Innovation Awards Criteria

Sustainability = Innovation 🌍 Environmental and social pressures are reshaping how companies approach growth, risk, and competitiveness. When strategically integrated, sustainability becomes a framework to identify operational inefficiencies, anticipate future demands, and respond to evolving market conditions. The starting point is recognizing how sustainability issues reveal opportunities for innovation. Rising input costs require rethinking material choices and supply strategies. Climate risk drives the need for resilient product design. Regulation, customer expectations, and resource constraints all point toward reconfiguring business models and value chains. Each business function faces specific triggers. Operations teams respond to inefficiencies in energy or water use. Procurement can reduce exposure by transitioning to circular sourcing. Product development must address the growing demand for low footprint design. Sales and marketing teams face increasing pressure from clients and regulators to demonstrate real, measurable impact. Several innovation pathways are already proving effective. These include redesigning products with lower impact materials, modular components, and take back systems. Business model shifts such as repair programs, resale strategies, and service based delivery models can extend product value. Digital tools enable smarter operations and transparency for customers. Functional teams require clear prompts to connect sustainability to their daily work. Operations can identify areas where reducing emissions also cuts costs. R&D teams should explore how to design for circularity from the beginning. Sales teams can develop solutions that align with client ESG targets. Finance can evaluate payback periods and risk adjusted returns. HR can focus on building a culture of sustainable problem solving. Impact measurement is essential to validate innovation efforts. Metrics may include revenue from sustainable offerings, product carbon intensity, emissions avoided, client retention linked to ESG solutions, and time to market for low impact products. Implementing innovation at scale requires specific tools. These include life cycle assessment platforms, circular design processes, materiality assessments, innovation accelerators, and sustainability linked finance instruments to fund new initiatives. Sustainability driven innovation is a strategic process embedded across the business. It enables long term value creation by aligning environmental and social imperatives with product, process, and business model development. #sustainability #sustainable #business #esg #innovation

Long-term innovation in human flourishing will not come from seeking the next Med-tech Unicorn. Planting an orchard or vineyard does not show immediate return on investment. It does, however, provide great fruit and great benefit for future generations. Our vision must be long-term. A few numbers (CDC) regarding annual economic burden of non-communicable diseases: 1. Heart disease: $219 billion 2. Pulmonary disease: $153 billion 3. Obesity: $147 billion 4. Diabetes: $327 billion 5. Mental Health: $238 billion A 2% reduction in these five disease states would save the US $21.68 billion annually. Let's not seek the next unicorn in treating disease. Let's reduce disease by 2%. A small percentage is a big number (and many, many lives). This societal pain and burden can be reduced. Both for individuals and our over-stressed healthcare systems. The vision is creating an ecosystem purposely stewarded for flourishing. One which uses all available tools we have (AI, behavioral economics, cognitive computing, social networks/social physics, neuroscience, etc.) to reduce these numbers by 2%. The focus is not an input, a tech platform, or improving a legacy model. The focus is an outcome. An ecosytem of flourishing with success measured by avoidance and reduction of disease. This innovation must be supported, resourced, and stewarded by our largest brands, most advanced tech companies, smartest scientist, and civil society. The vision must be long term and we must build to benefit future generations. Economic gain is a worthy pursuit. This is true. I would argue human flourishing is of greater value. Value we can achieve.  It is not a luxury; it is our truest identity. Economic gain will follow. #Leadership #Innovation #Flourishing #Healthcare

This is a pivotal time for business leaders to apply strategic foresight and systems thinking. Go beyond tariffs and stock market trends and consider the broader, longer-term impacts: 1. How might a trend toward AI deregulation in product safety affect the AI products my business relies on? 2. In what ways could shifts in immigration policy influence my workforce strategy for maintaining a competitive edge with emerging technologies? How could these policies reshape PhD talent pipelines? 3. How will evolving U.S. geopolitical relationships impact my third-party suppliers and global partnerships? 4. With the increasing influence of techno-politics, what new considerations emerge for my business strategy? Scenario planning is key in moments of change and uncertainty.

