Originally published at twarx.com - read the full interactive version there.
Last Updated: June 21, 2026
Most AI technology workflows are solving the wrong problem entirely. Everyone's fixated on which chip trains the next frontier model — when the trillion-dollar shift already underway in AI technology is that inference spending will soon dwarf training, and the companies positioned to capture it look nothing like the ones the headlines worship. This is the single most important reframing in AI technology right now, and almost nobody is budgeting for it.
A June 20, 2026 Motley Fool analysis published on Yahoo Finance argues that ON Semiconductor (NASDAQ: ON) — a power and sensing chipmaker, not a GPU vendor — could become 'the Nvidia of AI inference.' That claim matters right now because inference is an operating cost that scales forever, not a one-time capital build.
By the end of this piece you'll understand the financials, the systems-level reason this is happening, and a framework — the AI Coordination Gap — that explains why inference economics are about to reshape how every engineering team builds.
ON Semiconductor positions its power and sensing portfolio as critical infrastructure for AI inference workloads across hyperscaler data centers and the edge. Source: The Motley Fool / Yahoo Finance
Overview: What Was Announced and Why It Matters
On Saturday, June 20, 2026 at 11:20 PM GMT+2, Motley Fool contributor Lee Samaha published an analysis on Yahoo Finance making a deceptively simple argument: as inference spending surpasses data center infrastructure spending over the next few years, the biggest beneficiaries may not be the obvious GPU makers but the power semiconductor companies that keep inference running.
The thesis centers on ON Semiconductor (NASDAQ: ON), an Nvidia partner best known for power and sensing chips serving the electric vehicle (EV) and industrial markets. Per the report, the company's data center revenue grew 30% in the first quarter and was equivalent to $250 million on $6 billion in total sales in 2025. Samaha had selected ON as his 'top stock to buy for 2026,' citing an inflection point in its EV market and improving industrial end markets — both of which, he writes, materialized.
Here's the distinction that makes this newsworthy for engineers, not just investors. The article frames infrastructure spending as AI's capital budget — the one-time rush by hyperscalers to build data centers and train models. Inference spending — running AI models for real-world use — is the ongoing operating cost. As the article puts it, after infrastructure is initially built out, 'inference will likely account for the majority of spending.' And inference is 'power hungry, needs thermal management, and will inevitably scale up over time.'
30%
ON Semiconductor Q1 data center revenue growth
[Yahoo Finance / Motley Fool, 2026](https://finance.yahoo.com/technology/ai/articles/company-could-become-nvidia-ai-212000227.html)
$250M
ON data center revenue on $6B total 2025 sales
[Yahoo Finance / Motley Fool, 2026](https://finance.yahoo.com/technology/ai/articles/company-could-become-nvidia-ai-212000227.html)
$6B
ON Semiconductor total 2025 sales
[Yahoo Finance / Motley Fool, 2026](https://finance.yahoo.com/technology/ai/articles/company-could-become-nvidia-ai-212000227.html)
Why does a power-chip story belong in an AI technology engineering publication? Because the same economic shift that makes ON attractive to investors is the shift that breaks how most teams architect AI systems today. When inference becomes the dominant, permanent cost, the bottleneck stops being 'can we train it?' and becomes 'can we run thousands of model calls, reliably, cheaply, at the edge and in the cloud, coordinated together?' That second problem is the one almost nobody is structurally solving — and it has a name. For context on the underlying chip economics, see analyst coverage from Reuters Technology and market data on Nasdaq.
Training is a capital expense you pay once. Inference is an operating expense you pay forever. The entire AI industry is still budgeting like it's 2023.
What Is It: ON Semiconductor and the Inference Economy in Plain Language
Strip the jargon. When people say 'AI,' they usually picture two activities mashed together:
Training — teaching a model. Enormously compute-heavy, happens in big bursts, uses massive clusters of Nvidia GPUs. Think of it as building a factory.
Inference — using the trained model to answer questions, generate text, classify images, or power an agent. This happens every single time a user interacts with an AI feature. The factory running 24/7 on the electricity bill.
The Yahoo Finance article argues the industry is in the 'building the factory' phase right now — hyperscalers are pouring capital into data centers. But once those are built, the recurring cost of running them takes over and never stops growing. I've watched this play out firsthand on the software side: teams agonize over training costs, then get blindsided when their inference bill doubles every quarter after launch.
