Overview

This essay argues for grounding the beneficial uses of AI in human wellbeing and the health of our social institutions. It advocates a pragmatic middle path between optimism and pessimism about AI, and emphasizes that there is no single universal definition of wellbeing. Yet it identifies concrete factors that contribute to a good life—supportive relationships, meaningful work, growth, and positive emotions—and underscores the importance of societal infrastructure (education, government, the market, and academia) in shaping wellbeing over time. The authors warn that AI can affect wellbeing for better or worse and stress the need to align AI development and deployment with wellbeing objectives. A core conclusion is that we need plausible positive visions of a society with capable AI, grounded in wellbeing. Like past transformative technologies, AI will disrupt our social infrastructure and daily life in profound ways. The authors argue we must imagine, envision, and actively build AI-infused worlds that strengthen institutions, empower meaningful pursuit, and nurture relationships. Acknowledging the rapid progress of foundation models, they further contend that deployment trajectories matter: we should strive to ensure models understand wellbeing and can support it, potentially via new algorithms and wellbeing-aware training data. The final sections outline strong leverage points to move from vision to practice. The piece also frames a concrete research agenda: (1) what we mean by AI that benefits wellbeing, (2) why we need positive visions anchored in wellbeing, and (3) concrete leverage points to guide AI research, development, and deployment. The authors survey the science of wellbeing, noting both its breadth and lack of consensus, and argue that progress can still be made by grounding efforts in workable measures of wellbeing and lived experience. For instance, the literature highlights factors like relationships, meaningful work, growth, and positive emotional experiences, and suggests integration with societal infrastructure to sustain wellbeing across years and decades. See the article for fuller discussion: https://thegradient.pub/we-need-positive-visions-for-ai-grounded-in-wellbeing/.

Key features (bulleted)

Ground AI benefits in real-world wellbeing outcomes and in the health of societal infrastructure (education, government, markets, academia).
Adopt workable wellbeing measures when guiding AI systems (e.g., PERMA concepts) while recognizing that the map is not the territory.
Consider long-term wellbeing across time horizons, not just short-term gains.
Treat foundation models and their deployment as a critical lever with the potential to reshapes life and institutions.
Seek plausible, positive visions of AI-enabled futures that enhance relationships, meaning, and engagement.
Propose concrete leverage points for research and product design that integrate wellbeing considerations into models and data.
Encourage cross-disciplinary engagement between wellbeing science and machine learning to align incentives and evaluation.

Common use cases

Low-cost but proficient AI coaches that support personal growth and reflection.
Intelligent journaling tools that help self-reflection and progress tracking.
Apps that help people connect with friends, partners, or loved ones and strengthen relationships.
Tools that assist in aligning daily activities with personal values and long-term wellbeing goals.

Setup & installation

# Retrieve the original article for offline reading
curl -L -o thegradient_ai_wellbeing.html "https://thegradient.pub/we-need-positive-visions-for-ai-grounded-in-wellbeing/"

# Alternative retrieval (no dependencies)
wget -O thegradient_ai_wellbeing.html "https://thegradient.pub/we-need-positive-visions-for-ai-grounded-in-wellbeing/"

Quick start

Minimal runnable example: a toy wellbeing score using PERMA-like factors

# Simple PERMA-inspired wellbeing score
def wellbeing_score(positive_emotions, engagement, relationships, meaning, achievement):
return (0.2 * positive_emotions +
0.2 * engagement +
0.2 * relationships +
0.2 * meaning +
0.2 * achievement)
print(wellbeing_score(0.8, 0.6, 0.7, 0.9, 0.5))

This illustrates how a simple, composite score might be used to evaluate AI features through a wellbeing lens. The original article points to using such frameworks (e.g., PERMA) as workable anchors for measurement while acknowledging theory fragmentation in wellbeing research.

Pros and cons

Pros
Aligns AI with human flourishing and lived experience.
Provides concrete, actionable metrics for researchers and policymakers.
Encourages cross-disciplinary collaboration between wellbeing science and ML.
Highlights the importance of long-term societal infrastructure in sustaining benefits.
Cons
Wellbeing is contested with multiple theories and proxies; no single definition fits all contexts.
Proxies may misrepresent true wellbeing if not chosen carefully.
Measuring wellbeing robustly in AI systems requires governance, transparency, and ongoing evaluation.
Achieving positive visions requires coordinated action across institutions and sectors.

Alternatives (brief comparisons)

| Approach | Focus | Pros | Cons |---|---|---|---| | Wellbeing-centered AI | Ground AI in wellbeing and institutions | Direct alignment with lived experience; actionable metrics | Requires consensus on proxies; measurement challenges |Economy-first AI | Prioritize GDP/efficiency and market impact | Clear metrics; scalable investments | Risk of misalignment with true wellbeing; may neglect non-economic values |Governance-first AI | Emphasize safety, policy, and regulation | Strong safety guarantees; structured deployment | Potentially slower innovation; dependence on policy cycles |