# Miguel Amigot

Chief Technology Officer, ibl.ai
Engineer · Gen AI + Speaker

- LinkedIn: https://www.linkedin.com/in/miguelamigot/
- GitHub: https://github.com/mamigot
- X: https://x.com/miguelamigot
- Email: miguel@ibl.ai

## About

Miguel Amigot is Chief Technology Officer of ibl.ai, where he leads engineering, architecture, and product. His background is in Computer Science and Electrical Engineering.

That foundation shapes how he thinks about AI infrastructure: systems that keep organizations LLM-agnostic — free to run any model and switch anytime — and in full ownership of their own code and data.

He works with universities, companies, and government agencies to deploy agentic AI inside their own infrastructure, so they can avoid the vendor lock-in and per-seat pricing that make AI prohibitively expensive at scale.

## Talks

### AI and Enrollment Management — Harvard Graduate School of Education · Leadership in Strategic Enrollment Management 2026

At Harvard Graduate School of Education's intensive on Leadership in Strategic Enrollment Management, Miguel joined a panel on AI and enrollment management — moderated by Paul LeBlanc — alongside the founders of EdSights and Kai.

His throughline: a university should control its own code and data. ibl.ai deploys the platform — the agents and the ontology and data layers — inside the institution's infrastructure, with no gotcha where using your own data means talking to a vendor's servers.

The technical case: campus data sits in silos — the SIS, CRM, and LMS — and MCP connectors (the standard Anthropic introduced, Google adopted, and the Linux Foundation now stewards) translate plain English into API calls. Agents ground themselves in live student data, and their skills are written in English — no code, no flowchart diagrams.

Deployments are progressively useful: Syracuse's Clementine navigator started with easily accessible materials and connected more systems phase by phase, useful from day zero. At West Coast University, a screen-aware agent guides nursing students through the LMS at night, when no employee is on shift — transparently disclosed as AI, with strong usage and satisfaction.

On economics, Miguel walked through the information asymmetry between $20-per-user-per-month list pricing and developer token pricing that converges on the cost of electricity. A SUNY IITG team used that knowledge to turn a six-figure multi-vendor quote into roughly $30K, and Syracuse deployed against APIs in its own infrastructure instead of per-seat plans — avoiding well into eight figures.

On workforce fears, his answer: AI's efficiency lets an institution do the same job with fewer people or do a drastically bigger job with the same people. Leadership's role is to make adoption evolutionary — the training, the professional cover, and the unified data layer that let staff spend their time transforming students instead of fishing data out of silos.

Other speakers:
- Paul LeBlanc — Moderator · former President, Southern New Hampshire University — https://www.linkedin.com/in/paul-j-leblanc-6a17749/
- Claudia Recchi — Co-Founder & Co-CEO, EdSights — https://www.linkedin.com/in/claudia-recchi-67343a106/
- Lungile Tshuma — Co-Founder & CEO, Kai — https://www.linkedin.com/in/lungile-tshuma/

Program site: https://www.gse.harvard.edu/professional-education/program/leadership-strategic-enrollment-management

Clips (see all in the playlist at https://www.youtube.com/playlist?list=PLSbLJL1mvcOY):
- Universities Should Own Their Code and Data — https://www.youtube.com/shorts/t1wJlQhaI6M
- What Is MCP — Anthropic's Model Context Protocol — https://www.youtube.com/shorts/6CjG9FoKTzw
- You Write AI Agent Skills in Plain English — https://www.youtube.com/shorts/CVnOLLzyfiA
- The $20-Per-Seat AI Con — How Schools Overpay — https://www.youtube.com/shorts/TTrunO4Cbxg
- The Night-Shift AI Agent for Nursing Students — https://www.youtube.com/shorts/gVakYGLMPXA
- AI Efficiency — Fewer People or a Bigger Job — https://www.youtube.com/shorts/gQwG_UEA5D4

### AI Sovereignty & Ownership — Banco Santander gathering with 100 university presidents · Mexico City

At a Banco Santander gathering of roughly 100 university presidents in Mexico City, Miguel joined a panel — moderated by Paul LeBlanc — on what agentic AI makes possible in higher education today, and what is still hard.

The throughline was AI sovereignty and ownership. Institutions don't have to lock themselves into a single vendor's models at $20–$30 per user per month. By staying LLM-agnostic and tapping developer-tier token pricing, a deployment that would cost millions a year can run for a fraction of that.

Miguel made the case for owning both the code and the data — running the entire platform inside the university's own infrastructure — so security, cost, and the roadmap stay under the institution's control rather than a SaaS vendor's.

Other speakers:
- Paul LeBlanc — Moderator · former President, Southern New Hampshire University — https://www.linkedin.com/in/paul-j-leblanc-6a17749/
- Ron Stalnaker — Vice President of Business and Finance, Georgia Southern University — https://www.linkedin.com/in/rwstal/
- Andrew Joncas — AVP Enterprise Data and AI, Syracuse University — https://www.linkedin.com/in/andrew-joncas-08717210/

