Apr 29, 2019, 6:38 PM ETPanel, Conference Presentation

Investing in Cutting-Edge Technologies and Jobs of the Future

Diane Greene's Assertion: The era of Moore's Law (doubling every 18–24 months) is ending; technology progression is now accelerating 2–3 times faster due to the convergence of hybrid cloud, supercomputing, 4G/5G, and advanced AI.
Jim Whitehurst (Red Hat/IBM): Technological progress is multiplicative rather than additive, driven by the stacking of advances in storage, compute, big data, and analytics, resulting in changes observable every six months.
Doug Merritt (Splunk): Billions of users generating zettabytes of data create a "network effect" that accelerates innovation; a single major customer now processes 10 petabytes of data daily (more than the total web data captured from the mid-90s to present).
Peggy Johnson (Microsoft): The acceleration is fueled by business pressure, as every company is becoming a technology company seeking differentiating insights from massive data pools.
Ravi Kumar (Infosys): The pace of change is pervasive across all industries, necessitating a shift from traditional job roles to a "human + gig + machine" ecosystem.
Chris Liddell (White House): Exponential change often appears linear until it hits a critical inflection point; policy must shift from top-down mandates to unleashing private sector innovation to compete globally.

Job Displacement and Creation: A report cited by Ravi Kumar predicts 75 million jobs will vanish by 2022, while 35 million new jobs will be created by digital technologies.
Skill Shifts: 60% of jobs will undergo significant changes in the next decade, though only 5% will disappear entirely; the focus shifts from pure coding to "problem finding" and applying technology to business contexts.
The "New Collar" Economy: Infosys is pivoting from pure STEM to embracing liberal arts and design talent, noting that "problem finding" (creative application) is more valuable than "problem solving" (which machines handle).
Lifelong Learning: The linear path from education to work is dissolving into a continuum of lifelong learning; the government must support retraining rather than just initial K-12 or higher education.
P-TECH and P-TECH: IBM's P-TECH model connects underprivileged youth with high school and college pathways to secure employment, tapping into untapped talent pools ignored by traditional metrics like SAT scores.
First-Line Worker Empowerment: Microsoft is using HoloLens to empower 2 billion first-line workers (e.g., Chevron technicians) to perform complex repairs remotely via mixed reality, reducing the need for expert travel.

IBM's Acquisition of Red Hat: IBM acquired Red Hat for one-third of its market cap to gain capabilities in community collaboration, enterprise adoption of open source, and workforce skilling, noting Red Hat has "no IP" in the traditional sense but possesses critical deployment capabilities.
Microsoft's M12 Fund: Microsoft established a corporate venture fund (M12) to gain early signals on emerging tech, partnering with rather than leading VC firms to avoid missing trends.
Satya Nadella's "Growth Mindset": Microsoft's cultural shift, inspired by Carol Dweck's Mindset, prioritizes "learn-it-alls" over "know-it-alls," transforming the company from a zero-sum game to a partnership ecosystem (e.g., collaborating with competitors like Amazon on Alexa/Cortana).
Splunk's "Data Fabric": The company is moving away from owning data to becoming an open abstraction layer, emphasizing interoperability and componentization to help users mix and match technologies.
User-Led Innovation: Jim Whitehurst notes that major tech advancements (cloud, big data) originate from user needs (e.g., Google indexing the web), not vendor R&D, necessitating a shift to open-source community engagement.

Three Government Roles: The U.S. government acts as a spender ($150B R&D, $500B vs. China's total), regulator, and owner (of assets like spectrum, national labs, and data).
Infrastructure Priorities: Chris Liddell identifies three critical legislative priorities: Digital infrastructure (including quantum computing and national labs), Immigration reform (merit-based H-1B), and Trade (USMCA and IP protection).
Data Unleashing: The government should release massive data repositories (healthcare, GPS model) to drive private sector innovation, citing GPS as a prime example of successful data monetization.
National Council for the American Worker: A new federal entity is reforming the Higher Education Act, proposing to extend Pell Grants to short-term, vocational programs and changing accreditation to recognize micro-credentials.
Education Reform: Current statistics show only 64% of four-year and 33% of two-year students graduate; policy must shift toward vocational training, apprenticeships, and "earn and learn" models.

Populist Backlash: Rapid technological acceleration is driving anxiety that threatens free enterprise buy-in, potentially pushing populations toward radicalized populism on the left or right due to perceived lack of shared prosperity.
SAT Bias Critique: The transcript challenges the Silicon Valley bias toward high SAT math scores (e.g., Microsoft/Apple founders), arguing that creative and non-algorithmic intelligence (e.g., electricians, plumbers) is vital to the economy.
Brittle vs. Growth Mindset: Carol Dweck's research indicates today's top students are "exhausted and brittle," fearing mistakes; the goal is to create systems that encourage risk-taking and failure as part of the innovation process.
Shared Prosperity: Success in future industries (5G, AI, synthetic biology, quantum computing) depends entirely on building a workforce capable of sharing in the economic benefits, not just driving GDP growth.
Inclusion of Non-Technical Talent: The future economy requires liberal arts skills (design, creativity, collaboration) alongside technical skills; the goal is not to turn everyone into a coder but to empower every person to achieve more with technology.