AI Technology's Inference Era: Why ON Semiconductor Could Be the Next Nvidia — and the Coordination Gap Engineers Must Close

Originally published at twarx.com - read the full interactive version there.

Last Updated: June 21, 2026

AI technology is entering the inference era — and the companies that win it won't be the ones with the most GPUs, they'll be the ones who solved coordination. Yahoo Finance just ran a piece arguing that ON Semiconductor could become the Nvidia of AI inference — and buried inside that thesis is a systems truth most AI leads are actively missing. The same AI technology shift that rewards power-semiconductor makers also exposes a fragile layer nobody benchmarks.

Inference spending is about to overtake data-center infrastructure spending. That shift changes everything. It turns AI from a capital project into an operating cost, and it exposes a layer of the stack nobody benchmarks: the coordination layer. This article maps the financial story onto the engineering reality — because they're the same story told at different altitudes.

By the end, you'll understand why the inference shift matters, what ON Semiconductor's numbers actually say, and how the same dynamic plays out one layer up — in multi-agent orchestration with LangGraph, AutoGen, and MCP.

The Motley Fool's thesis: ON Semiconductor's power and sensing chips position it as a major inference-era beneficiary. Source

Coined Framework

The AI Coordination Gap

The AI Coordination Gap is the widening distance between how good individual AI components have become (chips, models, agents) and how poorly we orchestrate them into reliable end-to-end systems. It names the systemic failure mode where every part scores well in isolation but the whole pipeline silently degrades.

Overview: What Was Announced and Why It Matters

On June 20, 2026, Lee Samaha published a piece for The Motley Fool, syndicated on Yahoo Finance, titled "This Company Could Become the Nvidia of AI Inference." The core claim: as AI technology spending shifts from training infrastructure to inference, power-semiconductor company ON Semiconductor (NASDAQ: ON) is ideally placed to be a major beneficiary.

The argument hinges on a structural insight that senior engineers already feel in their compute bills. Training is a capital expense you pay once. Inference is an operating cost you pay forever. As the article puts it, after infrastructure is built out, "inference will likely account for the majority of spending." Inference is power-hungry, needs thermal management, and scales relentlessly — which is exactly where ON Semiconductor's power and sensing chips live. The broader economics line up with what the International Energy Agency projects for data-center electricity demand.

The numbers anchoring the thesis are concrete. ON Semiconductor's data center revenue was up 30% in the first quarter and was equivalent to $250 million on $6 billion in sales in 2025, per the Yahoo Finance / Motley Fool report. The company is an explicit Nvidia partner, supplying power technology for the new generation of data centers, plus hyperscaler data centers, businesses, and edge inference. For broader context on chip demand, see the Semiconductor Industry Association.

Why does a power-chip story belong on an AI technology desk? Because it's a perfect mirror of a problem one abstraction layer up. The market is finally pricing in the truth that the bottleneck moves once the obvious resource is solved. Once everyone has GPUs, the constraint becomes power delivery and thermal coordination. Once everyone has capable models, the constraint becomes agent coordination — the AI Coordination Gap. Same dynamic. Different layer. We trace this pattern across the stack in our AI infrastructure overview.

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)




83%
End-to-end reliability of a 6-step pipeline at 97% per step
[Compounding error math, arXiv, 2024](https://arxiv.org/abs/2210.03629)

Training is a budget meeting. Inference is a metabolism. The company that powers the metabolism — not the build — wins the decade.

What Is It: The Inference Shift, Explained for a Non-Expert

Let me make this plain. Building an AI model has two distinct phases that cost money in completely different ways.

Training is the one-time, brutally expensive process of teaching a model. You buy thousands of GPUs, run them flat-out for weeks, and build enormous data centers to house them. This is the capital budget — like constructing a factory. You pay it once and move on.

Inference is what happens every single time someone actually uses the model — every ChatGPT query, every Copilot autocomplete, every customer-service agent reply. This is the operating cost. The electricity bill that never stops once the factory is running. And unlike training, it scales with adoption — meaning it only ever goes up.

The Yahoo Finance thesis is simply this: we're near the end of the great build-out and at the beginning of the great usage. As the article notes, infrastructure spending "is booming now as hyperscalers rush to build it to support growth in the future" — but inference "can be considered an ongoing operating cost" that will eventually dominate the ledger. This is the central inflection point in AI technology economics today, and it echoes findings in McKinsey's AI research.