Watch on YouTube: https://www.youtube.com/watch?v=L5aLsg6oWJg

Clips (representative — see all at https://www.youtube.com/@iblai/shorts):
- Own Your Code, Own Your AI: The Case for Code Sovereignty — https://www.youtube.com/shorts/hHbj4hp56cQ
- $20 a User vs 20 Cents a Million Tokens: The LLM Pricing Gap — https://www.youtube.com/shorts/8sA4CyLhHxc
- Syracuse Built an AI Agent for All 22,000 Students — https://www.youtube.com/shorts/HLI4AmpH1YU
- How One University Cut Its Utility Bill by $2M With AI — https://www.youtube.com/shorts/6YtbwihFB98
- Every Student and Every Employee Will Have an AI Agent — https://www.youtube.com/shorts/E01pQPVVzJY
- Why Small Language Models Run on Just 14 Watts — https://www.youtube.com/shorts/gTAe0GZLzQc
- A Campus Portal Rebuilt in 48 Hours With AI — https://www.youtube.com/shorts/HU0lhKF8At4
- The Hardest Part of AI Adoption Isn't Technology — https://www.youtube.com/shorts/1PmZiy_GwhI

### Responsibly Driving Student Success with AI — Google Cloud

A Google Cloud session on responsibly driving student success with AI — how institutions deploy AI for measurable student outcomes while keeping their data governed and under their own control.

Other speakers:
- Jesus Gomez — Strategic Business Executive at Google Cloud — https://www.linkedin.com/in/jesustrujillogomez
- Lev Gonick — Chief Information Officer, Arizona State University — https://www.linkedin.com/in/levgonick/
- Anant Agarwal — Founder, edX — https://www.linkedin.com/in/agarwaledu/

Watch on YouTube: https://www.youtube.com/watch?v=4d1JMkZqWxg

### Challenges of AI in Higher Education — CUNY AI Roundtable Discussion Series

In CUNY's AI Roundtable Discussion Series — "Challenges of AI in Higher Education" — Miguel sat down with Jose Diaz of CUNY City Tech to push past the hype and into the strategic realities institutions can't ignore.

The core idea: universities don't have to lock themselves into expensive, closed AI ecosystems. They can build LLM-agnostic infrastructure that mixes and matches models — OpenAI, Anthropic, Google, open-source, or local — while keeping control of their data and costs.

The conversation covered real use cases: scaling advising, tutoring, admissions, and financial-aid support; reducing operational cost while expanding access; and improving equity through always-available, personalized student support — all with institutional data governed internally.

It also confronted the harder questions — what happens to teaching and learning under AI, how to handle academic integrity and cheating, the evolving role of faculty (especially in the humanities), and how to balance personalization with critical thinking.

The takeaway: AI adoption isn't optional, but how you adopt it is a strategic choice. Flexibility, cost-awareness, and data control separate the institutions that lead from those that get locked into short-term decisions.

Other speakers:
- Jose Diaz — Director of Academic Technologies and Online Learning, CUNY City Tech — https://www.linkedin.com/in/jose-w-diaz-m-ed-48203b13b/

Watch on YouTube: https://www.youtube.com/watch?v=S1lBZmyp-kg

Clips (representative — see all at https://www.youtube.com/@iblai/shorts):
- You Don't Need to Be Locked Into Any AI Vendor — https://www.youtube.com/shorts/A7e01-haV2U
- From $2.4 Million to $10,000 a Year: LLM Pricing Arbitrage — https://www.youtube.com/shorts/awMiurpPCk0
- The Two Prices of AI: Business vs Developer — https://www.youtube.com/shorts/XNfe9A_AvrU
- Never Put All Your AI Eggs in One Basket — https://www.youtube.com/shorts/nOg81vo0UwA
- Why the Humanities Matter More in the AI Era — https://www.youtube.com/shorts/P7TyOs5Px8s
- Worried About AI Cheating? First Ask Why Your Course Matters — https://www.youtube.com/shorts/qNQf8tF4HjU
- Adaptive Learning Needs Your Data, on Your Platform — https://www.youtube.com/shorts/Cv4-KrWo7L8
- AI and the Gap Between Rich and Poor Students — https://www.youtube.com/shorts/uulv2OGap-M

### Toward Institution-Owned AI at Universities — SUNY CIT 2026 · Stony Brook University

At SUNY's Conference on Instruction & Technology (CIT 2026) at Stony Brook University, Miguel walked through the journey universities take from free chatbots to full code and data sovereignty.

The talk traces seven stages — from free ChatGPT accounts with no governance, through enterprise agreements and the multi-vendor cost problem, to the developer-pricing revelation where the same models cost ~100× less via API.

From there it builds toward LLM agnosticism, an integration layer that plugs into the LMS and SIS, and finally code ownership — the institution running and owning the entire stack, with private models running on its own infrastructure.

The conclusion for SUNY: institution-owned AI isn't just cheaper, it's the only model that keeps data, governance, and the long-term roadmap fully in the institution's hands.

View the slides: https://ibl.ai/cit
Conference site: https://cit.suny.edu/cit-2026/

### Discussing AI in Education with IBL — Amazon Web Services

A conversation with Amazon Web Services on bringing AI to education — building agentic AI on cloud infrastructure that institutions can run, govern, and own end to end.

Other speakers:
- Art Tuazon — https://www.linkedin.com/in/arttuazon/

Watch on YouTube: https://www.youtube.com/watch?v=ijBGZ9V7DbQ

### Building AI Mentors with Custom Indexes, Prompts, Guardrails and APIs — IEEE · MIT Lincoln Laboratory (MIT SuperCloud)

An engineering talk for the IEEE high-performance-computing community, hosted at MIT Lincoln Laboratory: the architecture behind production AI agents — custom retrieval indexes, prompt design, guardrails, and the APIs that tie them together.

Other speakers:
- Dr. Julie Mullen — MIT Lincoln Laboratory Supercomputing Center — https://www.linkedin.com/in/juliamullen/

View session: https://supercloud.mit.edu/scaling-hpc-education