---
author: a16z
date: '2025-12-26'
guest: ''
layout: post.njk
source: https://www.youtube.com/watch?v=BkJyVjq457w
speaker: a16z
tags:
  - electro-industrial-stack
  - financial-services-ai
  - agent-layer
  - systems-of-record
  - supply-chain-reshoring
title: 2026年人工智能重塑经济格局：三大前沿洞察
summary: 本文汇集了a16z对2026年经济发展的三大前瞻性洞察。Ryan Macintosh预言‘电工产业栈’（electro-industrial stack）将驱动美国工业未来，强调生态系统建设与人才融合。Angela Strange指出金融服务与保险业将迎来关键转折点，AI原生竞争者将取代遗留系统，带来工作流并行化、风险管理平台整合及企业效率的指数级提升。Sarah Wang则揭示‘动态代理层’（dynamic agent layer）正崛起，将颠覆传统‘记录系统’（systems of record），实现意图与执行的无缝对接，重塑企业软件运作模式。
insight: ''
draft: true
series: ''
category: ai-application
area: tech-engineering
project: []
people: []
companies_orgs: []
products_models: []
media_books: []
status: evergreen
---
### 电工产业栈：重塑美国工业未来

本次分享聚焦于2026年的几项重大创新理念，其中Ryan Macintosh探讨了“电工产业栈”（electro-industrial stack）的兴起，这是一种全新的构建和驱动美国工业未来的基础。他指出，下一轮工业演进将不仅仅发生在工厂内部，更将深入到驱动这些工厂运转的机器之中。这种“电工产业栈”融合了驱动电动汽车、无人机、数据中心及现代制造业的综合技术。

在分析当前关于国际技术竞争的普遍观点时，Macintosh提到，人们常陷入“中国遥遥领先，我们无法追赶”或“中国落后，美国飞速发展”的二元论。他认为，现实情况是，中国拥有的技术，美国同样能够实现，因为美国在工程技术和特定领域执行力方面表现出色。例如，在稀土分离与加工等领域，美国拥有技术能力且能快速完成。然而，真正的挑战在于构建一个能够支持大规模工业化生产的生态系统，并实现低成本运营。

他进一步以SpaceX或Android等需要快速迭代的大型企业为例，说明它们常常因缺乏外部生态支持而被迫进行垂直整合，这是一种“因必要而非战略”的选择。在中国，则存在完善的供应商体系（包括一级、二级、三级供应商、组件及原材料）以及能够支持快速发展的机构和政治环境。Macintosh强调，美国在这些生态系统层面的建设可能需要数年甚至数十年才能赶上中国。虽然美国能掌握核心技术，但若其他配套环节未能同步发展，就如同“移动瓶颈”一般，无法实现整体突破。

要构建“电工产业栈”或支持这些技术的核心组件，美国需要将硅谷的软件人才与文化，与工业领域的资深专家相结合。即使是SpaceX，也曾从航天飞机项目和传统承包商那里吸纳推进系统人才。这表明，跨界融合了实际专业知识和前沿软件人才至关重要。此外，将工程与制造紧密结合，例如“面向制造的设计”（design for manufacturing），在同一生态系统内进行，能显著提升效率。最后，他强调需要围绕工业使命建立声望，吸引顶尖人才，因为许多优秀的工程师在传统硅谷领域也能找到高薪且有意义的问题去解决。软件对物理世界的影响将通过这些“具身化、电气化”的组件实现，包括电池、电力电子、计算单元和电机等。这些核心组件需要被重新本土化生产或在终端产品制造商内部实现垂直整合。能够解决这些复杂技术问题并拥有相应人才基础的国家，将在21世纪占据优势。随着软件和人工智能的进步，在自动化和工业军事领域拥有这些供应链将变得更加重要，并对未来经济和军事力量的控制产生深远影响。

<details>
<summary>Original English</summary>
Welcome
 to
 part
 two
 of
 our
 2026
 Big


Ideas.
 Ryan
 Macintosh
 explores
 the
 rise


of
 what
 he
 calls
 the
 electro-industrial


stack,
 a
 new
 foundation
 for
 how
 we
 build


and
 power
 America's
 industrial
 future.


Angela
 Strange
 identifies
 a
 critical


turning
 point
 in
 financial
 services
 and


insurance
 where
 decades
 old
 systems
 are


finally
 ready
 for
 reinvention.
 And
 Sarah


Wang
 reveals
 how
 a
 dynamic
 agent
 layer


is
 emerging
 to
 overtake
 traditional


systems
 of
 record
 fundamentally
 changing


how
 enterprise
 software
 operates.
 These


aren't
 just
 forecasts.
 their
 firsthand


perspectives
 from
 investors
 driving


change
 across
 American
 dynamism,


financial
 services,
 and
 enterprise


technology.