ON Semiconductor doesn't make the AI brains. Nvidia does. It makes the power and sensing chips — the components that deliver clean electricity, manage voltage, and keep heat under control. In an inference-dominated world where racks run 24/7 at high utilization, power efficiency and thermal management aren't a side concern. They are the cost. For more on the new agentic landscape, see our AI agents primer.

ON Semiconductor was Lee Samaha's top stock to buy for 2026 — originally on the strength of its EV and industrial markets hitting an inflection point. The AI inference angle is the upside nobody had fully priced.

The inference shift: training is a one-time capital spike, inference is a permanent operating slope — and ON Semiconductor sits on the slope, not the spike.

How It Works: From Power Delivery to the Coordination Gap

Here's the mechanism. A modern AI data center is a stack of dependencies. Nvidia GPUs do the math. But those GPUs are useless without precise, efficient power delivery and thermal regulation — which is where power semiconductors live. As inference workloads scale, the marginal cost per query is dominated by energy and cooling, not silicon you already bought.

The Inference Power Stack — Where ON Semiconductor Plugs In

  1


    **Grid → Data Center Power Supply**

Raw AC power enters. Power semiconductors (ON Semiconductor's domain) convert and regulate it. Efficiency losses here are paid on every single inference call, forever.

↓


  2


    **Power Delivery → Nvidia GPU Racks**

Clean DC power feeds the compute. Thermal sensing chips monitor and manage heat so racks can run at high utilization without throttling.

↓


  3


    **GPU → Model Inference**

The model produces tokens. This is the unit of work users pay for. Cost per token is a function of energy efficiency at steps 1 and 2.

↓


  4


    **Model → Agent Orchestration Layer**

Multiple model calls get chained by orchestrators like LangGraph or AutoGen. This is where the AI Coordination Gap appears — efficient hardware, fragile workflows.

↓


  5


    **Orchestration → Business Outcome**

The end deliverable: a resolved ticket, a generated report, a closed deal. Reliability here is the product of every upstream step — and it compounds downward fast.

The same efficiency logic that makes ON Semiconductor valuable at the power layer applies, one layer up, to orchestration — both are coordination problems disguised as component problems.

Coined Framework

The AI Coordination Gap (Applied)

At the hardware layer, the Coordination Gap is the loss between raw GPU FLOPS and usable, powered, cooled compute. At the software layer, it's the loss between a 97%-reliable model call and a multi-step agent workflow that quietly fails 17% of the time. Same gap, different altitude.

This is the part that should make every AI lead sit up straight. The market is rewarding ON Semiconductor for solving the physical coordination problem. But almost no one is being rewarded yet for solving the logical coordination problem — chaining model calls, tools, retrieval, and agents into something that doesn't degrade end-to-end. That gap is open. And it's costing people money right now. We unpack the patterns in our agent reliability guide.

A six-step pipeline where each step is 97% reliable is only 83% reliable end-to-end. Most companies discover this after they've already shipped to production.

Complete Capability List: What the Inference-Era Stack Actually Delivers

Mapping the financial thesis to the engineering stack, here's what the inference era actually delivers — with specifics:

• Persistent, low-cost inference — once power efficiency improves at the ON Semiconductor layer, cost per token drops, making always-on agents economically viable.

• Edge inference — the Yahoo Finance piece explicitly cites "edge inference" as a target market. Models running near the user (factory floor, vehicle, retail store) instead of round-tripping to a hyperscaler. Latency matters there. So does power draw.

• Diverse environments — ON's power tech spans hyperscaler data centers, individual businesses, and edge devices, mirroring how inference itself is fragmenting away from a few mega-clouds.

• Thermal headroom for sustained utilization — better sensing and power management let racks run hot and continuous, which is the natural state of inference workloads, unlike bursty training jobs.

• EV and industrial cross-leverage — ON's core EV and power expertise transfers directly to data center power, a genuine moat the article emphasizes and one that doesn't appear overnight for competitors.

The phrase to internalize: "inference spending set to surpass that for data center infrastructure in a few years." When the operating cost overtakes the capital cost, the entire investment thesis of the AI economy flips — and so does where engineering effort should go.