🌍 What if the very systems driving our progress are also accelerating our greatest challenges? I recently read an article titled “The System Dynamics Approach for a Global Evolutionary Analysis of Sustainable Development” by Feder, Callegari, and Collste, which uses the Earth4All model to explore this question. The study offers a sobering but vital perspective on how environmental, social, and economic systems are deeply interconnected—and how this interplay shapes our future. Here are the main findings: 📉 Unsustainable Development Erodes Resilience The study shows that our current development trajectory creates “macro-selection pressures” that harm global resilience. Environmental challenges like climate change and resource depletion degrade economies, while social issues like inequality and declining well-being weaken societies’ ability to adapt and innovate. 🌍 Interconnected Forces Amplify Risks These challenges are not isolated—they interact in feedback loops that compound their effects. For instance, environmental degradation increases inequality by raising the cost of resources, while inequality slows the social reforms needed to combat climate change. Together, they create cycles that destabilize long-term systems. 🔄 Delaying Action Increases the Costs The Earth4All model vividly illustrates how delays in tackling these issues lead to cascading crises. By the time the full effects are visible, reversing the damage becomes far more difficult and expensive. Here are some of my own reflections on this study: 💡 Systemic Interplay This research reinforces something I’ve long believed: we can’t address environmental, social, or economic challenges in isolation. They are interconnected. For example, policies focused solely on short-term economic gains often overlook their environmental costs, which eventually undermine the economy itself. Solutions must integrate these systems to work sustainably. 📊 The Role of Models Like Earth4All I’m a strong advocate for the Earth4All model because it provides a clear and integrated view of these complexities. It connects natural, social, and economic systems into one framework, helping us see the long-term impact of today’s decisions. For policymakers and leaders, it’s an invaluable tool to guide strategy. 🎓 Learning and Awareness Finally, this study reminded me of the importance of understanding complexity. Tools like Earth4All empower us to move beyond short-term fixes and focus on systemic solutions. Awareness is the first step toward meaningful action. To learn more about this model, I would highly recommend visiting their website: (https://earth4all.life/) If you’re interested, you can read the full article here: https://lnkd.in/e5tRft63 🌐 How do you see the interplay of these systems shaping the challenges in your field? #Sustainability #SystemsThinking #ClimateAction #SocialEquity #FutureOfEconomics 🌟

Are we measuring the wrong things in drug innovation? Some of the most valuable therapies might never show up on our innovation radar. The typical view in US #biopharma has long equated “innovation” with patents, new drug approvals, and R&D spend. They're easy to count and look good in investor decks. However, these metrics often reward volume more than total value. They don't tell us whether a therapy meaningfully improves patient lives, strengthens public health, or delivers returns beyond the financial metrics. A new six-dimensional framework published in The Incidental Economist offers another option. Drawing from over 600 interdisciplinary studies, the authors propose a more rigorous definition of #innovation: - Scientific and Technological Advances: Captures innovation and productivity using metrics such as new molecules, new drug applications, and patents. Emerging indicators, such as AI-enabled R&D and digital biomarkers, offer forward-looking insights. - Clinical Outcomes: Highlights therapeutic impact through metrics such as safety, efficacy, and patient-reported outcomes, emphasizing real-world patient benefits and delays in disease progression. - Operational Efficiency: Measures efficiency in development and production using trial success rates, R&D timelines, supply chain resilience, and adaptive trial designs. - Economic and Societal Impact: Evaluates economic returns and societal benefits through cost-effectiveness analyses, budget impacts, and productivity improvements. - Policy and Regulatory Effectiveness: Assesses how regulatory frameworks support innovation through approval speed, breakthrough designations, and surrogate endpoint integration. - Public Health and Accessibility: Examines broader health impacts, including reduced disease incidence, healthcare access improvements, and equitable geographic distribution, ensuring innovations meet widespread public health needs. This doesn't have to just be academic. It could change what gets funded, approved, and reimbursed. Some examples mentioned in the article: -An Alzheimer's therapy might look risky on paper, but when viewed through long-term productivity gains and reduced caregiver burden, it becomes a more attractive, high-risk/high-reward bet. -A platform technology (e.g., mRNA) may not boost new molecule counts today, but could enable faster, more precise drug development in the future. -A one-time gene therapy with high upfront cost could prove more valuable than chronic treatments when lifetime adherence and hospitalizations are factored in (if payers can afford the upfront investment). Of course, expanding how we define innovation introduces trade-offs. Complexity increases. Metrics will compete against each other. The question is whether the upside of greater alignment with ALL stakeholders is worth the operational complexity and potential reductions in value for some individual stakeholders. Would you be in favor of evaluating innovation more holistically?

