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Best strategies for startups in the AI era

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Best strategies for startups in theAI eraMade for Freddy Vega, by Hanademi
Best strategies for startups in theAI eraMade for Freddy Vega, by Hanademi
Anthropic annualized run rateLog scale: every gridline is ×10.Made for Freddy Vega, by HanademiSources: Nuñez, M. (2026, May 8). Anthropic says it hit a $30 billion revenue run rate. VentureBeat; Verma, A. (2026, Apr 23). Anthropic: TheHypergrowth Tightrope. AlphaTarget.UnitUSD$10.00M$100.0M$1B$10B$100BJan 2024Dec 2024End 2025Feb 2026Mar 2026Apr 2026$87M$30B
Anthropic went from $87M to $30B run rate in 27 months: outcome-priced agent infrastructure is the fastest enterprise ramp on record.
Run rate anualizado de AnthropicEscala logarítmica: cada línea es ×10.Made for Freddy Vega, by HanademiFuentes: Nuñez, M. (2026, May 8). Anthropic says it hit a $30 billion revenue run rate. VentureBeat; Verma, A. (2026, Apr 23). Anthropic: TheHypergrowth Tightrope. AlphaTarget.UnidadUSD$10,00 M$100,0 M$1 mil M$10 mil M$100 mil MJan 2024Dec 2024End 2025Feb 2026Mar 2026Apr 2026$87 M$30 mil M
Anthropic pasó de $87M a $30.000M de run rate en 27 meses: la infraestructura de agentes con precio por resultado es la escalada empresarial más rápida registrada.
Claude Code hit $1B in six months,with over 1.Sources: Verma, A. (2026, Apr 23). Anthropic: The Hypergrowth Tightrope. AlphaTarget.
Claude Code hit $1B in six months, with over 1.
Claude Code alcanzó $1.000M en seismeses, con más de 1.Fuentes: Verma, A. (2026, Apr 23). Anthropic: The Hypergrowth Tightrope. AlphaTarget.
Claude Code alcanzó $1.000M en seis meses, con más de 1.
Cursor ARR rampLog scale: every gridline is ×10.Made for Freddy Vega, by HanademiSources: Lemkin, J. (2025, Nov 18). Cursor Hit $1B ARR in 24 Months. SaaStr.UnitUSD ARR$1M$10M$100M$1,000MDec 2023Oct 2024Jan 2025Jun 2025Nov 2025$1M$1,000M
Cursor went from $1M to $1B ARR in 24 months on per-seat-plus-usage: the AI era rewards both the agent and the copilot.
Escalada de ARR de CursorEscala logarítmica: cada línea es ×10.Made for Freddy Vega, by HanademiFuentes: Lemkin, J. (2025, Nov 18). Cursor Hit $1B ARR in 24 Months. SaaStr.UnidadUSD de ARR$1 M$10 M$100 M$1.000 MDec 2023Oct 2024Jan 2025Jun 2025Nov 2025$1 M$1.000 M
Cursor pasó de $1M a $1.000M de ARR en 24 meses con asiento+uso: la era IA premia tanto al agente como al copiloto.
AI pricing model mixMade for Freddy Vega, by HanademiSources: ICONIQ Growth. (2026). 2026 State of AI: Bi-Annual Snapshot; Lemkin, J. (2026, Feb 7). AI Pricing is aMess. SaaStr.UnitShare of AI companies (%)58%Subscription (Jan 2026)37%Plan to change in 12 months35%Usage-based (Jan 2026)18%Outcome-based (Jan 2026)2%Outcome-based (Q2 2025)
Outcome pricing jumped 9x in three quarters, but subscription still leads at 58% and 37% of vendors plan to change pricing again within a year.
Mezcla de modelos de precio en IAMade for Freddy Vega, by HanademiFuentes: ICONIQ Growth. (2026). 2026 State of AI: Bi-Annual Snapshot; Lemkin, J. (2026, Feb 7). AI Pricing is aMess. SaaStr.UnidadPorcentaje de empresas IA58 %Suscripción (ene 2026)37 %Planea cambiar en 12 meses35 %Por uso (ene 2026)18 %Por resultado (ene 2026)2 %Por resultado (Q2 2025)
El precio por resultado se multiplicó por 9 en tres trimestres, pero la suscripción aún lidera con 58% y 37% planea cambiar de precio en 12 meses.
Salesforce Agentforce price pointsMade for Freddy Vega, by HanademiSources: Salesforce. (2025, May 15). Flexible Agentforce Pricing; Lemkin, J. (2026, Feb 17). Salesforce Now Has 3+ Pricing Models forAgentforce. SaaStr.UnitUSD per unitPer conversation (Oct 2024)$2Per action, Flex Credits (May 2025)$0.1Per user/month AELA (late 2025)$125
Salesforce shipped three Agentforce price models in 18 months and now runs all three at once: nobody, even the incumbent, knows what the right unit is.
Precios de Salesforce AgentforceMade for Freddy Vega, by HanademiFuentes: Salesforce. (2025, May 15). Flexible Agentforce Pricing; Lemkin, J. (2026, Feb 17). Salesforce Now Has 3+ Pricing Modelsfor Agentforce. SaaStr.UnidadUSD por unidadPor conversación (oct 2024)$2Por acción, Flex Credits (may 2025)$0,1Por usuario/mes AELA (fin 2025)$125