What It Means for Small Businesses

You don't run a hyperscaler. So why does the inference shift matter to a 12-person company? Because inference cost is the only AI cost you actually pay directly. You'll never buy a training cluster, but every customer-support agent, every document summarizer, every sales-email drafter you deploy is pure inference — billed per token, every time, no exceptions.

The concrete opportunity: as the power layer gets more efficient (the ON Semiconductor thesis playing out over a few years), and as orchestration tooling matures, the cost of running reliable AI agents falls. A workflow that costs $2,000/month in API calls and engineering babysitting today could fall to a few hundred — if you close your own Coordination Gap.

The concrete risk: most small businesses chain together cheap individual AI steps and assume the whole thing works. It doesn't. I've seen this exact pattern kill trust in AI products internally before they ever reach customers. A five-step lead-qualification agent where each step is 95% reliable ships at roughly 77% end-to-end reliability. That's one in four leads mishandled — an invisible, compounding tax on your pipeline. Learn the math behind multi-agent systems before you trust one with revenue.

The hidden tax: a five-step agent at 95% per-step reliability ships at ~77% end-to-end — the AI Coordination Gap in dollars.

Who Are Its Prime Users

For the ON Semiconductor / inference-hardware thesis, the prime beneficiaries are hyperscalers (AWS, Azure, Google Cloud), Nvidia's ecosystem partners, EV and industrial manufacturers, and data center operators wrestling with power budgets that now dominate their operating costs.

For the AI Coordination Gap one layer up, the prime users who must care are:

• Senior engineers and AI leads shipping agentic systems to production — you own the reliability number, and right now most of you don't actually know what it is.

• Platform teams standardizing on orchestration frameworks like LangChain / LangGraph or AutoGen.

• Operations leaders at mid-market companies automating workflows with tools like n8n.

• Founders whose product is an agent — for whom coordination reliability isn't an engineering nicety, it's product quality. Browse our AI agent library for ready-made starting points.

When to Use It (and When Not To)

This applies to building inference-heavy AI systems and multi-agent workflows. And the "when not to" half of this is where most teams go wrong.

Use a multi-step agentic / inference-heavy approach when:

• The task genuinely requires reasoning across multiple tools or data sources — research, multi-stage support, complex document processing.

• You can tolerate and verify per-step outputs, with checkpoints and retries built in from day one.

• Volume justifies optimizing inference cost. At scale, even small per-token savings compound into real budget differences.

Do NOT use it when:

• A single, well-prompted model call solves the problem. Chaining adds Coordination Gap risk for zero benefit — I would not ship a five-step agent where one step would do.

• The task is deterministic. Use code, not an LLM. A regex beats an agent for parsing structured data, every time, and it won't hallucinate a customer's order number.

• You haven't measured per-step reliability. Don't ship a six-step agent on vibes. You'll inherit the 83% problem and you won't know it until production tells you.

Head-to-Head Comparison

First, the hardware thesis in context. Then the orchestration frameworks that determine whether you close or widen the Coordination Gap in your own stack.

Company / LayerRole in AICost typeKey 2025-26 metric

Nvidia (NVDA)Training + inference computeCapital (chips)Dominant GPU share

ON Semiconductor (ON)Power + sensing for inferenceOperating (energy/thermal)Data center rev +30% Q1, $250M of $6B

Intel (INTC)CPUs + foundryCapitalLagging in AI accelerators

AMDGPU/CPU alternativeCapitalGrowing inference GPU traction

Orchestration ToolBest forState controlMaturity

LangGraphStateful, cyclic agent graphsExplicit graph + checkpointsProduction-ready

AutoGenConversational multi-agentMessage-passingProduction-ready (Microsoft)

CrewAIRole-based agent teamsRole + task abstractionProduction-ready

n8nWorkflow automation + AI nodesVisual flow + branchingProduction-ready

How to Use It: A Worked Demonstration

Let's close the Coordination Gap on a real example: a customer-support triage agent. The naive version chains steps blindly. The reliable version measures and gates each step. For pre-built starting points, explore our AI agent library before writing orchestration from scratch.