We are no longer in a normal innovation cycle. Several major technologies are scaling at the same time. Automation, software and computational biology are advancing together, not sequentially. Previous cycles digitized information. This one embeds decision-making directly into operating systems. Intelligence runs continuously. Decisions that once took hours or days are executed in seconds. Many are still applying linear thinking to a compounding system. This acceleration is not driven by a single breakthrough. It comes from interaction effects. Compute shortens development cycles. Software designs hardware. Automation reshapes workflows end to end. The defining operational shift is persistence. Training large models still absorbs peak capital, but the economic impact now comes from systems that run continuously. When decision-making never turns off, the constraint moves from software to physical delivery. Power is the binding constraint. Software improves in months. Grid upgrades, density increases, and physical deployment take years. That mismatch sets the pace of adoption. It cannot be optimized away. This is why the challenge is no longer technological. It is executional. Research from innovation-focused investors like ARK describes the current period as an acceleration driven by automation, productivity gains, and digital networks rather than isolated product cycles. Infrastructure alone does not guarantee success. It never has. Capital-rich incumbents have failed in every prior industrial transition. Organizations that fall behind will not do so because they lacked access to technology. They will fall behind because they underestimated deployment complexity and overestimated the time available. Labor is changing. Automation now hits coordination, scheduling, logistics, engineering workflows, and scientific research. This does not remove work. It compresses decision cycles. Value shifts from individual output to system design and integration. Digital assets matter for one reason: settlement. The change is not price volatility but financial plumbing. Tokenization and programmable settlement reduce friction in capital markets much as standardized containers did in global trade. Settlement times compress. Capital moves faster. Biology follows the same pattern. Compute-driven protein folding, gene editing, and multiomic analysis shorten development timelines that once took decades. Drug discovery shifts toward computational design. Not all advanced technologies matter on the same timeline. Quantum remains longer-dated. The economic impact of this next 5 years comes from compute at scale. Advantage accrues to those who can execute. The macro outcome is likely higher productivity growth, unevenly distributed. History is unforgiving. In every major industrial transition, failure came less from misunderstanding the technology than from misjudging timing. Today, the most dangerous assumption is that you still have time. #ai

This paper on AI from the Fed is the single best thing to read for a snapshot of: 1) The recent innovations in generative AI 2) How firms are using AI 3) The likely macro implications of AI I can't often can bring myself to fully read long reports, but this one is well worth it. Abstract: With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain. The effect of the technology on the innovation process is a crucial open question. Some inventions, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not. In contrast, two types of technologies stand out as having longer-lived effects on productivity growth. First, there are technologies known as general-purpose technologies (GPTs). GPTs (1) are widely adopted, (2) spur abundant knock-on innovations (new goods and services, process efficiencies, and business reorganization), and (3) show continual improvement, refreshing this innovation cycle; the electric dynamo is an example. Second, there are inventions of methods of invention (IMIs). IMIs increase the efficiency of the research and development process via improvements to observation, analysis, communication, or organization; the compound microscope is an example. We show that genAI has the characteristics of both a GPT and an IMI—an encouraging sign that genAI will raise the level of productivity. Even so, genAI’s contribution to productivity growth will depend on the speed with which that level is attained and, historically, the process for integrating revolutionary technologies into the economy is a protracted one. #AI #Fed #rates #macro #economics

