AUC students visiting Microsoft for applied AI and infrastructure exposure
National infrastructure pipeline. Atlanta first.
The next civil rights gap is infrastructure access. We are closing it before it sets.
AARI trains HBCU students and underrepresented learners to operate the full AI infrastructure stack, from energy and chips to data centers, GPUs, edge robotics, quantum systems, and production AI. The people who run the next decade of AI are being trained right now. Most of the country just has not noticed yet.
Focus
Infrastructure-first AI, not app-first AI.
Cohort Base
HBCU students, AUC learners, and underrepresented technical talent.
Outcome
Operators who can run the systems underneath modern AI.
Summer 2026 program progress
Data Center Site #2 Is Under Construction
AARI scholars and technical mentors have begun racking and staging systems at our solar-powered Data Center Site #2 co-location facility in Atlanta. The new environment expands our ability to teach physical infrastructure, Linux, networking, virtualization, cloud architecture, cybersecurity, observability, and AI infrastructure through real equipment.
Scholars are handling servers, assembling racks, tracing cables, documenting systems, and learning how modern computing environments are built from the floor up.
2
Active training sites
Build in progress
Racking, staging, and documentation
The AARI Infrastructure Stack
AI is not one layer. It is a living stack.
AARI teaches the systems beneath AI, from power and compute to edge deployment and embodied robotics. As students move through the stack, they learn how each layer shapes what can be built.
Infrastructure Access Statement
Students cannot become operators in rooms they never enter.
AARI connects AUC and HBCU learners to labs, data centers, cloud systems, robotics work, partner workshops, and demo environments so the infrastructure behind AI becomes visible, teachable, and buildable.
AARI in Action
We are not building a brochure. We are building an ecosystem.
From student workshops and corporate site visits to robotics labs, edge AI development, and data-center buildouts, AARI puts students inside the infrastructure, the tools, and the rooms where the future is built. Training, employers, capital, and community do not sit in separate boxes here. They reinforce each other. That is what an operator ecosystem looks like.
AARI student cohort at Microsoft Atlanta
Students in technical lab sessions at Morehouse
Garage Data Center work session with students
Students reviewing live systems inside the Garage Data Center
AARI partner and student workshop
Student-led discussion during Microsoft session
AARI leadership presenting applied AI infrastructure work
From Exposure to Execution
AARI turns access into ownership.
The pipeline is sequential by design. Students see real environments, learn the stack, build systems, prove the work, enter the market, and then build companies of their own.
01
See the Room
Students visit labs, data centers, corporate campuses, and live technical environments.
Students learn what real AI infrastructure environments look like before they are asked to choose a pathway.
02
Learn the Stack
Students study cloud, robotics, edge AI, infrastructure, networking, and quantum foundations.
The curriculum connects each layer so learners understand dependencies, not isolated tools.
03
Build the System
Students work on applied labs, demos, and product-oriented projects.
Labs move from vocabulary to hands-on work: devices, networks, cloud environments, and technical documentation.
04
Prove the Work
Students present, demo, benchmark, and defend what they built.
Students explain tradeoffs, show working outputs, and practice the communication expected in technical roles.
05
Enter the Market
Students move into internships, jobs, research, and leadership roles.
AARI frames outcomes around readiness, placement, research, and leadership opportunities tied to real operator work.
06
Own the Company
The strongest operators get backed as founders. Training creates operators. Capital makes them owners.
The long-term model connects training, founder readiness, and capital so operators can become owners.
The build order
Atlanta is the prototype. The grid is national.
AARI was built in Atlanta on purpose, rooted in one of the most important Black academic ecosystems in the country. Atlanta proved the model travels. The next sites are Orlando, Brooklyn, and Houston, each chosen for the same reason: talent density, an employer base that needs operators, and infrastructure demand that is not slowing down.
Each city gets a full local engine, not a satellite classroom. Same standard, same doctrine, four cities.
Access and excellence are not regional. They scale together, or they do not scale at all.
Orlando
Simulation, robotics, and a defense and aerospace corridor that runs on infrastructure talent.