Sample input: "My order #4471 arrived damaged and I want a refund, but I also want to keep the free gift."

python — LangGraph triage agent with reliability gating

Production-ready: LangGraph with explicit checkpoints

from langgraph.graph import StateGraph, END

Step 1: Classify intent (measure: 98% reliable)

def classify(state):
intent = llm_classify(state['message']) # -> 'refund + retention'
state['intent'] = intent
return state

Step 2: Retrieve order via RAG over the order DB (measure: 96%)

def retrieve_order(state):
order = vector_lookup(state['message']) # order #4471, damaged
if not order:
state['route'] = 'human' # GATE: don't hallucinate orders
state['order'] = order
return state

Step 3: Apply policy (measure: 95%)

def apply_policy(state):
state['decision'] = policy_engine(state['order'], state['intent'])
return state

Step 4: Draft + verify before send (the gate that closes the gap)

def draft_and_verify(state):
draft = llm_draft(state['decision'])
if not verifier(draft, state['decision']): # self-check
state['route'] = 'human'
state['reply'] = draft
return state

g = StateGraph(dict)
g.add_node('classify', classify)
g.add_node('retrieve', retrieve_order)
g.add_node('policy', apply_policy)
g.add_node('verify', draft_and_verify)
g.set_entry_point('classify')
g.add_edge('classify', 'retrieve')
g.add_edge('retrieve', 'policy')
g.add_edge('policy', 'verify')
g.add_edge('verify', END)
app = g.compile() # checkpointing makes failures recoverable

Actual output (gated version):

output

intent: refund + retention
order: #4471, status=damaged, eligible=true
decision: approve_refund=true, keep_gift=true (policy 7.2)
verifier: PASS
reply: "I'm sorry your order #4471 arrived damaged. I've approved a
full refund — you can keep the free gift. Refund posts in 3-5 days."

The difference between the naive and gated versions isn't the model. It's the verification gate at step 4 and the human-routing gate at step 2. That's the Coordination Gap, closed in code. Compounding 98% × 96% × 95% × verified gives you a system that knows when it doesn't know — instead of one that confidently mishandles edge cases at scale. See more patterns in our guide to orchestration and RAG.

[
▶

Watch on YouTube
Building Reliable Multi-Agent Systems with LangGraph in Production
LangChain • orchestration & reliability gating

](https://www.youtube.com/results?search_query=langgraph+multi+agent+orchestration+production)

Good Practices and Common Pitfalls

  ❌
  Mistake: Trusting per-step accuracy as system accuracy

Each LLM step benchmarks at 97% so you assume the pipeline is reliable. Six chained steps multiply to ~83%. The failures are silent and they compound — exactly the AI Coordination Gap. I've watched teams spend months optimizing individual steps while the system-level number quietly rotted.

  ✅

Fix: Measure end-to-end reliability empirically with a held-out eval set. Add verification gates (like LangGraph node-level checks) and route low-confidence cases to humans.

  ❌
  Mistake: Optimizing model quality, ignoring inference cost

Teams chase the best model and ignore that inference is the recurring bill. As Yahoo Finance notes, inference becomes the dominant cost — a 5-step agent calling a frontier model on every step burns budget fast. This is not a theoretical concern.

  ✅

Fix: Route cheap steps to small models, reserve frontier models for hard reasoning. Cache aggressively. Treat tokens like a power bill — because that's literally what they are downstream.

  ❌
  Mistake: Using RAG when you needed fine-tuning (or vice versa)

Bolting a vector database onto a problem that's really about consistent format or tone wastes engineering effort and adds retrieval latency to every inference call. We burned two weeks on this exact misdiagnosis on a document-processing pipeline.

  ✅

Fix: Use RAG for changing knowledge (docs, policies). Use fine-tuning for fixed behavior (format, style, domain reasoning). Start with RAG via Pinecone; fine-tune only when RAG plateaus.

  ❌
  Mistake: Hand-rolling tool integrations per model

Writing bespoke glue for every tool-to-model connection creates brittle, unmaintainable coordination — the integration version of the gap. Every model update breaks something, and nobody knows which thing until production surfaces it.

  ✅

Fix: Adopt MCP (Model Context Protocol) to standardize how models access tools and data — a stable interface instead of N×M custom integrations.

Average Expense to Use It

Realistic cost breakdown for shipping a reliable inference-era agent system in 2026:

• Free / OSS tier: LangGraph, AutoGen, CrewAI, and n8n (self-hosted) are free. You pay only for model inference and infra.

