Seven Powers Applied to AI

Sources#

Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next

Summary#

Boris Cherny uses Hamilton Helmer's 7 Powers framework (popularized by the Acquired podcast) to predict which competitive moats survive AI and which erode. His thesis: process power and switching costs collapse; network effects, scale economies, and cornered resources persist. The "SaaS apocalypse" question often debated is the wrong frame — the apocalypse hits a specific subset of SaaS (process-power and switching-cost dependent), not all of it. Direct implication for builders: the moat you bet on determines whether AI is a boost or a wrecking ball.

The seven powers (Helmer)#

Scale economies
Network effects
Counter-positioning
Switching costs
Branding
Cornered resource
Process power

Boris discusses five of these explicitly.

Powers that erode with AI#

Switching costs#

"Switching costs [erode] because you can just use the model and you can kind of port from one thing to a different thing."

If your moat is "users have built workflows / data / integrations that would cost too much to migrate," AI agents lower migration cost dramatically. Agents can rebuild integrations, port data, regenerate macros. The user's investment in your specific shape becomes less binding.

Cases this hits hardest: enterprise SaaS with deep custom integrations, vertical software with proprietary data formats, productivity tools with mountains of user-built configuration.

Mitigation: depend less on lock-in, more on continuing value delivery.

Process power#

"Process power [erodes] because for companies whose mode is workflows and process and things, [Claude] is getting really good at figuring out process. And especially with 4.7, it can just hill climb anything. So if you give it a target and you tell it to iterate until it's done, it will just do it. I think this is the first model like that."

Process power = the company has refined a way of doing things over years that competitors can't easily replicate. Boris's claim: a strong model + a target = automated hill-climb that recovers the process. Process is now imitable in ways it wasn't.

Cases this hits hardest: operationally-excellent companies whose advantage is "we run X better than anyone else" without scale or network effects backing it. Distribution, ops, customer service, etc.

Mitigation: process power needs to be paired with a structurally-protected power (scale, network) to survive.

Powers that persist#

Network effects#

User value scales with other users on the platform. AI doesn't change that. A messaging app, a marketplace, a developer ecosystem — the value is in the network, not in the code that supports it. AI may reduce the cost of building the supporting code, but it doesn't replicate the network.

Scale economies#

Cost-per-unit drops with scale. AI compute itself has scale economies (foundation model training, GPU fleet utilization). Verticals where capital intensity matters — semiconductors, infrastructure — keep their moat.

Cornered resources#

Exclusive access to a key input — a contract, a regulator, a research result, a data stream. AI doesn't grant access. If your business sits on a contract no competitor can replicate, AI doesn't dissolve that.

Power Boris doesn't explicitly evaluate#

Counter-positioning — Boris doesn't address this, but the analogy in Printing Press Software Democratization suggests counter-positioning flourishes: AI-native startups can choose business models incumbents structurally can't.
Branding — also unaddressed; arguably persists since AI doesn't reduce brand-formation costs to zero.

Why startups specifically benefit#

"If you look at the number of startups today or like maybe in the next 10 years, I think the number of startups in the next 10 years that are just going to disrupt everything is going to increase like 10×."

"A large company has to evolve their business process, retrain everyone, face internal resistance to that. No one [in this room] has that problem. If you're starting fresh, you can build with AI natively from the ground up."

Startups don't have process-power-dependent businesses to defend. They can pick the powers AI doesn't erode and build directly on those, without paying the migration cost incumbents face.

Counter-considerations#

Brand-and-trust SaaS (Stripe, Slack, etc.) sit on switching costs plus network/scale; even if switching-cost erodes, the rest holds. Boris's framework correctly predicts they're more exposed than network-effect-pure companies, but not catastrophically so.
Process power isn't dead — it's harder to monopolize. A model can hill-climb most processes given a target, but defining the right target and feeding the right inputs is itself a skill. Process power may be in transition rather than gone.
Cornered resources includes "talent." Boris doesn't dwell on this; if frontier-AI talent is cornered, that's a power AI itself amplifies for the holder (Anthropic, OpenAI, Google).
Mythos / Opus 4.7 as cornered resource. Anthropic dogfoods both internally before release. The "cornered resource" of a frontier model creates a window where the holder is meaningfully ahead — but the window closes when the model ships.