Salesforce lanzó tres modelos de precios para Agentforce en 18 meses y corre los tres a la vez: ni el incumbente sabe cuál es la unidad correcta.
Sierra ARR, outcome-priced agentsMade for Freddy Vega, by HanademiSources: Cheeky Pint. (2026, Mar 10). Bret Taylor of Sierra on AI agents and outcome-based pricing.UnitUSD ARR$165MQuarter 9$150MQuarter 8$100MQuarter 7
Sierra charges only when its agent resolves the case and still cleared $100M ARR by quarter seven: outcome pricing works when the agent really works.
ARR de Sierra, agentes por resultadoMade for Freddy Vega, by HanademiFuentes: Cheeky Pint. (2026, Mar 10). Bret Taylor of Sierra on AI agents and outcome-based pricing.UnidadUSD de ARR$165 MTrimestre 9$150 MTrimestre 8$100 MTrimestre 7
Sierra cobra solo si su agente resuelve el caso y aún así superó $100M de ARR en el trimestre siete: el precio por resultado funciona cuando el agente funciona.
Intercom Fin sells at $0.99 per resolution.Sources: Stripe. (n.d.). Intercom Innovates Outcome-Based Pricing for Fin AI Agent; Majumder, D. (2025, Sep 30). Fin AI pricing explained. pagergpt.
Intercom Fin sells at $0.99 per resolution.
Intercom Fin vende a $0,99por resolución.Fuentes: Stripe. (n.d.). Intercom Innovates Outcome-Based Pricing for Fin AI Agent; Majumder, D. (2025, Sep 30). Fin AI pricing explained. pagergpt.
Intercom Fin vende a $0,99 por resolución.
Decagon: ARR and valuationMade for Freddy Vega, by HanademiSources: Sacra. (2025). Decagon revenue, valuation & funding; Decagon. (2025, June 23). Decagon raises Series C at $1.5B valuation.UnitUSD$0$10M$20M$30M$2,000M$3,000M$4,000M$10M$1,500M$4,500MARR end-2024ARR Oct 2025Valuation Jun 2025Valuation Jan 2026ARR (USD M)Valuation (USD M)
Decagon tripled ARR to $35M while valuation tripled to $4.5B: outcome-priced support agents are getting paid like infrastructure.
Decagon: ARR y valoraciónMade for Freddy Vega, by HanademiFuentes: Sacra. (2025). Decagon revenue, valuation & funding; Decagon. (2025, June 23). Decagon raises Series C at $1.5B valuation.UnidadUSD$0$10 M$20 M$30 M$2.000 M$3.000 M$4.000 M$10 M$1.500 M$4.500 MARR fin-2024ARR oct 2025Valoración jun 2025Valoración ene 2026ARR (USD M)Valoración (USD M) · Valuation (USD M)
Decagon triplicó su ARR a $35M mientras la valoración triplicó a $4.500M: los agentes de soporte por resultado se pagan como infraestructura.
Gross margin: AI-native vs SaaSMade for Freddy Vega, by HanademiSources: ICONIQ Growth. (2026). 2026 State of AI; Murray, B. (2026, May 12). Inference Efficiency Ratio. The SaaS CFO.UnitGross margin (%)AI-native 202441%AI-native 2026E52%Bessemer Supernovas25%Bessemer Shooting Stars60%Traditional SaaS80%
AI-native margins climb to 52% but still trail SaaS at 80%, and the fastest Supernovas live on just 25%: speed is bought with inference cost.
Margen bruto: AI-nativo vs SaaSMade for Freddy Vega, by HanademiFuentes: ICONIQ Growth. (2026). 2026 State of AI; Murray, B. (2026, May 12). Inference Efficiency Ratio. The SaaS CFO.UnidadMargen bruto (%)AI-nativo 202441 %AI-nativo 2026E52 %Supernovas Bessemer25 %Shooting Stars Bessemer60 %SaaS tradicional80 %
El margen AI-nativo sube a 52% pero aún queda lejos del 80% del SaaS, y los Supernovas más rápidos viven con 25%: la velocidad se paga con inferencia.
Three failure modes of agent startupsMade for Freddy Vega, by HanademiSources: Davis & Temkin (2025, Mar 24). TechCrunch; Orosz, G. (2025, Jun 12). Pragmatic Engineer; Doerer, K. (2025, May 9). CX Dive.COMPANYHEADLINEEVIDENCELESSON11xAI SDR, $74Mraised70-80% churn, $3M realARR vs $14M reported,unauthorized logosOutcome pricing exposesfake retention fastBuilder.ai$1.5B peakvaluation2024 revenue restated$220M to $55M;$40M/quarter burnRestated revenue endsthe round, fastKlarnaAI bot did700 FTEResolution time 11 to 2min, $40M lift, thenrehired humans in 2025Quality collapse forcesthe walkback
Three high-profile crashes show the same pattern: when the agent cannot truly deliver the result, churn, restatements, and rehires arrive within months.