Brooklyn
Dense talent, a growing tech base, and the clearest case that the operator pipeline belongs in the Northeast.
Houston
Energy, compute, and the front line of where power and AI meet.
Catching them early
The engineers who will fix the robots are sixteen right now.
Most AI workforce programs aim at adults who already have degrees. AARI goes earlier, into the gap nobody trains for: the sixteen to twenty year olds who will become the technicians and engineers who repair the robotics on a factory floor and raise the data centers the entire AI economy runs on. These are not entry-level jobs. They are the backbone.
By the time most programs reach a young person, the system has already decided robotics and data centers are not for them. We reach them first, put their hands on real hardware, and show them the work is technical, durable, and theirs to own. That is where the gap is. That is where we close it.
The flywheel
From operators, to owners.
AARI's founding line has always been operators, not observers. The next evolution of that line is ownership. We are building the capital layer that backs the founders who come out of our pipeline, people who understand the infrastructure stack from the inside because they were trained to build it.
This is what closes the loop. The pipeline produces operators. The operators become founders. The capital backs the founders. The companies they build hire the next cohort coming up behind them. That is not a program. That is an ecosystem, and once it spins, it does not stop.
Building the Operator Pipeline
A bright, AUC-rooted model for infrastructure fluency.
AUC-centered
Built from Atlanta’s HBCU talent base outward.
Cloud-to-edge
Students connect cloud systems to devices, robotics, and live environments.
Lab-based
Robotics and AI infrastructure labs reinforce hands-on execution.
Partner-exposed
Students see corporate, data center, and ecosystem pathways early.
Demo pipeline
Students build toward visible demos, technical explanations, and market-ready proof.
Problem
AI equity cannot stop at prompting.
Most AI education teaches students to use apps, prompts, and demos. But production AI depends on infrastructure: compute, cloud, networking, data centers, security, edge devices, robotics, and energy. If students do not understand the stack underneath AI, they remain consumers instead of operators.
Consumer path
Prompting tools without control over systems, budgets, or deployment environments.
Operator path
Understanding compute, uptime, security, edge hardware, data flows, and system ownership.
Our Operating Model
Energy → Chips → Infrastructure → Models → Applications
This is the AARI learning chain. Students learn how AI systems are powered, built, deployed, secured, optimized, and operated.
01
Energy
Power systems, efficiency, resiliency, and the reality that compute starts with energy.
02
Chips
GPU and accelerator awareness, edge hardware, silicon constraints, and performance tradeoffs.
03
Infrastructure
Linux, networking, cloud, containers, security, observability, and the systems that keep AI alive.
04
Models
Inference, deployment, optimization, guardrails, and model operations in real environments.
05
Applications
Robotics, edge AI, automation, and production workflows where systems meet the real world.
Programs
Our core training lanes
Data Center Dojo
Server installation, rack layout, cabling, imaging, Linux, networking, storage, virtualization, Kubernetes, logging, and operating documentation.
Skills: rack-and-stack, VLANs, DHCP/DNS, virtualization, monitoring, and uptime.
Lab: stage a system and build its operations runbook.
Model: test, development, and production operating practices.
Robotics & Edge AI
ROS 2, autonomous navigation, Jetson edge computing, sensors, computer vision, and physical AI projects that move from simulation to hardware.
Skills: sensors, local inference, telemetry, and constrained compute.
Lab: deploy an edge inference demo on Jetson-class hardware.
Pathway: edge AI technician and field systems support.
Cybersecurity & Observability
Security fundamentals, SIEM, incident response, vulnerability assessment, Splunk dashboards, alerts, and infrastructure telemetry.
Skills: security monitoring, SIEM, incident response, vulnerability assessment, and telemetry.
Lab: build Splunk dashboards and investigate an infrastructure alert.
Pathway: security operations and observability roles.
Cloud Architecture & Certification
AWS architecture training toward a September 2026 Solutions Architect target, grounded in real hybrid infrastructure.
Skills: AWS architecture, identity, networking, storage, reliability, and cost-aware design.
Lab: map the physical Site #2 stack into a hybrid cloud architecture.
