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How corporate venture capital de-risks emerging technology development

As the pace of technological change accelerates, corporations are no longer just adapting to disruption — they are actively investing in it. Through their corporate venture capital (CVC) arms, established companies are entering partner-investor relationships with startups across frontier domains such as artificial general intelligence (AGI), humanoid robotics, quantum computing, and other potentially game-changing technologies.

In this post, I’m exploring how corporates are using CVC to de-risk exploration in these emerging tech domains, the competitive advantage of corporate-startup partnerships, which categories are particularly well suited to CVC versus traditional VC, and how emerging-tech startups can best position themselves for CVC investment.

How CVC reduces technology risk for corporations

Corporations face an inherent tension: they need to pursue innovation and new business models, but they also must manage risk, protect core business margins, and maintain operational stability.

That’s why more than 25% of the funding deals last year included CVCs. CVC offers a hybrid path: by investing in and partnering with startups in nascent and emerging technologies, corporates can explore adjacent or disruptive opportunities while externalizing much of the technology risk.

In domains such as AGI, quantum computing, space tech, or next-gen energy storage, the technologies are capital-intensive, have long horizons, involve high technical and commercialization risk, and often require domain-specific assets, manufacturing, regulatory engagement, or ecosystem partnerships. For corporations, investing via CVC is a strategic way to gain early exposure, build optionality, secure technology rights or vantage, and integrate promising startups into their ecosystem — enabling them to stay ahead of both disruptive threats and complementary opportunities.

However, not all companies should invest in all emerging technologies — the key is strategic selectivity, focusing on technologies that could either disrupt their industry or offer complementary capabilities to enhance their competitive position.

CVC is a de-risking engine for corporates exploring emerging tech — it lets them access options in high-uncertainty spaces without the full burden of building in-house, while building strategic alignment with their core business and future growth vectors.

How CVCs reduce technology risk through strategic value creation

While most emerging tech startups will need venture capital funding, nearly all funding deals can benefit significantly from CVC participation. The question isn’t whether corporate venture capital adds value, but rather how CVCs uniquely reduce technology risk for both corporates and startups across different technology categories.

What makes CVC different from traditional VC?

CVC brings strategic value beyond capital: manufacturing capabilities, distribution networks, supply chain access, regulatory expertise, and direct integration pathways. Traditional VC focuses primarily on financial returns and rapid scaling without operational entanglement.

Especially suited for CVC

Best for: Hardware, long horizons, strategic fit, high-capex technologies

  • Hardware + embedded systems (e.g., humanoid robots, advanced compute, quantum computing, next-gen energy storage, nuclear energy) — These domains require supply chain, manufacturing, and integration with existing platforms, often with regulatory or domain-specific partnerships. Corporations with manufacturing or platform assets can add real value. For example, HP Tech Ventures’ investment in EdgeRunner AI demonstrates how corporates can accelerate AI hardware integration. EdgeRunner builds domain-specific, air-gapped, on-device AI agents for military and enterprise applications that operate entirely without internet connectivity. The company’s platform delivers mission-specific AI assistants that ensure low latency, enhanced data privacy, and reduced cloud costs — critical advantages that scale when coupled with AI hardware platforms and edge computing products and expertise. Similarly, Intel Capital’s investment in Rigetti Computing showcases how corporate backing accelerates quantum computing development. Rigetti builds full-stack quantum computers, and Intel’s expertise in chip manufacturing, supply chain access, and deep semiconductor knowledge provides strategic advantages that pure financial investors cannot match, reducing both technical and commercialization risk.
  • Platform or ecosystem technologies (technologies that require broad industry adoption and create value through network effects, such as 6G/hyperconnectivity, clean-tech infrastructure etc.) — Corporates are often deploying or will deploy these platforms themselves, so investing via CVC gives them inside access and optionality.
  • Strategic technology adjacencies for the corporate. For example, if a corporate sees synthetic biology or biotech as a future adjacency to their business, then CVC allows them to explore while leveraging internal capabilities (e.g., R&D, supply chain, global operations).
  • High-capex / long-horizon technologies — Traditional VCs demand high returns within a fixed timeline, but corporates can afford longer horizons if strategic alignment is strong.

Related to the above, trending data show that CVCs have recently been prioritizing AI and Robotics, which exemplify both platform technologies and strategic adjacencies that many corporates are exploring. For example, nearly 30% of CVC deals in 2024 revolved around AI.

HP Tech Ventures’ recent investment in Multiverse Computing — a quantum-inspired AI company that compresses large language models by up to 95% while maintaining performance — exemplifies this trend. Multiverse’s technology addresses a critical infrastructure challenge in AI deployment, enabling models to run on edge devices and dramatically reducing computing costs and energy consumption.

HP’s strategic support helps Multiverse scale this technology across enterprise applications, bringing AI benefits to companies of all sizes.

Making it work for both CVC and startup

In the accelerating wave of frontier technologies — from AGI and quantum computing to next-gen energy storage, synthetic biology, and space tech — the smart corporates will not wait passively. They will deploy their CVC as a strategic lever to access, partner with, and accelerate startups that can redefine their future business models.

How do startups benefit from CVC partnerships?

For startups operating in these domains, the path to growth means not just securing capital, but forging the right strategic partnerships: ones that bring scale, integration, and access to a corporate ecosystem that would otherwise take years to build.

For HP Tech Ventures, this means offering portfolio companies access to HP’s world-class technology, one of the world’s largest channel and distribution partner networks, and a vast global manufacturing and supply chain — resources that help startups scale rapidly and achieve significant market impact.

Emerging tech sectors alignment

From humanoid robots to synthetic biology, the next wave of innovation is rewriting the boundaries of what’s possible — and CVCs are uniquely positioned to shape that future. The following table maps how each major emerging-tech sector aligns with corporate venture capital, and what founders should keep in mind as they navigate this evolving landscape.

What should founders consider when pursuing CVC?

By aligning technology, business model, partner strategy, and timing, both corporations and startups can ride the emerging-tech wave with lower risk and higher impact.

If you’re a startup in one of these frontier domains and are thinking about CVC, ask yourself: Which corporations in my value chain have scale, distribution, or manufacturing that could accelerate me? How much risk are they willing to take? Am I ready for strategic integration?

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How to future-proof your business in the age of AI

The pace of AI advancement has moved from gradual evolution to explosive transformation. In McKinsey’s latest survey, 78% of respondents said their organizations use AI in at least one business function.
 
In my role as a futurist and managing partner of HP Tech Ventures, I’ve witnessed firsthand how artificial intelligence is not just changing individual processes or products, but fundamentally rewiring entire industries in order to succeed. And I’m not alone. 88% of tech leaders believe AI adoption will create a competitive edge.
 
The businesses that will thrive in this new landscape are those that build adaptive capacity today. The good news? Your business doesn’t necessarily need to invest large sums of money to succeed.

The AI Megatrend: beyond the hype

Unlike previous technological shifts that evolved over decades, the impact of AI is compressed into years, sometimes even months.
 
The startups we work with through HP Tech Ventures are helping to transform established industries who are grappling with a fundamental reality: AI isn’t coming to transform their industry — it’s already here.
 
The question becomes how to build resilience and adaptability into your organization when the rate of change itself is accelerating.
 
Four pillars of AI-ready business architecture

  1. Cultivate an experimentation mindset
     
    Future-proof businesses don’t wait for perfect AI solutions; they experiment with imperfect ones. The most successful companies I’ve observed are running small-scale AI pilots across multiple business functions simultaneously. They’re building organizational muscle memory for rapid adoption and iteration.
     
