Two years ago, it was all “Code Red” for Google. ChatGPT, the little chatbot that could, had shocked the behemoth:
Although ChatGPT still has plenty of room for improvement, its release led Google’s management to declare a “code red.” For Google, this was akin to pulling the fire alarm. Some fear the company may be approaching a moment that the biggest Silicon Valley outfits dread — the arrival of an enormous technological change that could upend the business.
Google’s first response to ChatGPT, the long-discarded Bard, was mediocre. It seemed like the GPT tidal wave might overwhelm the dominant search engine, tangled in its bureaucracy, and nipped at by Anthropic, OpenAI and Perplexity.
A trifecta of breakthroughs suggests that the search giant’s steadfast investments in fundamental research are bearing fruit. First, a quantum computing advance that could prove transformative. This was swiftly followed by vindication in autonomous vehicles, as its Waymo unit emerged triumphant while competitors retreated. The coup de grâce arrived with significant improvements to its Gemini AI model, alongside a suite of practical applications that could reshape its core search business.
I want to turn to the most Googley of all the AI widgets they released: Gemini DeepResearch.
The product lets you do quite deep research on complex questions. It is the kind of thing I imagined when I looked at the evolution of knowledge technologies in 2020. I suggested these systems would
make the process of synthesizing knowledge cheaper, which will have knock-on effects both substituting of that activity and spurring a range of new innovations around it.
When you use DeepResearch, it feels a little like science fiction. Feed it a query, and it springs into action, crafting a bespoke research strategy before methodically executing it. The process, which unfolds over several minutes, involves the AI prowling academic databases and authoritative Web pages before weaving its findings into a coherent synthesis.
Here is the question I asked.
Analyze the intersection of renewable energy adoption and economic development in emerging markets between 2020-2024, with particular focus on:
• Policy frameworks that have successfully accelerated clean energy transition
• Economic impacts on local communities and job markets
• Technology transfer mechanisms between developed and developing nations
• Financing models that have proven most effective Please include quantitative data on implementation costs, ROI metrics, and employment statistics where available.My tireless research assistant produced 3,441 words, organised into 17 sections and a bibliography. As you can see below, tt was all rather impressive.