Unlike OpenAI and other startups such as Stability AI, Google has released its most powerful image- and text-generation models only to a limited group of testers. Google executives in recent years have stressed the need to test new artificial-intelligence tools for signs of bias while guarding against potential misuse, concerns shared by many academics.

Such caution has at times frustrated researchers at groups such as the artificial-intelligence unit Google Brain, some of whom have left to raise money for their own startups where they can more easily release new products, said people familiar with the matter.

Last week, the head of Google's research division, Jeff Dean, published a more-than-7,000-word blog post summarizing the company's recent work in artificial intelligence, writing that the developments are "making their way into real user experiences that will dramatically change how we interact with computers."

The pressures add to a difficult business environment for Google, whose search and ad-tech operations have both been targeted by Justice Department lawsuits. Google also announced the largest layoffs in company history last week, cutting about 12,000 employees.

"We have long been focused on developing and deploying AI to improve people's lives," a Google spokeswoman said. "We believe that AI is foundational and transformative technology that is incredibly useful for individuals, businesses and communities, and as our AI Principles outline, we need to consider the broader societal impacts these innovations can have."

Eoin Treacy's view

Google had a reservation robot system running in 2018. It automatically made calls to the restaurant when an online booking system was not available. They got so much backlash against robots taking jobs that it essentially canned the program. Today apps like Opentable or Fork have grown to capture more of the booking market but that does not negate the fact robo caller tech is at least five years old. 

OpenAI launched its product with a splash but it is important to think about what it actually does. The system combs the internet for the best possible fit of potential answers and creates a grammatically correct answer. That is why it works well with answers to college essay questions. Millions of students have answered the same questions for decades and therefore there is a large sample space to compile from.

The result of asking it to come up with a way to structure an ETF to bear the market has to wade through tomes of compliance approved marketing materials that conclude it is impossible to bear the market with regularity so that is the result that is delivered.

Making the leap from line of best fit from available data to second order thinking is a major innovation.  There is no evidence it has been reached. Nonetheless, significant capital is being devoted to trying to make that leap.

Nvidia has devoted it business to making chips for AI data centres so the share has rebounded to break the step downtrend.

Alphabet (Google) has shaken off antitrust fears to firm in the region of the 1000-day MA.

Microsoft has invested $10 billion in OpenAI and is also firming from the region of the 1000-day MA.

No company is more associated with AI than Tesla since they have essentially bet the firm on achieving autonomy. The share continues to unwind the deep short-term oversold condition.