    Start with low-risk, high-learning opportunities. Use AI tools to enhance customer service interactions, optimize scheduling, or improve content creation workflows. The goal isn’t immediate ROI. It’s developing institutional knowledge about how AI integrates with your specific business context.

  2. Invest in human-ai collaboration, not replacement
     
    The companies that thrive will be those that utilize AI to augment human capabilities. This requires rethinking job roles, not eliminating them. Customer service representatives become orchestrators of the customer experience. Financial analysts become strategic advisors. Marketing professionals become campaign architects.
     
    This shift demands significant investment in reskilling and upskilling your workforce. However, it also creates a competitive advantage: while your competitors focus on cost reduction through automation, you’re building enhanced capabilities through augmentation.
     
    It’s a win-win, too. 94% of employees would stay longer at a company that invests in their career development.

  3. Build data infrastructure as a strategic asset
     
    AI is only as good as the data that feeds it, yet most businesses treat data as a byproduct rather than a primary asset. Future-proofing requires viewing data infrastructure as critically important as financial systems or supply chain logistics. Improved data and security, as well as reduced compliance breaches, are among the top benefits of having data governance in place. 
     
    How do you accomplish this? Establish clear data governance protocols, invest in data quality systems, and create mechanisms for data sharing across organizational silos. It also means being strategic about what data you collect and how you structure it for future AI applications you haven’t even imagined yet.

  4. Develop ethical AI frameworks before you need them
     
    As AI becomes more central to business operations, the ethical implications become more complex. Businesses that establish clear ethical guidelines for AI use — covering everything from bias prevention to privacy protection and transparent decision-making — will have a significant advantage over those scrambling to address these issues reactively.
     
    Recent studies indicate a high level of public concern about AI’s negative impacts, with 86% of people supporting the regulation of AI companies. 
     
    But this isn’t just about compliance or public relations; it’s about ensuring the well-being of our employees. Ethical AI frameworks enable businesses to make informed decisions about which AI applications to pursue, how to implement them responsibly, and how to establish trust with customers and employees throughout the transformation process.

The network effects of future-proofing

 
One of the most interesting patterns I’ve observed is that AI-ready businesses create ripple effects throughout their ecosystems. Suppliers adapt their processes to integrate better with AI-enhanced workflows. Customers develop new expectations for service and customization. Partners begin exploring collaborative AI applications.
 
This network effect creates a virtuous cycle: businesses that move early and thoughtfully in adopting AI help shape the standards and expectations for their entire industry. They become the gravitational center around which ecosystem innovation occurs.

Weak signals to watch

While most attention focuses on obvious AI applications like chatbots and process automation, the businesses that will truly dominate are paying attention to weak signals that indicate where AI is heading next:

  • The convergence of AI with other emerging technologies like quantum computing and advanced materials science
  • The development of AI systems that can reason about physical world constraints, not just digital information
  • The emergence of AI that can collaborate with other AI systems to solve complex, multi-step problems
  • The evolution of AI from task-specific tools to general-purpose reasoning systems

How to take action today

Future-proofing isn’t about predicting the future perfectly. It’s about building the organizational capabilities to adapt quickly when the future becomes clear. The businesses that will thrive are already taking concrete steps:

  • This Quarter: Identify three business processes where AI could provide immediate value and launch pilot programs. Establish cross-functional teams to evaluate AI tools and develop initial implementation strategies.
  • This Year: Invest in data infrastructure improvements and staff training programs focused on AI collaboration. Develop ethical guidelines for the use of AI and establish mechanisms for monitoring the performance and impact of AI systems.
  • Ongoing: Build relationships with AI technology providers, participate in industry groups exploring AI applications, and maintain awareness of emerging AI capabilities that could disrupt your business model.

The Age of AI is today’s reality. The businesses that recognize this and act accordingly won’t just survive the transformation; they’ll lead it. The question isn’t whether your business can afford to invest in AI readiness.

The question is whether it can afford not to.

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Second Brain AI
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How Corporate Venture Capital is Reshaping Innovation

The corporate venture capital landscape is undergoing a fundamental transformation, driven by evolving economic conditions, new organizational models, and the increasing convergence of internal R&D with external innovation partnerships.

As we navigate through 2025, the data tells a compelling story of how CVCs are not just adapting to change but actively reshaping the innovation ecosystem.

The Numbers Don’t Lie: CVC’s Growing Influence

The momentum behind corporate venture capital continues to accelerate. Global CVC-backed funding reached $65.9B, a 20% YoY increase in 2024. More telling is that CVCs made up 28% of all active investors in 2024, with a shift toward strategic early-stage investing rather than concentration in large late-stage rounds.

This shift represents more than just increased funding. It signals a strategic evolution in how corporations approach innovation.

New CVC Models Emerging in 2025

The Rise of Corporate Venture Studios

The traditional CVC model of pure investment is giving way to more hands-on approaches. Venture studios combine the entrepreneurial spirit of creating new ventures with the scale and resources of corporations. This hybrid model is particularly attractive to corporations seeking deeper control over innovation outcomes.

Corporations across industries are adopting venture studio models to create new businesses from scratch, while leveraging their existing capabilities and market positions.

Accelerator Programs with Strategic Focus

Corporate accelerator programs have evolved into strategic alliances that provide startups with frameworks for growth, product innovation, and market access, rather than just funding and mentorship.

These programs are becoming more sector-specific and deeply integrated with corporate strategic objectives. Companies are using accelerators not just to scout for external innovation, but to create systematic pathways for bringing that innovation into their core business operations.

Innovation Partnership Platforms

A new model emerging in 2025 involves corporations creating comprehensive innovation platforms that combine multiple touchpoints — venture capital, accelerators, partnership programs, and even acquisition vehicles — under unified strategic frameworks. This approach allows for more flexible engagement with startups at different stages of maturity and alignment. An example of this would be the Microsoft for Startups program, which includes a founder’s hub, investor network, regional accelerators, and strategic partnerships.

Economic Shifts Reshaping CVC Strategies

The macroeconomic environment has fundamentally altered how both VCs and CVCs operate right now, with more selective investments emphasizing strategic value, lean models, and clear pathways to profitability. Yet CVCs still maintain their more holistic strategic views of their investments.

Strategic Value Over Pure Returns

Unlike traditional VCs focused primarily on financial returns, CVCs are increasingly prioritizing strategic value creation. This shift has several implications:

  • Portfolio Construction: CVCs are building portfolios that complement and enhance their core business capabilities, rather than pursuing maximum financial diversification.
  • Investment Timelines: Corporate investors can afford longer development cycles when investments align with strategic objectives, providing crucial runway for deep-tech and complex innovation projects.
  • Market Validation: CVCs can offer startups immediate access to enterprise customers and market validation opportunities that traditional VCs cannot provide.

While traditional VCs face pressure for quick returns as markets recover, CVCs may be better positioned to take advantage of the strategic opportunities created by market dislocations.

The Blurring Lines: Internal R&D Meets External Innovation

The most significant transformation in corporate innovation is the dissolution of boundaries between internal R&D and external venture partnerships. This convergence is creating new models of collaborative innovation that leverage the best of both approaches.

Integrated Innovation Ecosystems

Modern corporations are creating innovation ecosystems where internal teams work directly with portfolio companies, sharing resources, expertise, and market access.

This integration goes far beyond traditional corporate-startup partnerships:

  • Shared Technology Platforms: Portfolio companies gain access to proprietary corporate platforms and APIs, while corporations benefit from rapid external innovation cycles.
  • Cross-Pollination of Talent: Employees move between corporate R&D teams and portfolio companies, creating knowledge transfer and cultural bridges.
  • Collaborative Product Development: Joint development projects between corporate teams and startups are becoming more common, leading to products that neither could create independently.

