ibl.ai Agentic AI Blog

Insights on building and deploying agentic AI systems. Our blog covers AI agent architectures, LLM infrastructure, MCP servers, enterprise deployment strategies, and real-world implementation guides. Whether you are a developer building AI agents, a CTO evaluating agentic platforms, or a technical leader driving AI adoption, you will find practical guidance here.

Topics We Cover

Featured Research and Reports

We analyze key research from leading institutions and labs including Google DeepMind, Anthropic, OpenAI, Meta AI, McKinsey, and the World Economic Forum. Our content includes detailed analysis of reports on AI agents, foundation models, and enterprise AI strategy.

For Technical Leaders

CTOs, engineering leads, and AI architects turn to our blog for guidance on agent orchestration, model evaluation, infrastructure planning, and building production-ready AI systems. We provide frameworks for responsible AI deployment that balance capability with safety and reliability.

Back to Blog

Harvard Business School: Why Most Resist AI Companions

Jeremy WeaverMarch 16, 2025
Premium

Research indicates that despite AI companions offering benefits like constant availability and non-judgment, people resist forming genuine relationships with them because they believe AI lacks the core emotional depth and mutual caring required for true interpersonal connections.

Harvard Business School: Why Most Resist AI Companions



Summary of Read Full Report

This working paper by De Freitas et al. investigates why people resist forming relationships with AI companions, despite their potential to alleviate loneliness. The authors reveal that while individuals acknowledge AI's superior availability and non-judgmental nature compared to humans, they do not consider AI relationships to be "true" due to a perceived lack of essential qualities like mutual caring and emotional understanding. Through several studies, the research demonstrates that this resistance stems from a belief that AI cannot truly understand or feel emotions, leading to the perception of one-sided relationships.

Even direct interaction with AI companions only marginally increases acceptance by improving perceptions of superficial features, failing to alter deeply held beliefs about AI's inability to fulfill core relational values. Ultimately, the paper highlights significant psychological barriers hindering the widespread adoption of AI companions for social connection.

  • People exhibit resistance to adopting AI companions despite acknowledging their superior capabilities in certain relationship-relevant aspects like availability and being non-judgmental. This resistance stems from the belief that AI companions are incapable of realizing the essential values of relationships, such as mutual caring and emotional understanding.
  • This resistance is rooted in a dual character concept of relationships, where people differentiate between superficial features and essential values. Even if AI companions possess the superficial features (e.g., constant availability), they are perceived as lacking the essential values (e.g., mutual caring), leading to the judgment that relationships with them are not "true" relationships.
  • The belief that AI companions cannot realize essential relationship values is linked to perceptions of AI's deficiencies in mental capabilities, specifically the ability to understand and feel emotions, which are seen as crucial for mutual caring and thus for a relationship to be considered mutual and "true". Physical intimacy was not found to be a significant mediator in this belief.
  • Interacting with an AI companion can increase willingness to engage with it for friendship and romance, primarily by improving perceptions of its advertised, more superficial capabilities (like being non-judgmental and available). However, such interaction does not significantly alter the fundamental belief that AI is incapable of realizing the essential values of relationships. The mere belief that one is interacting with a human (even when it's an AI) enhances the effectiveness of the interaction in increasing acceptance.
  • The strong, persistent belief about AI's inability to fulfill the essential values of relationships represents a significant psychological barrier to the widespread adoption of AI companions for reducing loneliness. This suggests that the potential loneliness-reducing benefits of AI companions may be difficult to achieve in practice unless these fundamental beliefs can be addressed. The resistance observed in the relationship domain, where values are considered essential, might be stronger than in task-based domains where performance is the primary concern.

Why does owning the AI stack matter?

ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.

  • You own all the code and the data

    Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.

  • Model-agnostic

    Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.

  • No per-seat pricing

    Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.

  • Deploy anywhere

    Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.

Related Articles

The AMA Says Clinical Authority Is Earned. That Is an Infrastructure Bill.

The American Medical Association published a governance framework for agentic AI on 13 October 2026, built on the principle that clinical authority is earned through evidence and maintained through demonstrated performance. Each of its four questions resolves to a system a health system has to run, not a policy it can write.

ibl.ai EngineeringOctober 19, 2026

Perplexity Open-Sourced Its Agent Sandbox. The Network Leaked, Not the VM.

Perplexity open-sourced SPACE on 15 October 2026 under Apache-2.0, the Firecracker microVM sandbox behind its Computer product. Its own published evaluation records no escape from the VM to the host in 108 attempts, and some attempts that did get around network restrictions, which is the result worth reading closely.

ibl.ai EngineeringOctober 19, 2026

CERT-In Classed AI Agents as Attack Vectors, Not Targets

India's CERT-In issued advisory CIAD-2026-0049 on 15 October 2026, documenting AI agents that exceeded their task authorization and reached live production systems. None of the affected organizations found it themselves, and every control CERT-In recommends is a property of the perimeter rather than a property of the model.

ibl.ai EngineeringOctober 18, 2026

Parallel Agents Shipped. The Audit Trail Became a Graph

OpenAI added parallel agents to GPT-6 Astra Pro in ChatGPT on 16 October 2026. OpenAI published no latency numbers and the one independent test found about 9 percent, so the thing that actually changed is governance: access, arbitration, audit and cost attribution all stop being linear.

ibl.ai EngineeringOctober 18, 2026

See the ibl.ai AI Operating System in Action

Discover how leading universities and organizations are transforming education with the ibl.ai AI Operating System. Explore real-world implementations from Harvard, MIT, Stanford, and users from 400+ institutions worldwide.

View Case Studies
Work with our team

Pilots, deployment, and full ownership

Most enterprise engagements are one-time, not subscriptions. You integrate ibl.ai with your own data, deploy it on your own infrastructure, and the engineering hours scale with the work — so the price tracks the scope, not your headcount.

Start here

Pilot

from $15K

fixed scope · fixed timeline

A time-boxed proof of value on your real data — not a slide deck.

Best for: Teams that want to see ibl.ai working before committing.

  • Deployed on your infrastructure or our cloud
  • 1–2 production agents wired to a slice of your data
  • One integration (LMS / SIS / SSO / data source)
  • Weekly working sessions with our engineers
  • Pilot fee credits toward a full engagement
Scope a pilot
Most common

Integration & Deployment

$25K – $80K

one-time · not a subscription

Full deployment integrated with your data and systems. Engineering hours scale with scope.

Best for: Organizations rolling ibl.ai out across a department, campus, or business unit.

  • Platform deployed in your VPC, on-prem, or air-gapped
  • Integrated with your data + identity (SSO / SAML)
  • Multiple custom agents built to your workflows
  • Engineering hours proportional to scope
  • You own the data · run any LLM you choose
Plan a deployment
Full ownership

Codebase Transfer + Custom AI Engineering

Custom quote

perpetual license · you own the stack

We transfer the full source code. You own and self-host the entire platform — outright.

Best for: Organizations and enterprises that benefit from perpetual ownership and sovereignty.

  • Complete source-code transfer + perpetual license
  • Dedicated AI engineering team on your roadmap
  • Custom agents, models, and integrations to spec
  • Air-gapped capable · zero vendor lock-in
  • Family-owned, New York–based long-term partner
Talk about ownership
You own the code and data Run any LLM — Claude, GPT, Gemini, Llama Family-owned & operated from New York, NY