ON Semiconductor doesn't make the brains — the GPUs. It makes the power delivery and thermal-related semiconductors: the components that convert, regulate, and manage the electricity feeding those GPUs and keeping them from melting. Every rack of inference hardware needs more of these. As inference scales, so does demand for power chips. That's the 'Nvidia of inference' pitch: not the compute, but the picks-and-shovels of running compute reliably. The broader energy challenge is well documented by the International Energy Agency, which projects data center electricity demand doubling this decade.
The company's reach is broader than the cloud. Per the article, its opportunity spans 'diverse environments, including hyperscaler data centers, businesses, and edge inference' — meaning AI running not just in giant data centers but on devices, factory floors, vehicles, and local servers. For more on this broader landscape, see our overview of AI infrastructure trends.
ON's data center revenue is still tiny — $250M of $6B, roughly 4% of sales. The bull case isn't the current number; it's the trajectory: 30% Q1 growth in a segment that compounds with every deployed inference workload.
Coined Framework
The AI Coordination Gap
The AI Coordination Gap is the widening distance between an organization's ability to build capable AI components and its ability to run them together, reliably, and affordably at inference time. It names why teams ship impressive demos that collapse under real-world inference load, cost, and orchestration complexity.
The economic crossover at the heart of the news: training is a capital spike, inference is a rising operating curve. The AI Coordination Gap lives entirely on the inference side. Source: Yahoo Finance / Motley Fool
How It Works: The Inference Stack and the Coordination Gap
To understand why power semiconductors and orchestration software are two sides of the same coin, you have to see the full inference stack — from the electron to the agent. Here's the flow.
The Full-Stack Inference Pipeline: From Power Chip to Coordinated Agent
1
**Power & Thermal Layer (ON Semiconductor)**
Power semiconductors regulate voltage and manage heat for GPU/accelerator racks. Inputs: grid power. Outputs: clean, dense, efficient power to compute. This is the physical floor of every inference call.
↓
2
**Compute Layer (Nvidia GPUs, custom accelerators)**
Runs the forward pass of the model. Latency-critical. The cost-per-token here is what makes inference an operating expense that scales with usage.
↓
3
**Model Serving Layer (vLLM, TensorRT-LLM, Triton)**
Batches requests, manages KV cache, routes to the cheapest viable model. Where most inference cost savings actually happen.
↓
4
**Retrieval & Context Layer (RAG, vector databases)**
Pinecone, pgvector, or similar fetch relevant context before each call. Reduces hallucination and cuts token cost by avoiding giant prompts.
↓
5
**Orchestration Layer (LangGraph, AutoGen, CrewAI, n8n)**
Coordinates multiple model calls into a workflow or multi-agent system. This is where the AI Coordination Gap bites: reliability compounds downward across steps.
↓
6
**Interop Layer (MCP — Model Context Protocol)**
Standardizes how agents access tools and data. Anthropic's MCP is becoming the connective tissue that lets coordinated systems scale across vendors.
The sequence matters because cost and reliability are determined bottom-up (power, compute) but failure is felt top-down (orchestration). ON Semiconductor plays at layer 1; engineers fight the Coordination Gap at layers 5–6.
Here's the counterintuitive truth most teams discover too late — usually after they've already shipped: a six-step pipeline where each step is 97% reliable is only about 83% reliable end-to-end (0.97⁶ ≈ 0.833). Add more agents, more tool calls, more model hops, and reliability craters. That is the AI Coordination Gap in one equation. Every one of those hops is an inference call, which means the Coordination Gap isn't just a reliability problem. It's a cost-amplifier. Coordination failures burn money on retries before anyone notices the system is broken. This compounding-error dynamic is increasingly documented in academic work on arXiv.
Coined Framework
The AI Coordination Gap
Reliability multiplies, it doesn't add. The gap between a 97%-reliable component and a 97%-reliable system is the single most expensive blind spot in production AI — and it scales with every inference call you add.
Complete Capability List: What ON's Inference Position Actually Delivers
Grounded strictly in the reporting and ON's public positioning, here's what the company brings to the inference economy:
Power technology for next-generation data centers built in partnership with Nvidia — explicitly named in the article as a relationship that positions ON for the new compute buildout.
Power and sensing chips originally built for EV and industrial markets, now redeployed for AI infrastructure — a portfolio with proven thermal and efficiency credentials.
