* Deep learning could be worth $17 trillion. As deep learning advances, it should automate and improve technology, transportation, manufacturing, healthcare, finance, and more.
Digital Finance could add 6% to world GDP by 2025 which would be about 0.7% per year to GDP growth for 8 years. This GDP growth would also likely continue.
There are other disruptive technologies that could be worth trillions.
* Self driving cars and robotic taxis could be $2 trillion opportunity by 2030
* According to ARK, 3D printing may grow into a $41 billion market by 2020, and Wood noted a McKinsey forecast of as much as $490 billion by 2025.
* ARK thinks mobile transactions could grow 15x, from $1 trillion today to upwards of $15 trillion by 2020.
* By 2035, Wood said US GDP could be $12 trillion more than it would have been without robotics and automation—that’s a $40 trillion economy instead of a $28 trillion economy.
* Wood believes there’s at least one big area blockchain and cryptoassets are poised to break into: the $500-billion, fee-based business of sending money across borders known as remittances. That is just one market. Blockchain for health and other records, financial innovation and other areas should see a multi-trillion dollar impact
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.
In exhibit e3 I presume the upper bars are % of current tasks that can be automated?
But if 33% of all labor gets automated away, how does ‘managing people’ get off with only 9% reduction?
That would seem to imply new not-easily-automated jobs for 24 of the 33% put out of work…?