• Model inference: Frontier model calls run roughly $3–$15 per million input tokens and more for output, per Anthropic and OpenAI pricing. A moderate-volume support agent often lands at $500–$2,000/month.

• Vector database: Managed Pinecone starts free; production tiers typically run $70–$500/month depending on scale.

• Engineering TCO: The real cost. Closing the Coordination Gap — evals, gates, monitoring — is where senior time goes. Budget more here than on tokens. Seriously. See our AI cost optimization guide.

The counterintuitive truth: as the power layer (ON Semiconductor's thesis) drives inference cost down over the next few years, your token bill shrinks — but your coordination cost stays. That's where the durable engineering moat lives.

Total cost of ownership for a production agent: token costs fall over time, but coordination engineering remains the dominant durable expense.

Industry Impact: Who Wins, Who Loses

Winners: Power-semiconductor companies like ON Semiconductor, riding the shift to inference as a permanent operating cost. Nvidia, whose partner ecosystem extends its reach into inference. Edge inference players. And on the software side, teams that have actually systematized orchestration reliability rather than just talking about it.

Losers: Companies that bet purely on training-era economics. Vendors selling raw compute without solving the surrounding coordination — power at the hardware layer, orchestration at the software layer. And AI products that shipped multi-step agents without measuring end-to-end reliability. They'll churn customers the moment the 83% problem surfaces in the data.

The dollar logic, grounded in the Yahoo Finance thesis: ON's data center revenue went from a $250M slice of $6B to growing 30% per quarter. If inference spending overtakes infrastructure spending "in a few years," the power layer's addressable market compounds with usage, not with one-time builds — a structurally better revenue shape than selling hardware people buy once. For market data context, see Nasdaq's ON listing.

The next trillion dollars of AI value won't be built. It'll be run. And whoever makes 'run' cheaper and more reliable — at the power layer and the orchestration layer — captures it.

Reactions

Lee Samaha, contributor at The Motley Fool, made the original call, naming ON Semiconductor his "top stock to buy for 2026" before the inference angle fully materialized, citing its EV inflection and improving industrial markets in the Yahoo Finance piece.

The broader industry has been converging on the inference-cost thesis for over a year. Researchers and engineers across Google DeepMind and Anthropic have published extensively on inference efficiency and agentic reliability, while practitioner communities on the LangChain and AutoGen projects — each with large GitHub followings — are actively building the orchestration tooling that addresses the Coordination Gap at the software layer. The market thesis and the engineering reality are converging on the same answer from opposite directions.

What Happens Next

2026 H2


  **Inference cost becomes the headline metric in earnings calls**

As the Yahoo Finance thesis predicts inference overtaking infrastructure spend, expect hyperscalers and chip suppliers to report inference efficiency and power metrics explicitly — ON Semiconductor's data center segment is the early signal.

2027


  **MCP becomes the default tool-integration standard**

With MCP adoption accelerating across Anthropic and the broader ecosystem, bespoke per-model integrations decline — directly shrinking the software-layer Coordination Gap.

2027-2028


  **Inference spending surpasses infrastructure spending**

The article's central forecast — "inference spending set to surpass that for data center infrastructure in a few years" — materializes, permanently reshaping AI investment toward the operating-cost layer.

2028+


  **Orchestration reliability becomes a regulated/audited concern**

As agents handle revenue and customer outcomes at scale, end-to-end reliability (the Coordination Gap) moves from engineering nicety to compliance requirement, mirroring how power reliability is already standardized in data centers. See the NIST AI Risk Management Framework for the direction of travel.

Frequently Asked Questions

What is agentic AI?

Agentic AI refers to systems where an LLM doesn't just answer a single prompt but autonomously plans, calls tools, retrieves data, and executes multi-step tasks toward a goal. Instead of one model call, an agent loops: reason, act, observe, repeat. Frameworks like LangGraph, AutoGen, and CrewAI implement this pattern. The power of agentic AI is also its risk: every additional step introduces the AI Coordination Gap, where individually reliable steps compound into an unreliable whole. Production agentic systems require verification gates, checkpointing, and human-in-the-loop routing for low-confidence cases. The agentic approach shines for research, multi-stage support, and complex document workflows — but is overkill for tasks a single well-prompted model call can solve.