Edge inference power delivery — capability for AI running outside the data center: businesses, factory floors, devices. This is underappreciated.
Hyperscaler data center coverage — power components for the large cloud providers building the bulk of global inference capacity.
Dual-market resilience — the company benefits from both data center infrastructure spending (the capital phase) and inference spending (the operating phase), with the latter as the long-term profit driver, per the article.
Demonstrated growth inflection — 30% Q1 data center revenue growth, plus the EV inflection and industrial recovery the author predicted for 2026.
The phrase the whole thesis hinges on: inference is 'power hungry, needs thermal management, and will inevitably scale up over time.' Every word of that sentence is a line item on ON's future income statement.
How to Access and Use It: For Investors and For Engineers
This story has two audiences, so here are two answers.
If you're evaluating the investment
Ticker: ON Semiconductor trades as NASDAQ: ON. Reference financials via the official ON Semiconductor site and SEC filings — not a single Motley Fool column.
Verify the thesis drivers: data center revenue trajectory (was $250M / ~4% of $6B 2025 sales), EV market inflection, and industrial recovery.
Watch the crossover: the central bet is inference spending surpassing infrastructure spending 'in a few years.' Track hyperscaler capex commentary from earnings calls — that's your leading indicator.
If you're an engineer building inference systems
You can't buy a power chip to fix your stack — but you can engineer for the inference economy that makes ON's thesis true. Here's the practical path to closing your Coordination Gap.
Closing the AI Coordination Gap in practice: instrument every inference hop, measure end-to-end reliability, and route to the cheapest viable model per step.
python — LangGraph: a coordinated, cost-aware inference node
Minimal LangGraph node that routes to the cheapest viable model
and tracks end-to-end reliability across an inference pipeline.
from langgraph.graph import StateGraph, END
def route_model(state):
# Cheap model for simple steps, frontier model only when needed.
# Inference = operating cost. Every token here is recurring spend.
complexity = state['task_complexity']
state['model'] = 'haiku' if complexity < 0.6 else 'sonnet'
return state
def run_inference(state):
# Wrap the call with retry budget — retries are pure cost leakage.
state['result'] = call_llm(state['model'], state['prompt'],
max_retries=2)
state['inference_calls'] += 1 # track operating expense
return state
graph = StateGraph(dict)
graph.add_node('route', route_model)
graph.add_node('infer', run_inference)
graph.set_entry_point('route')
graph.add_edge('route', 'infer')
graph.add_edge('infer', END)
app = graph.compile()
Reliability rule: every node you add multiplies failure probability.
Measure 0.97^n, not 0.97.
Want pre-built coordinated workflows to start from? You can explore our AI agent library for orchestration templates that already account for retry budgets and model routing. For the orchestration fundamentals, our guide to multi-agent systems walks through the reliability math step by step.
Authoritative docs to bookmark: LangChain / LangGraph docs, Anthropic docs (MCP), Pinecone docs, NVIDIA Triton docs, and n8n docs.
When to Use It (and When NOT To)
The inference-first mindset — and ON's thesis — isn't universally correct. Here's the honest map.
Lean into inference-cost engineering when: your AI feature has real, recurring usage; you're running multi-step agents; you're deploying at the edge; or your monthly model bill is climbing faster than revenue.
Don't over-engineer when: you're still validating product-market fit. Pre-PMF, a single frontier-model call beats a brittle six-agent pipeline every time. I would not ship a complex orchestration layer before you know users actually want the thing. The Coordination Gap only matters once you're at scale.
The investment caveat: ON's data center segment is ~4% of sales. If you need pure-play AI-inference exposure, this isn't it yet — it's a power/industrial/EV company with an emerging AI tailwind. Treat the 'Nvidia of inference' framing as a thesis, not a fact.
The companies winning with AI agents aren't the ones with the most GPUs. They're the ones who solved coordination — and coordination is just inference reliability under a different name.
Head-to-Head Comparison: ON Semiconductor vs the Inference Field
CompanyRole in inferenceAI exposureKey data point
ON Semiconductor (ON)Power & thermal semiconductorsEmerging (~4% of sales)$250M data center rev, +30% Q1
Nvidia (NVDA)Compute / GPUsDominantReference point — '1/100th the size' vs ON, per article
Power-chip peers (broad)Power delivery / VRMsMixed industrial + AIBenefit from same inference-power demand
Inference software (vLLM, Triton)Model serving / batchingPure inferenceWhere per-token cost is reduced
Orchestration (LangGraph, AutoGen, CrewAI)Coordination layerPure inferenceWhere reliability compounds — the Coordination Gap
Note: only ON's figures are drawn from the source article. Other rows describe category roles, not cited financials — verify each independently.
