If You Work in AI, You Need to Know Your Moat

This is you with your nice, proprietary moat

Cold Open

It’s 7:13 a.m. when your phone buzzes: “Vendor demo at 9. Claims 40‑hour savings per engineer—can you join?” You sigh, top up the coffee, and count the déjà‑vu demos you’ve survived this quarter. Sound familiar? This playbook is for anyone trying to figure out when they should whip out the corporate Amex and when they should holster that bad boy.

The AI Gold Rush & Why FOMO Is Expensive

• Budgets balloon, value lags. IDC projects global spending on AI‑supporting tech will leap past $749 B by 2028, more than double today’s ~$337 B baseline (IDC). Yet a McKinsey survey finds only 1 % of leaders say they’ve achieved “AI at scale” (McKinsey).

• SMB reality check. A June 2025 U.S. Bank survey shows 36 % of small businesses already use gen‑AI, but 68 % spend < $50/month—strong on curiosity, weak on revenue for vendors (Axios, U.S. Bank).

  
  

Know Your Niche—Build Your Moat

Generative AI has turned feature building into free samples at a Costco on Sunday - tons of them and everyone gets one. If a two‑person startup can clone your shiny “innovation” this weekend with Hugging Face weights and $10 K of cloud credits, you don’t own a moat—you own a mirage.

We can look at how well you can defend your moat with three layers:

1. Proprietary data. First‑party telemetry, weird long‑tail edge cases, or customer labels no public model has ever tasted.

2. Process context. Nuanced domain workflows, regulatory quirks, tribal knowledge that lives in the heads of veterans or buried in SOP binders.

3. Workflow stickiness. AI that sits where work happens—remove it and the assembly line stalls.

   
   

Models alone are fungible; those three layers aren’t. MIT Sloan calls this the shift from algorithmic advantage to contextual advantage—and context is wickedly hard to copy (MIT Sloan).

Five Questions to Pinpoint Your Niche

1. What proprietary data can we access that rivals can’t? No secret sauce, no moat.

2. Where do we own process context or institutional knowledge? Think niche regulations or hyper‑specific ontologies.

3. Does latency, privacy, or deployment environment set us apart? Edge‑deployed vision models or on‑prem LLMs for HIPAA workloads raise the bar.

4. Which KPI will this solution 5×? No metric → no moat. Tie every initiative to a quantifiable, business‑critical outcome.

5. Can we enrich an ecosystem flywheel? SDKs, APIs, or data marketplaces that lock partners into your orbit.

   
   

Turn Insight into Action

• Write down your edge. If you can’t list at least three non‑trivial assets competitors can’t touch, you’re vending commodity features—rent, don’t build.

• Prototype off‑the‑shelf first. Validate KPI impact with no‑code or API glue before burning sprints on custom models.

• Collect the exhaust. Every click‑stream, sensor ping, or support transcript is tomorrow’s training data—label it like it’s gold.

• Human the magic. Use AI to torch toil so domain experts can handle the messy, high‑context 20 %.

• 30‑Minute "Moat Check." After every major tech decision we ask: Did we widen, maintain, or erode the moat? If nobody can answer, you should stop for pause.

  
  

Case‑Study Capsules (Proof > Pitch)

• Starbucks Deep Brew leverages 25 years of loyalty data to optimize staffing and menu mix—something competitors can’t buy off‑the‑shelf.

• UPS Network Planning Tools crunch decades of route telemetry plus real‑time weather, shaving service misses even during peak holiday chaos.

• Netflix Foundation Recommender feeds 230 M subscriber signals into a giant foundation model, saving >$1 B annually by slashing churn.

  
  

These aren’t flexes about bigger GPUs; they’re lessons in turning unique data + deep context into compounding edge.

Build vs. Buy: A Reality Check

Question	Build	Buy / API
Cap‑ex	Training frontier models is eye‑watering—OpenAI spent $80‑$100 M on GPT‑4 (TechRadar Pro), while Google’s Gemini Ultra cost an estimated $191 M (Fortune).	Op‑ex: pay‑per‑token or per‑seat; scales with usage
Time‑to‑value	6–18 months to MVP	Days to weeks
Talent	Requires scarce ML engineers & SREs	Outsourced to provider
Edge cases / IP	Full control—great for domain‑specific IP	Risk of lock‑in & data exposure

FinOps practitioners warn production AI can spike cloud bills 30 % or more without guardrails (FinOps Foundation). Michael Dell’s 2025 keynote summarizes the modern stance: “Buy the plumbing, build the differentiation.” (theCUBE Research).

Vendor & Agent Sprawl: The Silent Budget Killer

• Shadow AI everywhere. A SecurityWeek study found 50 % of employees use unapproved AI tools (SecurityWeek).

• Tool overload. Axios reports companies juggle ≈ 67 AI tools, 90 % unlicensed (Axios).

• Agent sprawl. CIO.com warns that every department spinning up its own agent platform breeds complexity and kills ROI (CIO).

  
  

Governance Moves That Work

1. Inventory every AI API & agent with SaaS‑management software.

2. Lock down contracts—data rights, IP, model‑reuse clauses non‑negotiable.

3. Quarterly kill‑or‑consolidate reviews tied to usage × value metrics.

   
   

Market Signals & Case‑Study Nuggets

• CIO sentiment. a16z’s 2025 survey of 100 enterprise CIOs shows a marked tilt toward buying mature Gen‑AI apps over building from scratch (a16z).

• Strategic framing. Dell’s mantra reappears across analyst notes, echoing the shift to platform plumbing over bespoke models.

• Software rewiring. VentureBeat argues that in an AI‑first stack “software functions aren’t trapped inside apps” (VentureBeat).

  
  

A Pragmatic Manager’s Checklist

1. Inventory workflows & crown‑jewel data.

2. Map each use‑case on a 2 × 2 (strategic differentiation vs. technical complexity).

3. Score tools on ROI horizon, vendor viability, integration overhead, data/IP risk.

4. Pilot fast, exit faster. Kill anything that doesn’t beat a control metric in 90 days.

5. Govern: contracts, usage dashboards, quarterly sprawl audit.

6. Up-skill the crew. Prompt‑engineering literacy beats another slide deck.

   
   

In the AI land‑rush, the best defense against budget burn‑out is a maniacal focus on your super‑power and a willingness to walk away from “wow” demos that don’t move your scoreboard.

Closing Credits — My Exit Ticket For You

AI isn’t a corporate shopping spree; it’s a strategic lever. Before you swipe the card on yet another “paradigm‑shifting” subscription, pause and run this three‑point gut check:

1. Moat meter. Will this widen, maintain, or erode our moat? If the answer isn’t “widen,” keep your wallet holstered.

2. Metric mandate. Which KPI gets a 5× boost? No KPI, no contract.

3. Monday morning test. Will operators actually reach for this on Day 1, or will it rot in a backlog of abandoned pilots?

   
   

If any box lands on “not sure,” defer the spend and invest that energy in clarifying your niche. Capital and attention are finite; context and disciplined execution are the true multipliers.

Build when the work shapes the moat. Buy when the market already paved the road. Measure twice, ship once, and you’ll outlast the demo parade.

✌️Steven