Target: AWS Solutions Architect in September 2026.
Quantum Computing
Qiskit, CUDA-Q, Q#, linear algebra, quantum circuits, VQE experiments, and the connection between classical infrastructure and future systems.
Skills: Qiskit, CUDA-Q, Q#, linear algebra, circuits, and hybrid workflows.
Lab: run and document a VQE experiment.
Pathway: quantum research support and emerging compute literacy.
Proof
From demos to operating systems
AARI is not built around speculative brand language. It is built around labs, workshops, systems exposure, and operator training.
Edge deployment
Jetson edge inference
Students gain exposure to local inference paths, edge constraints, and hardware-aware deployment decisions on NVIDIA Jetson-class systems.
Infrastructure exposure
OpenShift cluster access
Cluster exposure is used to teach containerized systems, orchestration vocabulary, and what operational compute looks like beyond classroom abstractions.
Quantum literacy
Azure Quantum Lockbox
AARI workshops include quantum-literacy exercises that connect hybrid systems thinking to security, cloud, and next-generation compute workflows.
Workshop model
Microsoft Garage workshop
Workshop delivery has included Microsoft Garage-style environments where students move from concept to working system with direct technical support.
Training pipeline
AUC student training
AARI’s model centers AUC and HBCU learners, with Morehouse and Atlanta-based workforce pathways treated as the launch point for operator development.
AWS learning pathway
Cloud learning becomes visible, social, and practical.
AARI students and mentors connect AWS architecture study with hands-on infrastructure work, system design practice, certification preparation, and a community of peers who can learn and build together.
Student spotlight
Student Spotlight: Leeland
Leeland shares his perspective on participating in AARI in this student testimonial.
Video summary: Leeland shares his experience as an AARI student.
Student spotlight
Student Spotlight: Rasheed Jeheeb
“Because of AARI, I can see myself becoming a successful and passionate expert in robotics and a practicing engineer.”
Rasheed Jeheeb
AARI Scholar
Rasheed describes how hands-on robotics, data-center infrastructure, mentorship, and certification pathways are helping him connect technical growth with a clearer engineering career direction.
Read Rasheed’s full reflectionVideo summary: Rasheed reflects on learning practical robotics through mentorship, gaining unexpected data-center and hardware experience, pursuing technical certifications, and developing confidence in his future as an engineer.
Ecosystem / Partner Network
Our Partners
AARI groups organizations by their role in the infrastructure pipeline, including confirmed collaborations, active conversations, curriculum alignment, prospective partner relationships, and giving platforms.
Proof, Not Theater
Real exposure, real tools, real technical development.
AARI is built around real exposure, real tools, and real technical development. Students do not just hear about AI, robotics, cloud, edge computing, and infrastructure. They see it, touch it, question it, and build with it.
Impact
Seven months in. This is the floor, not the ceiling.
AARI started in November 2025. Here is the traction since.
Raised to date
$100K+
Committed funding secured in our first seven months, including corporate, grant, and philanthropic support.
Students trained
40+
Distinct students reached through AARI workshops, labs, and cohort programming.
Workshops delivered
3+
Hands-on technical sessions delivered across physical AI, cloud, edge deployment, and quantum literacy.
Industry partners engaged
10+
Organizations engaged through workshops, technical conversations, workforce planning, or program development.
First student placement
$115K
First documented operator outcome from the early AARI model.
Active technical projects
5
Current workstreams across edge AI, robotics, cloud architecture, quantum lockbox, and AI infrastructure/data center curriculum.
Campus / lab footprint
100K+ sq ft
Applied robotics and AI workforce training campus, Summer 2026 activation.
Metrics reflect current internal tracking as of 2026. Formal annual reporting is in development. We report in cohorts, labs, placements, and operator outcomes, not slogans. How we track impact
Recognition & Support
Recognition that strengthens the operator pipeline.
Industry and ecosystem support helps AARI turn infrastructure access into hands-on training, student projects, and workforce pathways.