The switching-cost picture is two-sided (Emergence Capital, June 2026)#

Boris's claim is that switching costs erode because agents port data and rebuild integrations. Emergence Capital's Beyond Benchmarks 2026 surfaces the other half of the AI-tools market at the same time: buyers are consolidating. Average AI spend grew 2.2× in 2025 ($16K/mo → $34K/mo) while the median company ran just 6–8 AI vendors all year — spend doubled, the vendor list didn't. AI now takes ~7% of the enterprise software wallet (up from ~3%). The report's read: "the experimentation phase is ending… winning the first deal is now substantially more valuable than it was 18 months ago." So even as agents make it cheaper to leave an incumbent, buyers are locking in early around a handful of AI platforms — switching-cost formation running alongside switching-cost erosion. The lock-in isn't fully set: the enterprise leaderboard is still in motion (Anthropic overtook OpenAI as the most-adopted AI vendor, #3→#1; xAI #13→#5; Perplexity fell out of the top 20 despite the buzz). For the builder this sharpens the land-then-lock-in logic: the first deal is where the time-locked moat starts accruing, and the window for new-vendor adoption is narrowing. (VC-published but data-partner-sourced — Stackpack vendor-level receipts, not analyst estimates.)

Implications for builders#

If your moat is	Build AI-native
Network effects	AI helps — better tools to capture and scale the network
Scale economies	AI helps — better tools to drive unit costs lower
Cornered resources	AI is neutral — it doesn't grant access to your resource, doesn't dissolve it
Switching costs	At risk — find a replacement moat or accept margin compression
Process power	At risk — pair with another power or accept commoditization

Connections#

The Verifiability Thesis — which Powers survive depends on what stays verifiable and defensible
Boris Cherny — articulator
Printing Press Software Democratization — companion analogy (cost-of-production collapse) explaining why certain powers shift
Engineer PM Convergence — internal mirror: process-heavy organizational structures lose value the same way process-power moats do
Harness Shrinkage as Models Improve — process-imitation by hill-climbing models is the direct mechanism behind process-power erosion
AI Native Product Cadence — startup advantage of building AI-native is operational, not just strategic
Compounding Data Moat — Anthropic's concrete prescription for building a moat from the persistent-power components Boris names: cornered behavioral data + workflow lock-in that survives migration tooling
AI-Native Startup Lifecycle — operationalizes the moat construction across stages; the Scale stage exit question ("If a well-funded incumbent copied your product today, would your users stay?") is the empirical test of these powers
Founder as Agent Orchestrator — the role this analysis governs: which moats a lean-unicorn founder can plausibly build given the post-Boris erosion of switching costs and process power
The AI-Native Safe-Choice Inversion — the live counter-positioning play the open questions ask for: Campfire exploits an incumbent that's structurally reluctant to become AI-native (NetSuite can't flip without cannibalizing)
Product Velocity as Moat — a treadmill, not a Power: velocity wins the land but must convert into durable lock-in to survive

Open Questions#

Is "switching cost" really collapsing in practice, or just in narrative? Anthropic's own retention numbers, Salesforce churn, etc. would test this.
What does Boris's "cornered resource" look like for foundation-model labs that are themselves trying to commoditize? Internal contradiction or transient phase?
Counter-positioning — explicitly the "incumbent can't follow" power — should amplify under AI. Is anyone running this play deliberately?

Derived#

Learning to Co-Work with AI: A Software Engineer's Field Guide — moats-that-survive logic applied to individual careers (strategic positioning skill cluster)
Opinions on Using AI Tools & the Future of the Software Engineering Role — strategic-positioning section: which moats (and which careers) survive the AI shift
AI-Native Moats Under Frontier-Model Improvement — ranks which AI-native moats survive frontier-model improvement vs. which are merely entry motions or wasting assets

Sources#

Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next
Beyond Benchmarks 2026: Five Data Sets Grounded in the Real World — Emergence Capital, Beyond Benchmarks 2026 (June 2026): AI-vendor consolidation (spend 2.2× / vendor count flat) as the switching-cost-formation counterpart to Boris's erosion thesis. Note: several tables in this PDF-derived raw have collapsed multi-value cells (the Core Four table stacks Top Decile over Top Quartile into one cell — 736% 184%) — every figure quoted here comes from the report's prose bullets, not from a parsed table