Toyota Open Labs is an open innovation platform that connects startups with various business units across the Toyota ecosystem to drive the future of mobility. The program focuses on key areas such as energy, circular economy, carbon neutrality, smart communities, and inclusive mobility.

From Venture Capital to Innovation Capital

This integration is leading to a new category that transcends traditional venture capital — innovation capital. This approach combines:

  • Financial investment with a strategic partnership
  • Technology licensing with joint development
  • Market access with co-innovation
  • Talent exchange with knowledge transfer

CVC-Driven Innovation Breakthroughs

AI and Machine Learning Revolution

Generative AI funding continues to grow rapidly, with funding in the first half of 2025 already surpassing the 2024 total. According to Bain & Company, Software and AI companies now account for around 45% of VC funding. Corporate venture arms have been particularly active in this space, not just as investors but as strategic partners providing data, compute resources, and enterprise distribution channels.

One notable example is the collaboration between corporate CVCs and AI startups. Examples of this include Salesforce investment in Anthropic, Microsoft’s investment in Databricks, and HP’s investment in EdgeRunner AI. These partnerships leverage corporate scale and customer access while benefiting from startup agility and innovation capabilities.

New Success Metrics

CVCs will increasingly measure success through strategic impact metrics rather than purely financial returns, tracking portfolio companies’ contributions to core business growth, new market creation, and competitive advantage.

The Innovation Imperative

Corporate venture capital is no longer just an investment strategy — it’s become a core component of corporate innovation infrastructure. The companies that succeed in leveraging CVC effectively will be those that view it not as a separate activity, but as an integral part of their innovation and growth strategies.

The data from 2024 and early 2025 clearly show that CVCs are not just surviving economic uncertainty, but thriving by offering startups something traditional VCs cannot: immediate access to enterprise customers, operational expertise, and strategic partnerships that can accelerate growth and market adoption.

For corporations, the message is clear: in an era of accelerating technological change, external innovation partnerships through CVC are essential for staying competitive and relevant. The question is not whether to engage in corporate venture capital, but how deeply to integrate it into your innovation strategy.

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Second Brain AI
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Your Digital Brain Partner: How AI Will Transform How We Think and Work

Imagine having a co-worker who never forgets anything, can instantly recall every conversation you’ve ever had, and helps you connect ideas you never would have linked on your own. This is the reality of Second Brain AI, and it can fundamentally change how we work, learn, and think.
 
We’re moving beyond simple AI tools that answer questions or automate tasks. The next wave of artificial intelligence will act as genuine thinking partners, extending our cognitive abilities in ways that feel almost magical. These aren’t replacements for human intelligence. They’re amplifiers that make us dramatically more capable.
 
Tools are now emerging that demonstrate this power. Google’s NotebookLM, launched in 2024 and continuously updated through 2025, serves as an AI research assistant, transforming uploaded documents into interactive conversations and even podcast-style audio overviews. Meanwhile, platforms like Elict help researchers identify valuable research seeds and explore topics through conversational AI. Granola focuses on bringing your team’s conversations into one place and enhancing them with AI through summarizing, finding connections through scattered ideas, and surfacing relevant information.

From Information Overload to Intelligent Insight

We live in an age of information abundance that often feels more like information overwhelm. Every day, we’re bombarded with alerts, emails, articles, videos, podcasts, and conversations. Our natural response is to try to consume more, faster, but that’s a losing battle.
 
Second Brain AI takes a completely different approach. Instead of helping you process more information, it helps you understand the information you already have. It identifies patterns you may have missed, connects ideas across different contexts, and surfaces exactly what you need when you need it.
 
Think of it as having a personal librarian who has read everything you’ve ever encountered and can instantly provide the perfect piece of information for whatever you’re working on. However, unlike a human librarian, this one learns your thinking patterns and improves at helping you over time.
 
ClickUp Brain exemplifies this approach, automatically summarizing lengthy conversation threads, drafting documents, and transcribing voice clips directly within tasks — eliminating the need for teams to switch between multiple tools and contexts.

The End of Forgetting

How many great ideas have you lost because you forgot to write them down? How many important details from meetings have slipped through the cracks? How often do you find yourself thinking, “I know I read something about this, but I can’t remember where”?
 
Second Brain AI solves the fundamental human problem of forgetting. It creates a permanent, searchable record of your thoughts, experiences, and learning that grows more valuable over time. More importantly, it doesn’t just store this information — it actively helps you use it.
 
Your digital brain partner remembers the context around every piece of information. It knows not just what you learned, but when you learned it, what you were working on at the time, and how it connects to other ideas in your mental landscape.
 
Notion with AI integration and Obsidian’s interconnected note system are making this vision a reality. The Second Brain AI platform at thesecondbrain.io now allows users to chat with their saved notes from Notion, Evernote, and other platforms, while Elephas enables users to create topic-specific “brains” that can be shared via URLs for collaborative learning.

Predictive Thinking: Knowing What You Need Before You Ask

The most exciting aspect of Second Brain AI is its ability to anticipate your needs. By learning your patterns of thinking and working, it begins to suggest relevant information and insights before you even realize you need them.
 
Working on a presentation? Your AI partner might surface research from six months ago that perfectly supports your argument. Facing a difficult decision? It could remind you of a similar situation you handled successfully and suggest applying the same approach.
 
This predictive capability transforms how we approach complex problems. Instead of starting from scratch each time, you build on the accumulated wisdom of your past experiences, guided by an AI that sees patterns you might miss.

Everyone Becomes an Expert

One of the most democratizing aspects of Second Brain AI is how it levels the playing field between experts and beginners. Traditionally, expertise comes from years of accumulated knowledge and experience. But what if you could instantly access the insights and patterns that experts have developed over the course of decades?
 
Second Brain AI doesn’t replace the need for deep thinking or creativity, but it dramatically accelerates the learning curve. A junior employee can make decisions informed by organizational wisdom that previously took years to acquire. Students can engage with complex topics by building on the collective knowledge of their field.

The Creative Amplifier

Creativity often comes from combining existing ideas in new ways. Second Brain AI excels at this kind of creative synthesis. It can identify unexpected connections between concepts, suggest novel combinations of ideas, and help you explore creative directions you might never have considered.

This isn’t about AI generating creative work for you. It’s about AI helping you be more creative by expanding the pool of ideas and connections you can draw from. It’s like having a creative partner who has perfect recall of everything you’ve ever been interested in and can suggest fascinating combinations at just the right moment.
 
Tools like MyMind and Bear App are pioneering this creative synthesis, using AI to help users discover unexpected connections between saved content, images, and ideas across different projects and time periods.

Privacy and Control in the Age of AI

A common concern about AI thinking partners is the issue of privacy and control. The most effective Second Brain AI systems are designed to be personal and private, learning from your information without sharing it or using it to benefit others.
 
It’s like the difference between a personal diary and a public social media post. Your Second Brain AI is your private thinking space, designed to serve your goals and protect your information. You maintain complete control over what information it has access to and how it uses that information.
 
For example, HP AI PCs are designed to streamline tasks, speed up workflows with AI data analysis, copy editing, and image creation, all while ensuring the security of on-device AI.

The Learning Revolution

Traditional learning is linear. If you read a book, take a course, or attend a lecture, then you try to remember and apply what you learned. Second Brain AI enables dynamic, contextual learning that adapts to your needs in real-time.
 
