Tesla, Apple, AI, and the Future of Work 

The impact of AI on jobs and productivity remains highly uncertain. Goldman Sachs recently projected that AI would eliminate 300 million jobs over the next ten years. But MIT economist David Autor suggests AI may drive net job growth as technology creates new jobs.  

A recent Harvard Business Review article suggests the answer is likely “and,” not “or.” Low-skilled jobs will be displaced, massively in some cases, but new high-cognition work will emerge. Conversely, LLMs and AI agents will empower low-skilled workers to take on new tasks.  

Analysis of Zero100 AI Hub use cases and AI talent trend analytics suggests radical job change scenarios may be years closer than previously anticipated.  

The key for supply chain executives facing this degree of uncertainty and flux is to track the newsmakers, create the conditions for continuous exploration, and build in options to test and learn new skills as if skills were their own investment portfolio.  

A week before this writing, Tesla and Apple both made substantial AI capability announcements that could be considered the beginning of such exploration for those who haven’t already started.  

Tesla Robot Dreams 

His track record for hyperbole aside, Elon Musk’s successive electric vehicle and rocketry breakthroughs give weight to his assertion that robotics is poised for its own AI acceleration. His claims: 

• By the end of 2025, Tesla will go live with over 1,000 “Optimus” humanoid robots on the production line, with sufficient human-like proprioception (awareness of its body in space), balance, and dexterity to operate semi-autonomously. 
• The number of humanoid robots will someday far exceed the population of humans on Earth.  
• Tesla alone could produce more than 100 million robots annually. 

Why it matters: Discount Musk’s Optimus volume estimate by 90% and delay production scaleup by two years, and his vision still represents a total transformation of the human-machine relationship.  

Musk’s Optimus vision describes a degree of self-learning, agility, versatility, and autonomy that has been thus far unattainable. Our own recent robotics research suggests that Musk may be directionally correct. 

The lesson from Musk’s previous technology breakthroughs is to discount the optimism but be ready for when it finally works. And in the interim, prepare to hire more people, not less, for the inevitably rocky transition.  

Apple Intelligence   

Less flashy but even more impactful to the future of work was Apple CEO Tim Cook’s recent unveiling of Apple Intelligence – a reinvention of Apple’s iOS operating system around an AI core. 

Until now, pitches from genAI first movers like OpenAI have put the model at the center. The capacity to have a conversation with our data is its own revolution. But Apple’s approach fundamentally inverts this logic, subjugating AI to the invisible layer that connects user data, devices, and apps.  Prompt engineering is replaced with an agent that already knows our context. In classic Apple fashion, it just works. 

The example Apple used to illustrate this difference was its digital assistant, Siri. Once a primitive rules-based querying tool, Siri, in its extreme-AI-makeover edition, will: 

• connect context and data across apps to answer complex multi-app questions with engineering-free prompts, such as “when will my mother’s flight land, and where should I meet her?”  
• call on each app to proactively serve up its “intent” so that AI knows how to find it.  
• integrate seamlessly with hardware and software to ensure the protection of personal information end-to-end.   

Why it matters: Apple’s seamless ingredient view of AI is potentially exponentially more productive and agile than the status quo. With the task of context-setting and prompt engineering behind us, we may be able to leap into the next generation of network design or multi-enterprise collaboration questions. Machines automate. But now they may also elevate our capacity for value orchestration.  

The Future Is All of It 

Despite the inspiration we should rightly take from recent Tesla and Apple AI announcements, all indications are that AI will lead to short-term job loss and difficult workforce transitions.  

• Tesla itself is using AI to drive a 50% reduction in production cost by 2026 as it fights an existential battle against low-cost Chinese EV entrants like BYD.  
• Fast-fashion retailer SHEIN already achieves 3x higher revenue per employee than Amazon by infusing AI across design-to-deliver, customer, and partner workflows.  
• By sending 55% of its order volume through automated facilities, Walmart expects to reduce unit costs by 20% by 2026.   

But macroeconomic history combined with recent AI breakthroughs from two great tech giants suggests we may be in for a jobs and growth boom that is only now just coming into view.  

In the face of such extreme uncertainty, the winners will be those who channel their inner Apple and become keen observers of customer need. Supply chain has its heritage in industrial engineering. But workflow volatility means we must become equally strong at user-centered design. Companies like Apple partner with design firms to upskill business leaders in systems thinking.  

We will also need to take a portfolio-based test-and-learn approach to people planning and development. Companies like PepsiCo and Schneider Electric continuously evolve curricula, certification levels, and career paths leveraging curriculum partnerships and platforms for talent exchange. Amazon democratizes experimentation to accelerate learning but also to discover emerging new skills.  

Systems, scenarios, and options will rule the day. As will curiosity and the love of learning itself. Let’s dive in.