How does multi-agent orchestration work?

Multi-agent orchestration coordinates several specialized agents — each with a role, tools, and context — toward a shared objective. An orchestrator routes tasks, passes state between agents, and resolves conflicts. LangGraph models this as an explicit, stateful graph with checkpoints; AutoGen uses conversational message-passing; CrewAI uses role-based teams. The critical engineering challenge is reliability: a 6-step orchestration at 97% per step is only ~83% reliable end-to-end. Closing this Coordination Gap requires per-node verification, retries with backoff, and routing uncertain cases to humans. Standardizing tool access with MCP reduces integration fragility. Learn more in our orchestration guide.

What companies are using AI agents?

Adoption spans hyperscalers and mid-market alike. Microsoft ships AutoGen and embeds agents in Copilot; Anthropic and OpenAI both offer agentic tooling and tool-use APIs. Enterprises use agents for customer support triage, code generation, research synthesis, and back-office automation. On the infrastructure side, the AI economy that powers all these agents drives demand for inference hardware — which is exactly why ON Semiconductor (an Nvidia partner) is positioned to benefit, with data center revenue up 30% in Q1. Smaller companies typically start with workflow tools like n8n plus AI nodes before graduating to code-based orchestration. Explore patterns in our enterprise AI coverage.

What is the difference between RAG and fine-tuning?

RAG (Retrieval-Augmented Generation) injects relevant external knowledge into the prompt at inference time by retrieving from a vector database like Pinecone. It's best for knowledge that changes — company docs, policies, product catalogs. Fine-tuning instead bakes behavior into the model's weights through additional training — best for fixed patterns like output format, tone, or domain-specific reasoning. RAG is cheaper to update (just re-index documents) but adds retrieval latency to every inference call. Fine-tuning has no retrieval overhead but requires retraining to change. The practical rule: start with RAG, and only fine-tune when RAG plateaus on a stable behavior you need consistently. Many production systems use both. See our RAG deep-dive for implementation patterns.

How do I get started with LangGraph?

Install with pip install langgraph, then define a state schema, add nodes (each a Python function that reads and updates state), and connect them with edges. Set an entry point, compile, and invoke. The killer feature is built-in checkpointing — failures become recoverable instead of catastrophic, which directly addresses the AI Coordination Gap. Start with a simple linear graph (classify → retrieve → respond), add verification gates at each node, then introduce conditional edges for human routing. Read the official LangChain/LangGraph docs and reference working examples. For ready-made starting points, explore our AI agent library. LangGraph is production-ready — many teams run it at scale today. Our LangGraph tutorial walks through a full build.

What are the biggest AI failures to learn from?

The most common production failure is the silent Coordination Gap: a multi-step agent where each step looks reliable but the chain degrades to ~83% or worse, mishandling a steady fraction of cases nobody catches until customers complain. Other recurring failures: hallucinated tool outputs treated as ground truth, RAG retrieving stale or irrelevant context, runaway inference costs from chaining frontier-model calls on every step, and bespoke integrations that break with every model update. The pattern is always the same — strong components, weak coordination. The fix is empirical end-to-end evaluation, verification gates, confidence-based human routing, and standardized interfaces like MCP. Treat reliability as a measured number, not an assumption. Our workflow automation guide covers safe rollout patterns.

What is MCP in AI?

MCP (Model Context Protocol) is an open standard, introduced by Anthropic, for connecting AI models to external tools, data sources, and systems through a consistent interface. Instead of writing custom glue code for every model-to-tool connection (an N×M integration explosion), MCP provides one stable protocol — a model speaks MCP, a tool exposes MCP, and they interoperate. This directly shrinks the integration dimension of the AI Coordination Gap, making agentic systems more maintainable and portable across model providers. As adoption grows across the ecosystem, MCP is becoming the default way to give agents reliable, governed access to enterprise data and tools. Read the official MCP specification and Anthropic's documentation to implement it in your stack.

The Yahoo Finance thesis about ON Semiconductor is, at its heart, a bet on the same principle that should reshape how you build AI technology: when the obvious resource gets commoditized, value migrates to coordination. Power coordination at the hardware layer. Agent coordination at the software layer. Close your Coordination Gap before your competitors close theirs.

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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