By Cristo Leon, Ph.D.
Last reviewed October 11, 2026
Executive Order 14434, Inaugurating the Era of Super Intelligence, places advanced artificial intelligence within a broader national culture of innovation. That framing is important. But high-impact AI should not be measured only by computational capability, speed, or productivity. Its deeper potential lies in enabling intercultural innovation: helping people from different disciplines, languages, institutions, and knowledge traditions understand difference, negotiate meaning, and co-create new knowledge.
A true culture of innovation is not simply technological. It is also social, interpretive, and collaborative.
From technological innovation to intercultural innovation
Innovation rarely emerges from a single vocabulary or worldview. It often appears at the boundaries between communities: engineers working with educators, clinicians collaborating with designers, researchers partnering with local communities, or interdisciplinary teams attempting to solve a shared problem while using different assumptions and professional languages.
These differences are not obstacles to innovation. They are often its raw material.
Intercultural innovation can be understood as the creation of new knowledge, practices, or solutions through sustained interaction among people who bring different cultural, linguistic, disciplinary, or epistemic perspectives.
The challenge is therefore not to make everyone think alike. It is to create conditions in which different perspectives can interact without one being prematurely translated, simplified, or erased.
AI as intercultural infrastructure
This is where I see one of the most important opportunities for high-impact AI.
AI can function not simply as an automation technology, but as intercultural infrastructure: a mediating layer that helps different knowledge communities recognize, preserve, and negotiate meaning across disciplinary, linguistic, and cultural boundaries.
In collaborative research, apparently simple terms can carry very different meanings across fields or communities. The same word may describe different concepts. Different terms may describe closely related ideas. A team may appear to agree while participants are actually working from different assumptions.
Used responsibly, AI can help make these differences visible.
It can support questions such as:
- Do we mean the same thing when we use the same term?
- Are two different terms describing related concepts?
- Which assumptions are disciplinary, institutional, linguistic, or cultural?
- Where is apparent agreement masking different interpretations?
- Which perspectives are missing from the conversation?
That is a different model from asking AI to generate the “best answer.”
The objective becomes helping humans understand the intellectual landscape in which multiple answers are being negotiated.
Innovation depends on preserving difference
There is a persistent temptation in both organizational innovation and AI-assisted collaboration to equate convergence with agreement.
I think that is too limited.
Innovation frequently requires preserving disagreement long enough for something new to emerge.
Intercultural collaboration makes this particularly visible. Two communities may use the same concept differently because each meaning carries its own history, values, practices, and relationships. An AI system that simply normalizes those differences may make communication more efficient while simultaneously making collaboration intellectually poorer.
The better role for AI is not always to resolve ambiguity.
Sometimes it should make ambiguity visible.
This requires systems and research practices capable of preserving multiple meanings, documenting their provenance, and allowing communities to negotiate their relationships rather than silently collapsing them into a single vocabulary.
From artificial intelligence to collaborative intelligence
The most important question about increasingly capable AI systems may therefore not be:
What can AI do for us?
A more consequential question is:
What can people become capable of doing together when AI helps them understand one another better?
High-impact AI should not only increase individual productivity. It should expand our capacity for collaborative intelligence.
That means helping interdisciplinary and intercultural teams:
- identify conceptual differences,
- surface hidden assumptions,
- preserve minority interpretations,
- translate without erasing,
- and build shared frameworks without requiring complete uniformity.
In this sense, AI can support convergence without demanding sameness.
A culture of innovation is a culture of co-creation
Executive Order 14434 (Inaugurating the Era of Super Intelligence, 2026) emphasizes the potential of advanced intelligence systems to amplify human ingenuity. I would extend that idea: one of the most important forms of amplification may be collective ingenuity (Fleming, 2016).
A culture of innovation should therefore be judged not only by how quickly it develops new technologies, but by how effectively it enables different communities to create together (León et al., 2025).
The future of high-impact AI should include systems that help researchers, professionals, and communities move across disciplinary, linguistic, and cultural boundaries while preserving the differences that make collaboration generative.
That is the kind of innovation culture I would like to see emerge:
not intelligence replacing difference, but intelligence helping difference become collaborative possibility.
Sources
Fleming, D. (2016). Tribal Alchemy: Mining Your Team’s Collective Ingenuity (1st ed.) [Paperback]. CreateSpace Independent Publishing Platform.
Inaugurating the Era of Super Intelligence, Pub. L. No. Executive Order 14434, 91 Federal Register 63129 (2026). https://www.govinfo.gov/content/pkg/FR-2026-10-02/pdf/FR-2026-10-02.pdf
León, C., Lipuma, J., & Oviedo-Torres, X. (2025). Artificial Intelligence in STEM Education: A Transdisciplinary Framework for Engagement and Innovation. Frontiers in Education, 10(0), 18. /Research/Education (CLDM_Ds_Peer-reviewed). https://doi.org/https://doi.org/10.3389/feduc.2025.1619888