My
 name
 is
 Ryan
 McIntosh.
 I'm
 an


investing
 partner
 on
 the
 American


Dynamism
 team.
 My
 big
 idea
 for
 2026
 is


that
 the
 electro-industrial
 stack
 will


move
 the
 world.
 The
 next
 industrial


evolution
 won't
 just
 happen
 in


factories,
 but
 inside
 the
 machines
 that


power
 them.
 This
 is
 the
 rise
 of
 the


electroindustrial
 stack.
 Combined
 tech


that
 powers
 electric
 vehicles,
 drones,


data
 centers,
 and
 all
 of
 modern


manufacturing.
 I
 I
 think
 there
 are


common
 uh
 tropes
 people
 report
 on.


People
 talk
 about
 China's
 so
 far
 ahead


uh
 we
 can't
 catch
 up.
 And
 actually,
 you


know,
 you
 go
 back
 a
 couple
 years
 ago
 and


people
 were
 saying,
 you
 know,
 China's


very
 far
 behind
 and
 America's
 incredibly


fast.
 So,
 we've
 seen
 sort
 of
 like
 a


whiplash
 and
 now
 it's
 the
 opposite.
 I


think
 the
 reality
 is
 that
 uh
 you
 know,


the
 technology
 that
 China
 has,
 America


can
 do.
 Uh
 we're
 very
 good
 at
 uh


engineering.
 We're
 very
 good
 at
 doing


specific
 things.
 And
 in
 fact
 even
 like


the
 you
 know
 recent
 stuff
 around
 rare


earth
 for
 example
 rare
 earth
 separation


and
 processing
 we
 know
 how
 to
 do
 this
 we


can
 do
 this
 we
 [music]
 can
 do
 it


incredibly
 fast.
 The
 real
 challenge
 is


building
 the
 ecosystem
 to
 do
 this


industrially
 at
 scale
 and
 doing
 it
 at
 a


low
 cost.
 Another
 example
 you
 know


people
 typically
 talk
 about
 is
 is


companies
 like
 SpaceX
 or
 Android
 these


large
 businesses
 that
 need
 to
 move


incredibly
 fast
 and
 thus
 vertically


integrate.
 In
 many
 ways
 they're


vertically
 integrating
 by
 necessity
 not


strategy.
 There
 just
 isn't
 an
 ecosystem


of
 companies
 that
 can
 scale
 with
 them.


That
 is
 not
 the
 case
 in
 China.
 There
 are


tier
 one,
 two,
 three
 suppliers,


components,
 raw
 materials
 that
 exist
 in


those
 ecosystems
 as
 well
 as
 the,
 you


know,
 the
 institutions
 [music]
 and
 uh
 uh


political
 bodies
 that
 allow
 them
 to
 move


incredibly
 fast.
 Those
 are
 the
 things


that
 might
 take
 years
 or
 decades
 [music]


for
 us
 to
 catch
 up
 to
 China.
 We
 can
 do


the
 technology,
 but
 everything
 else


needs
 to
 grow
 with
 it,
 or
 else
 we're


just
 moving
 the
 bottleneck.
 So
 if
 you


want
 to
 build
 uh
 the
 electro-industrial


[music]
 stack
 or
 the
 core
 components


that
 feed
 into
 these
 technologies
 in
 the


United
 States
 uh
 you
 need
 to
 blend


Silicon
 Valley
 software
 talent
 and


culture
 uh
 with
 industrial
 veterans.


Even
 companies
 like
 SpaceX,
 they
 were


pulling
 propulsion
 talent
 from
 people


who
 worked
 on
 you
 know
 shuttle
 program


and
 various
 old
 school
 [music]