Instead of trying to remember everything, you can focus on understanding concepts and making connections, knowing that your AI partner will help you recall specific details when needed. This shift from memorization to comprehension fundamentally changes how we approach learning and skill development.

Building Your Second Brain

 The transition to working with AI thinking partners isn’t about adopting new technology — it’s about developing a new relationship with information and learning. It requires shifting from trying to remember everything to trusting that the right information will be available when needed.
 
This transformation is already beginning. Early adopters are discovering that the most effective approach is gradual integration, starting with simple information capture and organization, then gradually expanding into more sophisticated AI-assisted thinking and decision-making.
 
The current landscape offers multiple entry points that offer increasingly sophisticated ways to build interconnected knowledge networks that grow more valuable over time.

The Future of Human Potential

We’re entering an era where the limiting factor in human achievement won’t be our ability to access information or remember details — it will be our creativity, judgment, and ability to ask the right questions. Second Brain AI handles information processing, allowing us to focus on the uniquely human aspects of thinking and problem-solving.
 
This partnership between human and artificial intelligence promises to unlock human potential in ways we’re only beginning to understand. We’ll be able to tackle more complex problems, make better decisions, and achieve goals that would have been impossible to work on alone.
 
Will you be among the early adopters who shape this transformation or among those who struggle to adapt later?

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Humanoid robots
— Comments Off on AI, Robotics & Biotechnology: 3 Game-Changing Technologies Transforming 2025

AI, Robotics & Biotechnology: 3 Game-Changing Technologies Transforming 2025

At the beginning of the year, I outlined 10 technology trends and weak signals I felt would have a transformative impact on 2025 and beyond. These emerging innovations represent not just incremental improvements but potential paradigm shifts that could fundamentally alter industries, economies, and societies.

These trends fall into three categories for me:

Game Changers are set to have a significant impact on industries, societies, and markets in 2025 and beyond. Will transform how we work, learn, and live.

Foundational Breakthroughs are major technological advancements needed for game changer technologies to succeed.

Weak Signal Wild Cards present the opportunity to be a future game changer or a foundational breakthrough but still in a nascent stage with a number of headwinds to overcome.

Today, I’m diving into the game changers — AI, Humanoid Robotics, and BioTech & Synthetic Biology, the opportunities and the questions they raise.

How will AI evolve beyond chatbots to digital companions?

Artificial intelligence is rapidly evolving beyond the familiar chatbot interfaces with which we’ve grown accustomed to. The next wave of AI development promises to fundamentally reshape how we work, interact, and live our daily lives.

Market Context: 77% of companies are using or exploring the use of AI in their businesses, and 83% of companies claim that AI is a top priority in their business plans. 

Will AI co-workers be your new colleagues?

The workplace of tomorrow will feature AI co-workers that not only answer questions but also actively collaborate on complex projects. These AI co-workers will understand context, maintain continuity across conversations, and contribute meaningfully to team dynamics. Unlike today’s AI assistants that operate in isolation, these systems will integrate seamlessly into existing workflows, attending meetings, managing projects, and even mentoring junior team members.

What This Means for You: Expect to see AI assistants to manage entire project workflows, attend meetings on your behalf, and maintain context across weeks or months of collaboration.

Can personalized AI become your next best friend?

Personalization is evolving beyond recommendation algorithms to AI systems that genuinely comprehend individual preferences, habits, and objectives. These personalized AI companions will learn from your behaviors, anticipate your needs, and adapt their communication style to match your personality. They’ll serve as personal advisors, creative collaborators, and decision-making partners across every aspect of life.

Will digital clones become a digital you?

Perhaps the most intriguing development is the emergence of digital clones — AI representations that can think, speak, and act like their human counterparts. These aren’t simple avatars, but sophisticated AI systems trained on personal data, communication patterns, and decision-making processes. Digital twins could attend meetings on your behalf, manage routine correspondence, or even continue your work in your absence.
 
Andrew Ng, along with DeepLearning.AI and RealAvatar, created a digital twin of himself.

Can AI wearables offer you intelligence at your fingertips?

The integration of AI into wearable devices is creating new forms of ambient intelligence. Smart rings, glasses, and clothing embedded with AI chips will provide real-time insights, health monitoring, and contextual assistance without the need to reach for a phone or computer. These devices will understand your environment, mood, and activities to provide perfectly timed interventions and support.
 
For example, news recently broke that Amazon acquired Bee, an AI wearables startup best known for a $50 wristband and companion app that records and transcribes nearly everything a user (and anyone within earshot) says.

Will digital AI become physical AI?

The same large language models powering today’s digital AI systems are being adapted to control robotic bodies. AI-enabled robots are now able to comprehend complex instructions, reason about their environment, move around the world like humans do, and interact naturally with us. The result is robots that can be taught new tasks through conversation rather than programming, and learn like humans do, through experience.

What are humanoid robots, and when will they arrive?

The convergence of multiple technological advances is bringing humanoid robots closer to mainstream reality. This isn’t science fiction — it’s an engineering challenge being solved through incremental breakthroughs across multiple domains.
 
Market Opportunity: The global market for humanoid robots is projected to reach $38 billion by 2035.

Large Language Models and Robotics

The same large language models powering today’s AI chatbots are being adapted to control robotic bodies. These foundational models enable robots to comprehend complex instructions, reason about their environment, and interact naturally with humans. The result is robots that can be taught new tasks through conversation rather than programming.

What Is Multimodal Sensing and Understanding?

Modern robots are developing human-like sensory capabilities through advanced computer vision, tactile sensors, and audio processing. This multimodal approach enables humanoid robots to understand their environment in rich detail, perceiving obstacles, sensing textures, and interpreting commands or environmental cues. The integration of these senses creates a more intuitive and responsive robotic experience that mimics natural human perception.

How Will Recent Dexterity Breakthroughs Come Into Play?

Recent advances in robotic manipulation are solving one of the field’s longest-standing challenges: dexterous hand control for humanoid robots. New approaches to finger movement, grip strength, and object manipulation are enabling humanoid robots to perform delicate tasks that were previously impossible. From threading needles to preparing meals, robots are gaining the fine motor skills necessary for everyday tasks that require human-like dexterity.

Will Edge Computing Improve Real-Time Decision Making?

The deployment of powerful computing directly within robotic systems is reducing latency and improving real-time decision-making. Edge computing allows humanoid robots to process information locally, enabling faster responses and reducing dependence on cloud connectivity. This advancement is crucial for robots operating in dynamic environments, where split-second decisions are essential for natural human-robot interaction.

FREQUENTLY ASKED QUESTIONS — HUMANOID ROBOTS

Q: When will I be able to buy a humanoid robot? 
A: Limited commercial models will be available in 2025–2026 for businesses. Consumer models are expected by 2027–2028.

Q: What will humanoid robots cost? 
A: The manufacturing cost of humanoid robots has dropped from a range that ran between an estimated $50,000 (for lower-end models) and $250,000 (for state-of-the-art versions) per unit in 2023, to a range of between $30,000 and $150,000 currently.

Q: What jobs will humanoid robots do first? 
A: Manufacturing assembly, warehouse operations, elder care assistance, and household cleaning are the first target applications.

What are biotechnology and synthetic biology?

Biotechnology utilizes biological systems for practical purposes, while synthetic biology aims to design and construct new biological systems or redesign existing ones with specific functionalities, often by combining biological parts in novel ways. Synthetic biology, a subset of biotechnology, is creating new breakthroughs by merging biology with engineering design principles to create living systems with tailored functions. Unlike traditional biotechnology, which moves genes between organisms, synthetic biology enables building organisms from the ground up.