What It Means for Small Businesses
You're not buying GPU racks. But the inference economy still hits your P&L directly.
Opportunity: inference is getting commoditized and cheaper per token, which means a small business can deploy a customer-support agent or a document-processing pipeline for a few hundred dollars a month — work that cost a salaried hire before.
Risk: the AI Coordination Gap is brutal at small scale because you don't have the engineering team to debug it. A 'simple' five-step agent that's 95% reliable per step is only ~77% reliable end-to-end — meaning roughly 1 in 4 customer interactions fails silently. That's not a model problem. That's an architecture problem.
Concrete example: a 3-person e-commerce shop builds a returns-handling agent. Each LLM call costs fractions of a cent, but retries from coordination failures triple the bill and frustrate customers. Fix: fewer, cheaper, well-monitored steps beat many clever ones every time.
For the playbook, see our guide to workflow automation and enterprise AI patterns that scale down to small teams.
Who Are Its Prime Users
Hyperscalers — the primary buyers of ON's data center power tech, building the inference backbone.
Senior AI/platform engineers — those owning inference cost and reliability, who feel the Coordination Gap daily in their on-call rotations.
EV and industrial manufacturers — ON's core markets, now overlapping with edge inference in ways nobody fully anticipated two years ago.
Mid-to-large enterprises deploying agents — where multi-step orchestration determines unit economics.
Edge AI builders — robotics, retail, and automotive teams running models on-device, where power efficiency isn't optional.
How to Use It: A Worked Demonstration
Let's make the Coordination Gap concrete with real numbers. Suppose you're building a customer-onboarding agent with five inference steps.
Before vs After: Closing the Coordination Gap on a 5-Step Agent
1
**BEFORE: 5 frontier-model calls @ 95% each**
End-to-end reliability: 0.95⁵ = 77.4%. Cost: 5 expensive calls + retries. Result: 23% of users hit a failure.
↓
2
**FIX 1: Route simple steps to a cheap model**
3 of 5 steps move to a small model. Cost per run drops ~60%. Reliability unchanged if validated.
↓
3
**FIX 2: Collapse 5 steps into 3 via better prompting + RAG**
Fewer hops = fewer multiplications. 0.97³ = 91.3% reliability — a 14-point jump.
↓
4
**AFTER: instrument every hop, bounded retries**
Add MCP tool standardization + observability. End-to-end ~91% at ~40% of original cost.
The biggest reliability win isn't a smarter model — it's fewer coordinated steps. Every hop you remove improves both reliability and inference cost.
Sample input: 'New user signs up; verify email, enrich profile, classify intent, route to team, send welcome.'
Naive output: 5 frontier calls, 77% reliability, ~$0.045/run.
Optimized output: 3 mixed-model calls + RAG context, 91% reliability, ~$0.018/run. That's a 60% cost cut and a 14-point reliability gain on the same task.
Run that across 100,000 onboardings/month and you've saved roughly $2,700/month — about $32K annually — while cutting failures by more than half. That is the dollar value of closing the Coordination Gap. I've seen teams stumble onto this math by accident after six months of wondering why their agent felt flaky. Don't wait six months.
Coined Framework
The AI Coordination Gap
It's the difference between 'our model is 95% accurate' and 'our system works for users.' The first is a benchmark; the second is a multiplication problem you only see in production.
Good Practices and Common Pitfalls
❌
Mistake: Measuring per-step accuracy, not end-to-end
Teams celebrate 97% per-node accuracy in AutoGen or CrewAI, then ship a six-node system that's 83% reliable — and blame the model. I've watched this happen in code review. The model was fine. The architecture wasn't.
✅
Fix: Track end-to-end success in LangGraph with tracing. Measure 0.97ⁿ, not 0.97.
❌
Mistake: Treating inference as a fixed cost
Budgeting AI like a one-time training spend ignores that inference is a recurring operating cost that grows with every user — exactly the shift the ON thesis is built on.
✅
Fix: Forecast inference as opex. Route to the cheapest viable model per step; reserve frontier models for hard tasks.