QTS
2026 Grant Partner $15,000QTS awarded AARI a $15,000 grant in 2026 to support general operations and strengthen AARI's work preparing students for data center and AI infrastructure roles.
General operating support for AARI's AI infrastructure and data center workforce pathway.
a16z Cultural Leadership Fund
Ecosystem Partner Program Renewable SupportAARI was selected for renewable support through the a16z Cultural Leadership Fund's Ecosystem Partner Program, recognizing AARI's work at the infrastructure layer of AI: systems, compute, networking, cloud, data centers, robotics, and production environments.
Renewable support recognizing AARI's work expanding access to the infrastructure layer of AI for HBCU students and underrepresented technical talent.
Leadership
Leadership Team
Nolan S. Code
Founder & Executive Director
Morehouse College Alumnus. MBA. Systems infrastructure and platform strategy.
LinkedIn
Leslie Nicholson
VP, Partnerships
Enterprise partnerships. Institutional development. Strategic alliances.
LinkedInAdvisory Board
Dwayne Joseph, PhD
Strategic Advisor
Dr. Joseph brings deep expertise in STEM education infrastructure and institutional partnerships, providing strategic guidance on academic integration and platform scaling.
Carlotta A. Berry, PhD
Robotics & Systems Engineering Advisor
Dr. Berry brings decades of robotics research and engineering education experience, ensuring technical rigor and depth in the platform's applied robotics and systems curriculum.
Board of Directors
We're raising $10M to scale the infrastructure layer that the AI talent market requires.
Nolan S. Code
Founder & Executive Director
Dwayne Joseph, PhD
Board Advisor
Carlotta A. Berry, PhD
Board Advisor
Governance
Built for trust.
AARI is a nonprofit organization building transparent governance, responsible fiscal operations, student-centered programming, and measurable workforce outcomes.
501(c)(3) status
Registered nonprofit standing with charitable-purpose programming and donor accountability.
Board oversight
Governance structure designed for fiduciary review, executive accountability, and policy direction.
Fiscal transparency
Program growth is tied to documented budgets, scoped initiatives, and partner reporting expectations.
Student safety
Hands-on training environments require clear conduct standards, supervision, and duty-of-care practices.
Partner reporting
Partners should expect milestone updates, scope clarity, and outcome framing tied to actual program work.
Annual impact reporting
Impact should be reported in cohorts, labs, placements, projects, and operator outcomes, not slogans.
Funders
New AI wealth should build new AI access.
As AI creates historic market value, AARI is building the pipeline that gives underrepresented learners access to the infrastructure behind that value: data centers, GPUs, cloud, robotics, edge AI, networking, and quantum systems. AI created the wealth. Infrastructure access will create the opportunity. We raised our first $100K in seven months. The $10M build comes next, and it scales this across four cities.
Student operator fellowships
Direct support for cohort stipends, scholarships, and the time required for students to train as operators rather than casual participants.
Lab equipment and edge AI hardware
Jetson-class systems, robotics components, networking gear, test infrastructure, and the tools required for real deployment practice.
Curriculum, technical staff, and program operations
The people, documentation, and operating support required to convert a promising cohort into a repeatable workforce pipeline.
Field Notes
Latest work from the AARI ecosystem.
AARI’s story is told through workshops, lab work, student demos, and partner exposure, not static claims. These field notes show where the operator pipeline is moving next.
Workshop
Microsoft Garage student workshop
Students engaged the systems mindset behind AI, cloud, quantum literacy, and applied technical execution.
Infrastructure
Garage Data Center lab work
Students see how compute environments, operations, and data center realities shape production AI.
Quantum
Quantum study group
AARI’s quantum pathway introduces hybrid thinking, compute literacy, and future-ready technical vocabulary.
Learn moreCurriculum
Robotics and edge AI curriculum
Hands-on training connects robotics, inference, data, and deployment discipline into one learning model.
View programsDemo Pipeline
Upcoming demo day / pitch competition
The next milestone is giving students a visible room to demo, explain, and defend what they built.
Partner with AARICall to Action
The operators of the AI economy are being chosen right now. Help us choose more of them.
Contact
Contact AARI
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