From creating therapies to treating diseases, to building microbes that allow plants to create their own fertilizer, synthetic biology is revolutionizing medicine, agriculture, and environmental advancements.

The convergence of biology and technology is creating unprecedented opportunities to design and manufacture biological systems. This field represents perhaps the most transformative frontier in science and technology.
 
📊 Market Growth: BCC Research Market Analyst predicts the global market for synthetic biology products was valued at $15.4 billion in 2023. The market is projected to grow from $19.3 billion in 2024 to $61.6 billion by the end of 2029.
 
Moving forward, artificial intelligence will likely supercharge synthetic biology, starting with molecular, pathway, and cellular design.
 
This immense potential comes with equally significant responsibilities for careful oversight and regulation. Effective management requires robust safety protocols, international coordination on standards, transparent public engagement about risks and benefits, and adaptive regulatory frameworks that can keep pace with rapid scientific advancement.

How will gene editing transform medicine?

CRISPR and next-generation gene editing technologies are moving beyond treating genetic diseases to enhancing human capabilities and creating new biological functions. The precision and accessibility of these tools are democratizing genetic engineering, allowing researchers to make targeted modifications with unprecedented accuracy and speed.

Current Applications: Gene therapies for sickle cell disease and beta-thalassemia are already FDA-approved. CAR-T cell therapies (using edited immune cells) have shown promising results for certain blood cancers.

Will cellular agriculture allow us to grow products without farming? 

The ability to produce animal products in laboratories is revolutionizing food production. Cellular agriculture bypasses traditional farming by growing meat, dairy, and other animal products directly from cells. This approach promises to reduce environmental impact, eliminate animal suffering, and create new forms of nutrition that were previously impossible.
 
Recent Breakthrough: The FDA has its first-ever approval for a safety consultation on lab-grown fish. Wildtype can now sell cell-cultivated animal products.

What is biomanufacturing?

Engineered microorganisms are becoming sophisticated manufacturing platforms capable of producing everything from pharmaceuticals to materials. 

These biological factories can be programmed to synthesize complex molecules, self-replicate, and even respond to environmental conditions. The result is a new form of manufacturing that’s both more sustainable, more adaptable than traditional industrial processes and which can create entirely new products.
 
For example, Cellibre is a US-based startup specializing in engineering cells to function as biomanufacturing units for a range of high-value products, from cannabinoids to pharmaceutical ingredients. By leveraging synthetic biology and precision fermentation, Cellibre creates efficient, scalable, and sustainable production methods.

Can gene synthesis write the code of life?

Advances in DNA synthesis are making it possible to write genetic code from scratch rather than just editing existing genes. This capability opens the door to designing entirely new biological systems, from custom microorganisms to synthetic organs. Gene synthesis is becoming faster, cheaper, and more precise, enabling researchers to prototype biological solutions rapidly.
 
This year, gene synthesis is experiencing significant growth and innovation, from advancements in synthetic biology, personalized medicine, and the need for high-throughput gene synthesis in research and industry. With trends like cost reduction, custom gene libraries, automation, and collaborations emerging, things are changing rapidly.

How could metabolic engineering optimize life’s processes?

Scientists are learning to redesign the metabolic pathways that power living cells, creating organisms optimized for specific functions. This might involve engineering bacteria to produce biofuels, modifying plants to absorb more carbon dioxide, or creating microorganisms that can break down plastic waste. Metabolic engineering is turning biology into a programmable platform for solving global challenges.
 
 Recent technical advances are leading to a rapid transformation of the chemical palette available in cells, thus making it conceivable to produce nearly any organic molecule of interest — from biofuels to biopolymers to pharmaceuticals.

TECHNOLOGY CONVERGENCE: What Happens When These Merge?

These three domains — AI, robotics, and biotechnology — are not developing in isolation. Their convergence promises to create entirely new categories of innovation:

  • AI-powered biological research: Robots conducting experiments 24/7, accelerating drug discovery
  • Biological materials for robotics: Self-healing robot components grown from engineered cells
  • Personalized medicine AI: Digital twins that predict your health needs before symptoms appear
  • Synthetic biology computers: DNA-based data storage and biological processors

What’s next?

The future is not a distant possibility.
 
It’s being built today in laboratories, startups, and research institutions around the world. The question isn’t whether these technologies will reshape our world, but rather how quickly and profoundly they will do so — and how we prepare for those changes.

The convergence of AI, robotics, and biotechnology isn’t just changing technology — it’s redefining what it means to be human in an age of artificial intelligence and synthetic biology.

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AI Sustainability
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The Dual Nature of AI’s Impact on the Environment

Key Takeaway: While artificial intelligence faces scrutiny for its environmental costs, it simultaneously offers revolutionary sustainability solutions that could reduce global carbon emissions by 4% while driving $5.2 trillion in economic benefits by 2030.

Addressing the Environmental Elephant in the Room

The current narrative around AI and sustainability is dominated by concerns about energy consumption and carbon emissions. Headlines warn of data centers consuming city-sized amounts of electricity and AI training processes with massive carbon footprints. These concerns are valid and deserve serious attention.

However, this focus on AI’s environmental costs obscures a more complex reality. The same technology that challenges our energy infrastructure also offers unprecedented opportunities for environmental optimization and sustainability transformation across industries.

We’re at a crossroads where artificial intelligence and sustainability converge in ways that could create unprecedented business transformation opportunities over the next decade. As someone who analyzes emerging trends and their implications for strategy, I’m convinced that the AI-sustainability nexus represents significant opportunities and challenges facing organizations today.

The big question isn’t whether AI will impact sustainability — it’s how quickly your organization can harness its potential while minimizing environmental costs.

The Dual Nature of AI’s Environmental Impact

The relationship between AI and sustainability is a complex one. On one hand, AI systems are energy intensive. Training large language models can consume as much electricity as a small city uses in a year, and the proliferation of data centers to support AI workloads is driving unprecedented demand for power.

The Energy Challenge: AI’s Growing Carbon Footprint

AI systems are notoriously energy intensive. Consider these sobering statistics:

  • Training a single large language model consumes electricity equivalent to what a small city use annually
  • Global data center energy consumption is projected to reach 8% -12% of total electricity demand by 2030
  • The carbon footprint of AI workloads grows exponentially with model complexity

The Optimization Opportunity: AI as Environmental Game-Changer

Yet this same technology offers unprecedented capabilities for environmental optimization. AI can analyze vast datasets to identify inefficiencies, predict system failures before they occur, and optimize resource allocation in ways that would be impossible for human analysts alone.

Real-world example: IKEA leverages AI algorithms to estimate product longevity, optimize resale strategies, and guide consumers toward environmentally responsible purchasing decisions, resulting in a 15% reduction in product waste.

Another success story: Google’s DeepMind reduced data center cooling energy consumption by 40% through AI-powered optimization, according to Vert Energy Group analysis. This same approach is being applied to commercial buildings across industries, where AI optimization can reduce energy consumption through intelligent heating, cooling, and lighting management.
 
Now the question is how we’ll harness its potential while mitigating its costs.

5 Transformative Applications on the Horizon


1. Smart Grid Revolution: 

What is it: AI-powered energy management systems that predict demand, balance renewable sources, and optimize heating, cooling, and lighting distribution networks in real-time.

Business impact:

  • 30–50% reduction in energy waste
  • Improved renewable energy integration
  • Predictive maintenance prevents costly outages

Prediction: Within five years, I expect to see widespread deployment of AI systems that can predict energy demand with remarkable precision, automatically balance renewable energy sources, and optimize distribution networks in real-time.