contractors
 when
 Shawwell
 came
 from


aerospace
 corporation.
 Like
 there
 there


is
 a
 there
 is
 a
 world
 where
 you
 need


this
 actual
 expertise.
 You
 [music]
 need


to
 know
 what's
 been
 tried
 before.
 There


are
 smart
 people
 ex
 out
 there
 in
 these


other
 companies,
 but
 you
 need
 to
 be
 able


to
 move
 [music]
 a
 lot
 faster.
 There's
 a


lot
 of
 advantages
 of
 software
 today.
 So,


you
 need
 to
 be
 able
 to
 get
 the
 software


talent
 that
 may
 not
 exist
 in
 these


companies
 previously.
 [music]
 You
 also


want
 to
 colllocate
 engineering
 and


manufacturing
 concepts
 like
 design
 for


[music]
 manufacturing
 uh
 are
 something


that
 you
 know
 when
 you're
 tightly


integrated
 on
 the
 same
 footprint
 or
 in


the
 same
 ecosystem
 uh
 you
 can
 move
 a
 lot


faster.
 And
 I
 think
 also
 you
 need
 to


build
 prestige
 around
 the
 mission.
 Um,


for
 a
 lot
 of
 sort
 of
 traditional
 Silicon


Valley
 talent,
 the
 smartest
 people
 can


work
 on
 a
 number
 of
 problems
 and
 there


are
 a
 lot
 of
 problems
 that
 are
 worthy
 of


working
 on.
 Some
 of
 them
 pay
 more
 than


others.
 So,
 you
 need
 to
 attach
 sort
 of
 a


a
 prestige
 or
 a
 purpose
 to
 what
 you're


working
 on
 and
 uh
 use
 that
 to
 to
 [music]


attract
 the
 top
 talent.
 The
 way
 that


software
 will
 affect
 the
 physical
 world


is
 through
 these
 sort
 of
 embodied


[music]
 electrified
 components.
 And
 it's


not
 just,
 you
 know,
 not
 just
 a
 humanoid


robot
 or
 an
 electric
 vehicle,
 but
 it's


the
 batteries,
 it's
 the
 power


electronics,
 it's
 the
 compute,
 it
 it's


it's
 the
 motors.
 All
 these
 things
 we're


going
 to
 need
 to
 either
 reshore
 or


vertically
 integrate
 within
 the


companies
 who
 are
 building
 the
 end


product.
 These
 are,
 you
 know,
 very


technical.
 These
 require
 a
 lot
 of


expertise.
 These
 are
 very
 difficult


problems
 to
 solve.
 But
 the
 companies
 who


solve
 it
 and
 the
 countries
 who
 have
 the


talent
 base
 to
 in
 order
 to
 support
 it


are
 the
 ones
 who
 are
 going
 to
 win
 in
 the


21st
 century.
 And
 as
 software
 and


artificial
 intelligence
 get
 stronger
 and


they
 start
 having
 you
 know
 more
 of
 a


presence
 in
 in
 automation
 and
 [music]


industrial
 military
 owning
 these
 supply


chains
 is
 going
 to
 become
 even
 more


important.
 And
 I
 think
 as
 we
 you
 know


look
 forward
 50
 100
 years
 owning
 the


supply
 chains
 today
 are
 going
 to
 have
 a


lot
 of
 effects
 of
 who
 controls
 both
 the


sort
 of
 economic
 and
 military
 uh
 powers


[music]


uh
 in
 in
 the
 future.
</summary>
</details>

### 金融服务业的AI驱动转型临界点

Angela Strange 指出，金融服务和保险行业正处于一个关键的转折点，长期存在的遗留系统（legacy systems）面临被淘汰的风险，其风险已超过维持现状的风险。她预测，大型金融机构将不再续签旧有合同，转而采用更现代化的、由AI驱动的竞争者。其根本原因在于，新一代的基础设施不仅仅是简单地集成AI，而是能够整合来自遗留核心系统、外部系统以及非结构化数据的统一信息，形成新的“记录系统”（system of record）。这使得企业不仅能够扩展规模，还能充分利用AI的能力。

当这一转变发生时，将带来三个对客户和开发者都至关重要的变化。首先，工作流程（workflows）将实现并行化，告别在不同屏幕间切换和复制粘贴数据的低效模式。例如，一个抵押贷款团队可以同时处理承销贷款所需的400多项任务，甚至可以将一些重复性工作交给AI代理（agents）完成，供稍后审核。其次，现有的业务分类将得到扩展。客户数据，包括入职（onboarding）、身份验证（KYC/KYB）、交易监控，乃至客户与客服团队的互动数据，都可能汇集到一个统一的风险管理平台中，更有效地整合欺诈、风险和合规管理。第三，对于开发者而言，新的市场领导者将实现指数级增长（10x bigger）。这不仅因为相关的软件类别市场规模扩大，更因为软件能够承担大量人类不愿从事或企业招聘不足的劳动力岗位。正如俗语所说，竞争的不是AI本身，而是使用AI的竞争对手。因此，顶尖的银行和保险公司将优化其内部系统，以充分利用AI，成为下一十年的最强竞争者。

