Thread by @OwlWealthy

I dont think Wall St gives $PLTR enough credit for its opportunity in AI

A thread...

The McKinsey Global Institute in 2018 forecasted that AI would deliver $13 trillion of incremental economic activity by 2030. Very few technologies throughout history have had this level of societal and economic impact.

McKinsey's forecasts call for an additional 1.2 percent of additional GDP growth per year through to 2030. As a comparison, the steam engine's boost to productivity of 0.3 percent per year between 1850 and 1910, and IT in the 2000s delivered 0.6 percent.

Data is fuel AI needs to run. AI uses machine learning (ML) to get smarter. learning comes from processing data. More data = smarter AI. $PLTR helps orgs bring all their data into single platform. AI built on top of Palantir's data integration platform will be smarter

While $PLTR gives customers important infra to make AI/ML operational, I see a lot of upside to increase their capture of the projected $13 trillion AI market, including more AI services such as natural language processing, computer vision and robotic process automation.

$PLTR can also provide AI apps for biz processes such as Anti-money laundering, predictive maintenance. already offering these AI apps. As $PLTR wins more industry platform implementations (skywise) will have more data than competitors & therefore a smarter AI model.

Another exciting area is $PLTR Edge AI, makes is easier to deploy and operate sophisticated AI models to edge computing. brings AI smarts to vehicles, equipment and infrastructure such as drones, cars, buildings, oil rigs, aircraft, ships, wind turbines, robots, & satellites.

New scenarios are requiring the application of machine learning models that empower equipment and infrastructure to make decisions throughout the course of their operation. This model is called machine learning inference.

A trained algorithm gets deployed to an IoT device to make predictions, based on data generated at this endpoint, to guide decision logic applied by the device. A good use case example of where machine learning inference @ scale will be required is autonomous cars.

U.S. gov is already $PLTR Edge AI customer. PLTR Edge AI has potential to bust open commercial. Same AI and edge computing innovations apply to commercial - AI on factory floors directing robots, enabling predictive maintenance, or better utilization across the electricity grid.