Key technologies: Machine learning algorithms, IoT sensors, predictive analytics

2. Circular Economy Acceleration:

What is it: AI systems that optimize recycling processes, enable predictive maintenance, and extend product lifecycles through intelligent design.

Business impact:

  • AI-powered recycling revolutionizes waste sorting efficiency and accuracy, creating closed-loop systems that are fundamental to circular economy principles. Humans typically sort 50 to 80 items each hour, while an AI robot with optical sensors can sort up to 1,000 items per hour with greater accuracy, enabling more materials to be recovered and reused rather than sent to landfills.
  • 25% extension in average product lifespan
  • Closed-loop systems track and optimize materials throughout their entire lifecycle.

3. Supply Chain Transformation:

What is it: AI-powered supply chain analytics that track carbon hotspots, predict disruptions, and optimize logistics across multi-tier networks.

Carbon hotspots are points in the supply chain where emissions are significantly higher than average, often due to energy-intensive processes, transportation inefficiencies, or unsustainable sourcing practices. Identifying and addressing these hotspots can dramatically reduce the overall environmental impact.

Business impact:

  • Real-time carbon hotspot identification
  • 20–30% reduction in supply chain emissions
  • 30% improvement in operational efficiency and reduced product delays
  • Risk prediction and mitigation: This capability directly supports sustainability by preventing disruptions that often lead to emergency measures with higher environmental costs, such as expedited shipping or the use of alternative suppliers with less sustainable practices.

Innovation example: Companies are using AI to create digital twins of their supply chains, allowing them to test sustainable alternatives without real-world risks.

4. Precision Agricultural:

What is it: AI-driven farming solutions using drones, computer vision, and machine learning to optimize resource usage at the individual plant level.

Environmental impact:

Key technologies: Computer vision drones, soil sensors, predictive analytics, automated irrigation systems

5. Carbon Intelligence:

What is it: Advanced AI algorithms that predict carbon impact of business decisions, identify offset opportunities, and optimize operations for carbon neutrality.

Strategic advantage:

  • Real-time carbon impact assessment
  • Automated offset identification
  • Seamless integration into business processes

Prediction: Carbon considerations will be automatically integrated into every business decision, from procurement to product development.

Overcoming the Infrastructure Challenge

The most significant barrier to realizing AI’s sustainability potential is infrastructural. The current generation of data centers powering AI workloads is fundamentally misaligned with sustainability goals. However, this challenge is driving innovation in several key areas.

Emerging Solutions


Edge Computing Revolution

  • Reduces data transmission energy costs by 65–80%
  • Enables real-time AI processing without cloud dependency
  • Minimizes latency while maximizing efficiency

Specialized AI Chip Development

  • 10x improvement in energy efficiency for machine learning operations
  • Purpose-built hardware optimizing performance per watt
  • Reduced cooling requirements and space needs

Renewable-Powered Data Centers

  • By 2030, a third of data centers will produce their own power
  • Major cloud providers are committing to 100% renewable energy
  • Solar and wind-powered AI infrastructure
  • Carbon-neutral computing is becoming industry standard

The Measurement Revolution: Real-Time Sustainability Dashboards Beyond Annual Sustainability Reports

One of the most significant developments is the emergence of AI-powered sustainability measurement systems, which provide real-time visibility into environmental impact across all business operations. Traditional sustainability reporting, with its annual cycles and estimated metrics, will give way to continuous monitoring and dynamic optimization.

Key Features of Next-Generation Sustainability Platforms:

  • IoT sensor integration for real-time environmental data
  • Satellite imagery analysis for supply chain monitoring
  • Operational system connectivity for comprehensive impact tracking
  • Predictive analytics for proactive sustainability management

Business Value:

  • Real-time visibility into environmental impact
  • Data-driven sustainability decision making
  • Automated compliance reporting
  • Dynamic optimization opportunities

Strategic Implications for Business Leaders

For leaders, the intersection of AI and sustainability demands a fundamental shift in thinking. Sustainability can no longer be viewed as a separate function or compliance requirement. It must be integrated into the core business strategy and operations. AI provides the tools to make this integration not just possible, but profitable.

Organizations that successfully harness AI for sustainability will gain competitive advantages in multiple dimensions: operational efficiency, risk management, brand value, and regulatory compliance. Those that fail to make this transition risk being left behind as stakeholders increasingly demand environmental accountability.

The timeline for this transformation is compressed. While full deployment of AI-powered sustainability systems may take a decade, the competitive advantages will begin accruing much sooner. Companies that start building these capabilities now will be positioned to lead in the sustainable economy of the future.

The Future Belongs to AI-Powered Sustainability

The convergence of artificial intelligence and sustainability represents a significant business transformation opportunity. Organizations that successfully harness AI for environmental stewardship will gain substantial competitive advantages across multiple dimensions.

The path forward requires investment in technology and talent, as well as partnerships across industries, and a commitment to transparency regarding environmental impact. For those willing to embrace this challenge, the rewards extend far beyond compliance or cost savings. They can build more resilient, more valuable, and more sustainable businesses for the future.

AI will transform sustainability. Will you be part of leading that transformation or struggling to catch up? The future belongs to those who can harness artificial intelligence to optimize their operations and impact on the world.

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A Look at the Future of Advanced Compute and Clean Energy

The global technological landscape is undergoing a transformation driven by several converging forces.

These emerging innovations represent not just incremental improvements, but potential paradigm shifts that could fundamentally alter industries, economies, and societies.

While attention often focuses on established trends, it’s the subtle indicators — the weak signals — that offer the most valuable insights into future disruptions. These early indicators, though not yet mainstream, carry significant implications for strategic planning and competitive advantage.

What trends are we seeing?

The tech landscape is constantly shifting, composed of established trends as well as weak signals. Some trends may be game changers while others are foundational breakthroughs, along with a few wild cards still in their nascent phase. Here are some of those trends we are watching closely.

  • Artificial General Intelligence (AGI)
  • Humanoid robots
  • Quantum computing
  • 6G Networks and hyperconnectivity
  • Advanced compute
  • Nuclear energy
  • Biotechnology and synthetic biology
  • Next generation energy storage
  • Clean tech
  • Space tech

Today, I’m diving into two of these trends: advanced compute and clean tech and energy.

Advanced compute

Driven by AI, the increasing demand for computing power is colliding head-on with energy constraints that challenge this infrastructure build out. The result? We will need advancements in both energy efficiency and energy creation to keep up. Innovations such as microfluidic-cooled chips and composable computing architectures are driving computing efficiency and performance to new heights.

At the same time, data centers, cloud computing, and other high-performance applications are placing immense pressure on the global energy supply, pushing the need for cleaner, more reliable energy sources. We’ve seen numerous clean tech products and services focused on energy efficiency and sustainable reuse, and you can expect this trend to accelerate.

The question is: How quickly can we implement solutions that strike a balance between technological growth and sustainability? The interaction between computing and energy production is becoming one of the most critical challenges and opportunities of the modern era.

Compute is getting smarter — and hungrier

Computing advancements are reshaping industries, bringing both efficiency gains and new challenges.

The evolution from massive supercomputers to high-performance, compact chips makes processing power more accessible and scalable, enabling more sophisticated AI models, data analysis, and automation.

These innovations drive digital transformation across sectors, from healthcare to finance, but they also come with a steep energy demand. AI-driven applications, especially large-scale models, require immense computing power, leading to a surge in electricity consumption and an urgent need for clean energy solutions to support this growth.