Tres modos de falla de startups de agentesMade for Freddy Vega, by HanademiFuentes: Davis & Temkin (2025, Mar 24). TechCrunch; Orosz, G. (2025, Jun 12). Pragmatic Engineer; Doerer, K. (2025, May 9). CX Dive.EMPRESATITULAREVIDENCIALECCIÓN11xSDR IA, $74MlevantadosChurn 70-80%, $3M ARRreal vs $14Mreportado, logos sinpermisoEl precio porresultado exponeretención falsa rápidoBuilder.aiValoraciónpico $1.500MIngresos 2024reformulados de $220Ma $55M; quema$40M/trim.Reformular ingresostermina la ronda,rápidoKlarnaBot IA hacía700 FTEResolución de 11 a 2min, $40M de mejora,luego volvió acontratar humanos en2025El colapso de calidadobliga la reversión
Tres caídas sonadas muestran el mismo patrón: si el agente no entrega de verdad, llegan en meses el churn, las reformulaciones y la recontratación.
11x: reported vs realMade for Freddy Vega, by HanademiSources: Davis, D-M. & Temkin, M. (2025, Mar 24). TechCrunch.UnitUSD millions / percent$75Customer churn (%)$14Reported ARR (USD M)$3Actual ARR (USD M)
11x reported $14M ARR; only ~$3M stuck after 70-80% churn: outcome buyers cancel the moment the agent stops resolving.
11x: reportado vs realMade for Freddy Vega, by HanademiFuentes: Davis, D-M. & Temkin, M. (2025, Mar 24). TechCrunch.Unidadmillones USD / porcentaje$75Churn de clientes (%)$14ARR reportado (USD M)$3ARR real (USD M)
11x reportó $14M de ARR; solo ~$3M quedaron tras churn 70-80%: los compradores por resultado cancelan en cuanto el agente deja de resolver.
ATMs did not kill bank tellersMade for Freddy Vega, by HanademiSources: Bessen, J. (2015). Toil and Technology. IMF F&D; Bessen, J. (2015). BU Law Working Paper 15-49.UnitCount / percentTellers per branch 198820Tellers per branch 200413Urban branch growth 1988-2004 (%)43Teller FTE growth/yr post-2000 (%)2
Each branch needed fewer tellers, so banks opened 43% more branches and teller employment kept growing 2% a year: cheaper labor expanded the market.
Los cajeros automáticos no eliminarontellersMade for Freddy Vega, by HanademiFuentes: Bessen, J. (2015). Toil and Technology. IMF F&D; Bessen, J. (2015). BU Law Working Paper 15-49.UnidadConteo / porcentajeTellers por sucursal 198820Tellers por sucursal 200413Crecimiento sucursales urbanas 1988-2004 (%)43Crecimiento FTE tellers/año post-2000 (%)2
Cada sucursal necesitaba menos tellers, así que los bancos abrieron 43% más sucursales y el empleo siguió creciendo 2% al año: mano de obra barata expandió el mercado.
BLS 2024-34 projectionsMade for Freddy Vega, by HanademiSources: U.S. Bureau of Labor Statistics. (2025). Accountants and Auditors, OOH; Bookkeeping Clerks, OOH.UnitThousand US jobsAccountants & auditors20242034 projected1,579.81,652.6Bookkeeping clerks1.1×1,613.41,519.1
Decades after spreadsheets, judgment-heavy accountants still grow 5% while clerical bookkeepers fall 6%: agents will upgrade the senior role and hollow the junior one.
Proyecciones BLS 2024-34Made for Freddy Vega, by HanademiFuentes: U.S. Bureau of Labor Statistics. (2025). Accountants and Auditors, OOH; Bookkeeping Clerks, OOH.UnidadMiles de empleos en EE. UU.Contadores y auditores20242034 proyectado1.579,81.652,6Empleados contables1,1×1.613,41.519,1
Décadas tras la hoja de cálculo, los contadores de criterio crecen 5% y los empleados contables caen 6%: los agentes elevarán al rol senior y vaciarán al junior.

The research behind this deck

Outcome pricing is rewriting startup playbooks: Anthropic, Cursor, and Sierra are scaling at unprecedented speed, but thinner margins, pricing chaos, and high-profile collapses prove the model punishes anyone who cannot actually deliver the work.

Key findings

The argument

This research is published in English and Spanish. Ver en español

La investigación detrás de esta presentación

El precio por resultado reescribe el manual de startups: Anthropic, Cursor y Sierra escalan a velocidad inédita, pero márgenes más finos, caos de precios y colapsos sonados muestran que el modelo castiga a quien no entrega el trabajo.

Hallazgos clave

El argumento

Esta investigación se publica en inglés y español. Read in English