❌
Mistake: Unbounded retries
Coordination failures trigger silent retries that multiply token spend and latency. This is the hidden tax of the Coordination Gap — and it doesn't show up in your benchmark numbers, only in your invoice.
✅
Fix: Set explicit retry budgets and circuit breakers. Log every retry as a cost line.
❌
Mistake: Custom glue instead of standards
Hand-rolled tool integrations break with every vendor change, widening the gap between teams. We burned two weeks on this exact problem before standardizing.
✅
Fix: Adopt MCP (Model Context Protocol) for standardized tool and data access across agents.
Average Expense to Use It
For investors: exposure costs the price of one ON share (NASDAQ: ON) plus brokerage fees — verify current pricing via the official site and your broker; the article cites $6B in 2025 sales as the company's scale.
For engineers building inference systems (realistic monthly ranges, not from the article — industry-typical):
Free / open-source orchestration: LangGraph, AutoGen, CrewAI, and n8n's core are free to self-host. Cost = your compute.
Model inference: small models run at fractions of a cent per 1K tokens; frontier models cost meaningfully more — route accordingly and that delta compounds fast at scale. Compare current pricing on the OpenAI pricing page and Anthropic pricing.
Vector DB / RAG: Pinecone has a free starter tier; production typically runs tens to a few hundred dollars/month depending on index size.
Total cost of ownership: a small production agent commonly lands in the low hundreds/month; the real TCO lever is coordination efficiency — our worked example showed a 60% cut just from routing and step reduction.
The cheapest inference optimization isn't a discount — it's deleting a step. Every coordinated hop you remove cuts cost and compounds reliability upward.
Industry Impact: Who Wins, Who Loses
Winners: power semiconductor makers like ON as inference scales; orchestration platforms (LangGraph, AutoGen, CrewAI, n8n) that close the Coordination Gap; model-serving layers that cut per-token cost; and Anthropic's MCP as the interop standard that's quietly becoming load-bearing infrastructure.
Pressured: teams that bet everything on training and ignored inference economics. Vendors selling brittle multi-agent demos that fall apart at scale. Anyone whose unit economics assume token prices stay flat — they won't, but your usage will grow faster than prices fall.
The defensible dollar logic: if inference becomes 'the majority of spending' as the article claims, then a 30%-growing data center segment at ON compounds fast off a $250M base — and on the software side, every enterprise running agents at scale has a six-figure-plus annual coordination-cost problem waiting to be optimized. That's not speculation. That's arithmetic. Our deep dive on AI cost optimization breaks down where the savings actually live.
The inference shift redraws the AI value chain: power and orchestration layers gain, training-only bets lose leverage.
Reactions
The thesis comes from Lee Samaha, contributor at The Motley Fool, who named ON his top stock to buy for 2026. The broader systems argument echoes the research community: papers on arXiv increasingly study multi-agent reliability and the compounding-error problem — it's not a fringe concern anymore, it's showing up in peer-reviewed work. Anthropic has positioned MCP as the standard for agent-tool interop, and the LangChain team continues to ship LangGraph as a production-grade coordination layer. For the macro inference trend, watch hyperscaler capex commentary covered by outlets like Yahoo Finance, Reuters Technology, and Bloomberg Technology.
Nobody got rich selling shovels by digging for gold. The next AI fortunes won't be made in training runs — they'll be made in power delivery and inference coordination.
What Happens Next: Predictions
2026 H2
**Inference cost becomes a board-level metric**
As the article frames inference as the dominant operating expense, expect CFOs to demand per-inference cost reporting — driving adoption of model-routing in LangGraph and AutoGen. This is already starting at companies I talk to. It's not a 2027 problem.
2027
**Power-chip AI revenue accelerates**
Off a $250M base growing 30% in Q1, ON's data center segment could become a material growth driver if the infrastructure-to-inference crossover the article predicts materializes.
2027–2028
**MCP becomes the default coordination standard**
Anthropic's MCP adoption reduces custom-glue failures, directly narrowing the AI Coordination Gap for enterprises running agents at scale.
2028+
**Edge inference scales the power-chip thesis**
The article's emphasis on 'edge inference' suggests demand for efficient power delivery outside data centers — robotics, retail, and automotive AI — extends ON's runway well past the initial hyperscaler wave.
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▶
Watch on YouTube
AI Inference vs Training Cost — Why Inference Wins Long-Term
AI infrastructure economics explained
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Frequently Asked Questions
What is agentic AI?