Today, data centers account for 1% to 2% of overall global energy demand, similar to what experts estimate for the airline industry. When costs related to delivering AI to the world is factored in, that figure is expected to hit 21% by 2030.

Moreover, the International Energy Agency projects that data centers will use 945 TWh of electricity in 2030, roughly equivalent to the current annual electricity consumption of Japan according to Nature.

New energy sources, storage, and compute power

This is resulting in looking at new energy sources. Including a revival of nuclear power, which today accounts for nearly 10% of the world’s electricity but could grow significantly in the coming years due in part to its low-carbon footprint.

That’s why Small Modular Reactors (SMRs) from startups such as Nuscale and TerraPower are stepping into the spotlight as a potential answer to powering AI-driven data centers, offering a steady and reliable energy source that can be deployed more flexibly than traditional nuclear plants. These reactors can generate consistent, carbon-free electricity, making them an attractive option for reducing the environmental footprint of high-performance computing. SMRs can generate up to 300 MW per unit, which is significant when considering that a typical hyperscale data center requires 20–50 MW of power capacity, making them an ideal power source for AI-driven supercluster data centers.

Similarly, looking to other sustainable energy innovations in solar, wind, and gas will be needed for AI to advance. But it’s not just how we create power but also store and distribute it that will be key to new compute models. Solid-state batteries, or SSBs, could play a crucial role in grid storage applications needed to power the future computing needs of AI. While initially being developed for electric vehicles, these next-generation energy storage solutions have the potential to support gri-dscale applications, helping bridge the gap between fluctuating renewable energy sources and computing technology’s increasing power demands. In fact,the solid state battery market is expected to grow at a compound annual growth rate (CAGR) of 33%, with commercialization efforts ramping up.

Advanced Compute Breakthroughs

The energy challenge is also driving innovation in computing itself. New processor architectures designed specifically for AI workloads are dramatically improving performance while reducing energy consumption.

Neuromorphic computing, which mimics the efficiency of the human brain, shows promise for reducing energy requirements by orders of magnitude compared to traditional computing approaches. Research from Intel’s Neuromorphic Research Lab demonstrates that neuromorphic systems can be up to 1,000 times more energy-efficient than conventional architectures for certain AI workloads.

Quantum computing developments could revolutionize how we approach certain computational problems, potentially solving in seconds what would take today’s supercomputers years, all with a fraction of the energy.

These compute breakthroughs, paired with energy innovations, will be essential to sustainable AI growth.

The Power Players

The compute and clean energy race is full of power players. Here’s where innovation is heating up:

Advanced Compute

Clean Tech

Nuclear Energy

Next-Gen Energy Storage:

Unexpected outcomes

The AI energy equation could take unexpected turns in coming years:

What if AI algorithms emerge that drastically reduce computational requirements? Some researchers are exploring “small language models” that deliver impressive results with far less computing power, similar to how the human brain achieves remarkable efficiency.

IBM, Google, Microsoft, and OpenAI have all recently released small language models (SLMs) that use a few billion parameters — a fraction of their bigger LLM counterparts.

Geopolitical implications could be profound if certain nations successfully implement nuclear or other sustainable energy solutions for AI infrastructure while others remain reliant on fossil fuels. Countries that solve the energy puzzle could gain significant advantages in the AI arms race, potentially reshaping global power dynamics.

Could success in one domain–either compute efficiency or clean energy–accelerate the other in a virtuous cycle? Or might we face a scenario where breakthroughs in AI capabilities consistently outpace our ability to power them sustainably?

A global shortage of critical minerals like lithium could emerge as a limiting factor, constraining the growth of both advanced computing and clean energy technologies.

These minerals are essential for producing high-performing batteries, semiconductors, and energy storage systems–components that power AI data centers, renewable energy grids, and more.

AI’s promise could become a paradox if we don’t solve the energy storage issue. We will have smarter tools powered by unsustainable systems.

Moving forward

As computing power and energy needs evolve, the intersection of advanced compute and clean energy will shape the next wave of technological and environmental progress.

The race to develop sustainable, high-performance computing solutions is accelerating, and innovations in energy efficiency, storage, and nuclear technology will define the next decade.

Will we rise to meet the energy demands of intelligent machines? Or will innovation outpace our ability to power it? I want to hear your thoughts on what breakthrough or roadblock you see defining the next decade.

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A Day-In-The-Life with Generative AI Part 2: Maximizing Efficiency

Generative AI is becoming embedded in our everyday lives and transforming how we approach daily tasks and activities. With AI seamlessly integrated into our routines, we can experience efficiency and personalization that was once the realm of science fiction.

Remember Aiden, a 26-year-old living in San Francisco, who weaves AI into her daily life to optimize her productivity and enhance her experiences? She’s not the only one leveraging generative AI to create a more streamlined daily life.

Meet Dylan, a 40-year-old corporate executive who expertly uses AI to be more productive, achieve his fitness goals, and maximize his free time.

Morning Start

GIF showing AI-generated vivid dream replay. Very colorful

Dylan enjoys an AI-generated replay of a vivid dream he had, complete with visual and narrative details, providing creative inspiration and insights as he starts his day.

Digital news feed from AI

A customized AI-generated news briefing, with curated headlines and stories based on his preferences and current interests sets a focused tone for Dylan’s day.

bike workout screen with data from AI trainer

After the news briefing, Dylan’s personalized AI trainer analyzes his recent workout data and suggests a customized exercise routine, including specific cardio and strength training exercises to help him meet his fitness goals.

Efficient Afternoon

food shopping bag ordered by AI chef

Dylan’s AI Chef orders culinary ingredients according to his AI customized meal plan and schedules delivery, ensuring he has fresh produce and essentials for later, delivered by an AI robot personal shopper.

smart watch on wrist sending emails automatically

While in a meeting, Dylan asks his AI assistant to draft and send follow-up emails, including a proposal for a new project. This allows him to focus on strategic planning while the AI handles the communications.

Engaging Interactions

Virtual Reality meeting with Madam C.J. Walker

During a staff meeting, an AI-powered digital twin of Madam C.J. Walker joins Dylan’s team for a Q&A session. Walker shares insights into her entrepreneurship, philanthropy, and social activism approaches.

Virtual Tutor

Dylan takes a break to learn about AI advancements via a virtual tutor, exploring topics like machine learning algorithms and their practical applications in his industry.

Wearable device capturing meeting notes

His AI wearable device discreetly reminds him of key contacts he met earlier, captures important meeting notes, and follows up on promised actions.

Evening Relaxation

Chicken and broccoli meal prepared by a Robot

As he winds down from work, Dylan’s robot sous-chef prepares a gourmet dinner, following a recipe from his meal plan, while he relaxes and catches up on personal projects.

Personalized movie on a TV screen

Dylan ends his day watching a movie tailored to his tastes and current mood, with AI creating a personalized viewing experience with a custom storyline.

Generative AI can transform how to manage tasks, access information, and enjoy leisure time. It’s not just about efficiency; it’s about creating a more personalized and enjoyable experience.

How do you envision generative AI transforming your routine and bringing more ease and excitement into your daily life?

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A Day-In-The-Life with Generative AI: A Glimpse into the Future

Technology is seamlessly integrated into our daily routines, and generative AI will revolutionize how we live and work. Let’s imagine a day in the not-too-distant future where generative AI doesn’t just serve as an assistant but as an active participant in every aspect of your life.

Meet Aiden. As a 26-year-old living in San Francisco, Aiden spends her time working at a healthcare startup, socializing with friends, and focusing on hobbies like fitness and reading. Here’s a peek into what a typical day for her might look like with generative AI:

Morning Routine

AI trainer delivers a custom workout plan

Aiden likes to exercise before she starts her day. Her AI trainer sends her a customized workout plan each morning and adjusts her exercise plan based on her current fitness goals.