Inspired by current debates, such as a New York Post piece that highlighted futurist Ray Kurzweil's forecast that human immortality could be achieved by 2030 with the use of technologies like nanobots, this commentary offers an incredibly thorough examination from the viewpoints of developmental economics and sustainability thought leadership. It examines the profound, interrelated effects on legal frameworks (contracts, inheritance), social structures (family, marriage, ageism, safety nets, stratification), economic systems (insurance, loans, salaries, medical provision), and the profound psychological effects of prolonged life. Importantly, this updated article also includes a focused analysis of the intersections between the possibility for radical human lifespan and the current strong trajectory of AI and robotic surrogate development. It makes the case that this convergence offers not only previously unheard-of difficulties with regard to labour markets, equity, and what it means to be human, but also a singular chance to spark a fundamental change in the direction of sustainable, just, and circular global systems, motivated by the personal significance of long-term effects. #RadicalLongevity #Immortality #AI #Robotics #FutureOfWork #Sustainability #DevelopmentalEconomics #CircularEconomy #Ageism #HealthcareFuture #SocialImpact #LegalReform #PsychologyOfLongevity #Inequality #FamilyStructures #EthicalTech #GlobalChallenges #Foresight #PlanetaryStewardship #Humanity20

The transistor was invented in 1947. It took decades to change the world. That's the lesson policymakers keep missing. Bell Labs — which turned 101 last year — produced an astonishing cascade of inventions: the transistor, communications satellites, cellular networks, solar cells, digital imaging. The New York Times just ran a beautiful retrospective (written by Steve Lohr, Jeanne Noonan-DelMundo, and Tina Zhou; 🎁 link in comments) on all of it. But here's what the Bell Labs story really teaches us about technology competition: Invention ≠ impact. The gap between the two is measured in decades. And what fills that gap is sustained national commitment — R&D funding, workforce development, infrastructure, and policies that outlast any single administration. The solar cell? Invented 1954. Market flop for decades. It only became viable when semiconductor manufacturing costs plummeted and government subsidies arrived. The Picturephone? Debuted 1964. Failed spectacularly. The same concept became ubiquitous 40 years later when smartphones and broadband caught up. The pattern holds for every major technology: there's a long valley between breakthrough and ubiquity, and policy is what builds the bridge. Post-Sputnik, America understood this. The federal government funded 70% of U.S. R&D. Created NASA and DARPA. Passed the National Defense Education Act. Made generational investments that are still paying dividends today. We've lost that discipline. Federal R&D spending has dropped to ~0.7% of GDP. Our share of global R&D has fallen from 69% to 28%. And we still don't have a coherent national technology strategy — just a patchwork of reactive measures and disconnected initiatives. Meanwhile, China has been executing long-term technology strategies — Made in China 2025, Standards 2035, the Belt and Road Initiative — backed by R&D spending growing 15% annually. Technology competition isn't won in the year a breakthrough happens. It's won in the decades that follow, through patient, sustained, bipartisan commitment to the ecosystem that turns inventions into industries. We need technology policies that transcend election cycles. We need a national technology strategy — one that promotes American competitiveness, protects critical advantages, partners with allies, and plans for the long haul. The transistor didn't change the world overnight. Neither will AI, quantum computing, or any other breakthrough. The question is whether we'll build the bridge again. Video: Bell Labs, 1962 — launching Telstar #TechPolicy #NationalSecurity #Innovation #Semiconductors #AI #USChinaCompetition

New research with the brilliant Likun Cao, out just today in Technological Forecasting & Social Change: For decades, innovation scholars have debated whether deep technological search (intensive work within a domain) or broad search (combining distant knowledge) drives greater technological impact for the companies that pursue them. Studies keep finding conflicting results. We think we've identified why: they were measuring impact at different time horizons. Using machine learning to map 4.9 million U.S. patents in hyperbolic space, we tracked how citations accumulate over 20 years. The pattern is striking: Deep search drives higher short-term impact—specialized communities recognize and adopt the work quickly. But returns diminish as innovations become "locked in" with limited diffusion potential. Broad search faces initial resistance—category-spanning work is harder to evaluate. But it reaches wider audiences over time and achieves greater long-term impact. The "foundational" and "tension" views of innovation aren't contradictory. They capture different phases of the same process. For R&D strategy: individual inventors and resource-constrained firms may benefit from depth's faster feedback. Larger organizations can alternate—using deep work to build reliable components that later fuel broader exploration. And successful organizations do! Paper: https://lnkd.in/gSFzJRyG