Agentic AI describes systems where one or more models autonomously plan, call tools, and take multi-step actions toward a goal — rather than answering a single prompt. It's one of the fastest-moving corners of AI technology. Frameworks like LangGraph, AutoGen, and CrewAI coordinate these steps. The catch is the AI Coordination Gap: each step is an inference call, and reliability multiplies downward — a five-step agent at 95% per step is only ~77% reliable end-to-end. Production agentic AI succeeds by minimizing steps, routing simple tasks to cheap models, bounding retries, and instrumenting every hop. It's also why inference cost matters so much: agents make many model calls per task, turning inference into a recurring operating expense rather than a one-time spend.
How does multi-agent orchestration work?
Multi-agent orchestration coordinates several specialized agents — a planner, a researcher, a writer, a checker — into one workflow. An orchestration layer like LangGraph, AutoGen, or CrewAI manages state, passes context between agents, and decides routing. Each handoff is an inference call, which is where the AI Coordination Gap bites: reliability compounds, so more agents means more failure surface. Best practice is to keep the graph shallow, validate outputs between nodes, standardize tool access with MCP, and trace end-to-end success rather than per-agent accuracy. See our multi-agent systems guide for patterns that hold up at scale.
What companies are using AI agents?
Hyperscalers and enterprises across finance, e-commerce, software, and customer support are deploying AI agents for tasks like onboarding, document processing, and triage. The infrastructure beneath them ties back to companies like ON Semiconductor, whose power chips run the inference these agents depend on, and Nvidia, whose GPUs do the compute. On the software side, teams adopt LangChain/LangGraph, AutoGen, CrewAI, and n8n. The common thread among successful adopters: they treat inference as a recurring cost and engineer aggressively against the AI Coordination Gap. Explore our AI agents resources and our agent library for real deployment patterns.
What is the difference between RAG and fine-tuning?
RAG (Retrieval-Augmented Generation) fetches relevant context from a vector database like Pinecone at inference time and injects it into the prompt — keeping knowledge current without retraining. Fine-tuning bakes new behavior into the model weights through additional training. RAG is cheaper to update, ideal for changing facts and proprietary documents, and lowers inference cost by avoiding huge prompts. Fine-tuning is better for fixed style, format, or domain reasoning. Most production systems use both: fine-tune for behavior, RAG for knowledge. In the inference economy, RAG is especially valuable because it reduces tokens per call — directly cutting the recurring operating cost the ON thesis is built around.
How do I get started with LangGraph?
Install it with pip install langgraph, then read the official LangChain/LangGraph docs. Start with a single-node graph that calls one model, confirm it runs, then add nodes one at a time — measuring end-to-end reliability after each (remember 0.97ⁿ). Add model routing so simple steps hit a cheap model and only hard steps hit a frontier model. Wire in tracing from day one so you can see where the AI Coordination Gap appears. For tool access, adopt MCP early. You can shortcut the boilerplate by starting from templates in our AI agent library, which already include retry budgets and routing.
What are the biggest AI failures to learn from?
The most common production failure in AI technology isn't a bad model — it's the AI Coordination Gap: stacking many 'reliable' steps into a system that fails far more often than any single step. A six-node pipeline at 97% per node is only ~83% reliable end-to-end. Other recurring failures: treating inference as a fixed cost (it's recurring opex), unbounded retries that silently triple bills, hand-rolled tool glue that breaks with vendor changes, and shipping brittle multi-agent demos that collapse under real load. The fix is consistent: fewer steps, cheaper model routing, bounded retries, standardized interop via MCP, and end-to-end observability. Engineer for the system, not the benchmark. See our AI failures breakdown for more.
What is MCP in AI?
MCP — the Model Context Protocol, introduced by Anthropic — is an open standard for how AI models and agents connect to external tools, data sources, and services. Instead of writing custom integrations for every tool, you expose them through MCP, and any MCP-aware agent can use them consistently. This directly narrows the AI Coordination Gap by reducing the brittle, hand-rolled glue that causes failures when vendors or tools change. As multi-agent systems scale across LangGraph, AutoGen, and CrewAI, MCP is becoming the connective tissue that lets coordination remain reliable across heterogeneous stacks — which is why it's emerging as a default standard for production agentic AI through 2027 and beyond.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work focuses on making agentic AI practical for builders and businesses.
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