Digital twin of women attending a virtual meeting

After she finishes her workout, Aiden is ready to start her workday. A morning meeting pops up that could have been handled as an email. Aiden sends her digital assistant to attend the online meeting on her behalf.

Aiden’s digital twin participates by handling routine discussions and updates. As soon as the meeting is complete, her digital twin sends Aiden the meeting notes, key takeaways, and action items.

flying car over Oakland, CA

While her digital twin is in the meeting, Aiden takes a flying car to a face-to-face meeting in Oakland with her boss. With this commute, she cuts down on travel time and avoids traffic on the ground.

Robot feeding a black and white husky dog in a living room

Aiden attends her face-to-face meeting in Oakland, while a humanoid robot manages her household chores back at her apartment – feeding her dog, cleaning her kitchen, folding her laundry, and preparing her lunch. By offloading mundane tasks, Aiden can focus on more high-level tasks, such as in-person meetings and sharing her latest strategy ideas.

Health, Wellbeing, and Lunch

watch showing vital signs being captured and sent to an AI doctor

Aiden continues to work while wearing her AI wearable. Her AI doctor monitors her health statistics and raises a couple of irregularities to Aiden’s human doctor, who is based in Sacramento. When she receives the information, her human doctor sends a report to Aiden with key takeaways, updated prescriptions, and health information.

The meeting concluded in Oakland, and Aiden got a promotion! She’s thrilled and heads back to the City for lunch. On her way home, she sends a request to her AI Assistant to invite her friends for happy hour at her apartment to celebrate her promotion.

Once Aiden returns to her apartment, she enjoys the BLT her humanoid robot prepared for lunch while she answers work messages.

Happy Hour

Young people dancing in a living room

As Aiden’s friends arrive to celebrate her promotion, her AI generates a celebratory playlist for the happy hour.

Drone delivering pizza

Aiden’s friends toast her accomplishment, and in the background, her AI assistant places an order for pizza delivery which her humanoid robot receives and brings inside. Enjoy!

Evening Routine

AI assistant lowers the lights in a living room

Aiden’s friends head home, and her AI assistant adapts the lighting and sound to her relaxation needs, offering her a comfortable and personalized environment to unwind. She settles into the couch to watch a series curated specifically for her previous viewing preferences.

Person touching screen and selecting next day tasks for AI assistant

Before she goes to sleep, Aiden delegates the household and work tasks she would like her AI assistant, digital twin, and humanoid robot to complete tomorrow.

Generative AI promises a future where technology enhances our routines, making our lives more efficient and enjoyable. From handling mundane tasks to offering personalized experiences, AI is set to become an integral part of our daily existence, turning futuristic visions into everyday realities.

How do you envision generative AI reshaping your daily life as we move toward this future?

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Revolutionizing daily lives: The future of AI wearables

Integrating artificial intelligence (AI) with wearable technology is poised to revolutionize our daily lives, blurring the boundaries between the human body and digital interfaces.

The AI wearable market is currently experiencing significant growth. According to Market.us, The Wearable AI Market is estimated to reach USD 304.8 billion by 2033, with a robust CAGR of 25.6% over the forecast period 2024-2033.

As we experience this explosive growth, let’s delve into the possibilities and implications of this technological evolution.

The evolution of wearables

Wearable technology started with basic fitness trackers and quickly moved to sophisticated smartwatches that monitor many health metrics. In 1977, Hewlett Packard released the HP-01 watch, which is considered the first instance of a mass-market wearable.

At the time, the ground-breaking, 28-key interface provided an alarm, data types, a timer/stopwatch, dynamic calculations, and more.

Over the next couple of decades, into the early 2000s, the miniaturization of electronics began. We saw devices that produced music transform from Walkmans to CD players to MP3 players.

This change brought about portability, making it easier to carry electronics around and ushering in a new era of wearables. During this time, cameras were also reduced tremendously in size from their origins, allowing them to be worn like GoPros, and recording was embedded into sunglasses, such as Snapchat’s Spectacles.


The next leap was embedding AI into these devices, enabling them to learn from our behaviors, predict our needs, and even intervene in real time to enhance our well-being.

Advancing health and wellness

As AI-powered wearable devices become more than passive trackers, they become proactive health advisors, continuously analyzing biometric data to provide personalized health insights. AI and wearable data can yield up to a 23.8% improvement in health prediction performance.

Research from MIT and Google shows that large language models (LLMs) can be trained on wearable sensor data based on contextual information, such as user demographics and health knowledge, and physiological data, including resting heart rate and sleep minutes, to deliver personalized, multi-modal health predictions.

AI-enabled wearables can then use this data to predict potential health issues before they manifest, recommend lifestyle changes, and even alert medical professionals in case of emergencies.

Oura has also made waves in the health and wellness space. The company’s ring translates our body’s most meaningful messages from 20 biometrics, including sleep, activity, stress, and heart health.

Enhanced cognitive abilities

AI wearables also have the potential to augment our cognitive functions. Devices can assist us with memory enhancement, language translation, and real-time information retrieval. Imagine wearing a sleek, discreet device that provides you with the names and details of people you meet at a conference or instantly translates foreign languages as you travel, breaking down communication barriers and fostering global connectivity.

AI startup Limitless will soon be launching a broach-like wearable device that records and processes conversations. The small device clips onto a shirt collar and sends recordings to a secure, encrypted, and AI-enabled cloud service, allowing you to remember and recall the conversations you’ve had and the people you’ve had them with.

This could also be helpful for a day when you are in back-to-back meetings or unable to take notes. The device can help you track action items and better connect with the person in front of you.

As AI advances, it won’t be long before we can wear a device that can record and summarize your conversations, create a to-do list, and automatically schedule a follow-up meeting with key stakeholders.

Seamless human-technology interaction

The future of AI wearables’ success lies in their ability to integrate seamlessly into our daily routines. Voice-activated assistants, gesture recognition, and even brain-computer interfaces will make interacting with technology as natural as breathing.

AI wearables could become an extension of our senses, intuitively understanding and responding to our needs.

Ethical and privacy considerations

With this great power comes great responsibility. The widespread adoption of AI wearables raises ethical and privacy concerns. As devices collect vast amounts of personal data, robust security measures to protect user privacy are necessary. Transparent data usage policies and stringent regulations will be crucial to ensure that the benefits of AI wearables do not come at the cost of our personal freedoms.

The future workplace

AI wearables will transform how we work. For example, AI-enabled wearables could monitor your stress levels and suggest short breaks or mindfulness exercises to maintain optimal performance.

Social and cultural impacts

The integration of AI wearables into everyday life will have profound social and cultural impacts. These devices will redefine how we connect with each other, potentially reducing the digital divide and fostering inclusivity. However, if access to advanced wearables becomes a privilege of the few, it may also exacerbate existing inequalities. Addressing these disparities will be essential to ensure that the benefits of AI wearables are equitably distributed.

Embracing the future

As we stand on the brink of this transformative wave in AI wearables, we must approach it with a balanced perspective, embracing the potential benefits while remaining vigilant about the ethical and societal challenges.

The future of AI wearables is about technological advancement and enhancing the human experience, empowering us to live healthier, more connected, and ultimately more fulfilling lives.

Envision a world where AI wearables are not merely gadgets but integral components of our daily existence, seamlessly woven into the fabric of our lives. The journey ahead is filled with promise, and it is up to all of us to navigate it wisely.

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