do shape market perception and regulatory attention.
Practical checklist to avoid a Bard-style stumble
- Define success metrics that include safety and quality, not just MAUs.
- Publish a short model card or limitations note in the product, not just docs.
- Use retrieval-augmented generation (RAG) for factual tasks so the model cites sources, and train users to click through.
- Run live-fire rehearsals: adversarial prompts, media Q&A, and crisis comms drills before launch day.
- Time launches so your team can monitor and respond in real time for at least 72 hours.
Notable quotes from the post
“This little company in San Francisco called OpenAI… this product ChatGPT.”
“Code Red.”
“Screw it, we gotta ship.”
Sources and further reading
- Reddit discussion: Google had the chatbot ready before OpenAI…
- Coverage linked in the post: The Bridge Chronicle, Business Insider
Bottom line
AI products fail in public all the time. What hurt Google, per this account, wasn’t the existence of a mistake; it was the mismatch between a perfectionist brand promise and the messy reality of generative models. UK teams can avoid the same trap by being explicit about risks, instrumenting for safety, and launching in stages with a clear story about what the system can and can’t do.