许多公司已讨论此转型数十年，但现在为何不同？主要有三个原因：第一，许多公司仍运行着数十年前的“大型机”（mainframes），其系统已接近因规模扩展而崩溃的边缘。第二，企业现在意识到，不利用AI将导致大量收入损失。例如，在保险业，由于无法快速处理需求，保险核保员有时甚至无法应对涌入的业务量，无法及时处理文件和扫描。若能部署正确系统并叠加AI，这部分巨大的收入潜力即可被捕获。第三，市场上已涌现出由深刻理解行业并具备深厚技术实力的企业家构建的、AI优先的新一代软件选项。这些软件彻底重构了平台，既能支持企业扩展，又能灵活地集成AI。

这种转型带来了巨大的机遇，可能重塑现有企业的胜败格局。那些率先采用新平台的公司将获得优势。我们已看到一些银行和保险公司开始以“前瞻性”、“易于合作”、“乐于拥抱变革”的形象获得声誉。在抵押贷款服务等领域，一些公司已成功将5%利润率的业务提升至50%利润率。这种效率的快速提升将使其在竞争中遥遥领先。作为投资者，对基础设施领域的热情源于其能够赋能卓越的消费者和商业体验。例如，银行之所以会向已拥有某项产品的客户推荐该产品，是因为客户数据分散在不同部门。而现在，通过统一的数据层和由AI代理辅助的智能团队，客户服务将能理解客户需求，提供个性化产品推荐，甚至预测未来需求，从而创造出色的客户和商业体验。到2026年，任何构建了AI优先平台并成功进入这些大型行业的公司，都将迎来显著的加速发展。对于那些深刻理解或好奇于银行业或保险业任何“古老”环节的创始人而言，机会就在眼前，可以更快地构建软件，并获得客户的青睐。

<details>
<summary>Original English</summary>
I'm
 Angela
 Strange,
 a
 general
 partner
 on


the
 AI
 applications
 fund.
 And
 my
 big


idea
 for
 2026
 is
 there
 will
 be
 a


dramatic
 turning
 point
 coming
 to


financial
 services
 and
 insurance
 where


finally
 the
 risk
 of
 not
 replacing
 legacy


systems
 will
 exceed
 the
 risk
 of
 change.


It's
 already
 happening.
 [music]
 Major


institutions
 will
 let
 long-standing


contracts
 lapse
 and
 implement
 their


newer
 AI
 native
 competitors.
 Why?
 The


next
 generation
 of
 infrastructure


doesn't
 just
 add
 [music]
 AI.
 They
 unify


the
 data
 from
 legacy
 cores,
 from


external
 systems,
 from
 unstructured
 data


into
 a
 new
 system
 of
 record,
 enabling


FIS
 not
 only
 to
 scale,
 but
 to
 take
 full


advantage
 of
 AI.
 When
 this
 happens,


there
 are
 three
 major
 changes
 that
 are


important
 for
 both
 customers
 and


builders.
 One,
 workflows
 will
 finally


become
 parallelized.
 No
 more
 bouncing


between
 screens,
 cut
 pasting
 data.
 For


instance,
 your
 mortgage
 team
 could
 see


the
 400
 plus
 tasks
 that
 are
 needed
 to


underwrite
 your
 loan,
 do
 them
 in


parallel,
 and
 even
 have
 agents
 do
 some


of
 the
 more
 mundane
 ones
 for
 you
 to


check
 later.
 Second,
 the
 categories
 as


we
 know
 them
 are
 going
 to
 expand.
 For


instance,
 [music]


customer
 data
 from
 onboarding,
 KYC,
 KYB,


transaction
 monitoring,
 even
 how
 those


customers
 behave
 with
 your
 customer


service
 team
 could
 all
 sit
 into
 a
 single


risk
 platform.
 brings
 together
 fraud,


risk,
 compliance
 much
 more
 effectively.


And
 then
 [music]
 third,
 most
 excitingly


for
 the
 builders,
 the
 new
 winners
 here


will
 be
 10x
 bigger.
 Not
 only
 because


those
 software
 categories
 [music]
 are


bigger,
 but
 because
 software
 is
 able
 to


consume
 a
 lot
 of
 the
 labor
 that
 humans


didn't
 want
 to
 do
 anyways
 or
 that
 banks


or
 insurance
 companies
 couldn't
 hire
 for


fast
 enough.
 So,
 as
 the
 saying
 goes,


it's
 not
 [music]
 AI
 that's
 the


competition,
 it's
 your
 competitors
 using


AI.
 So
 the
 best
 banks,
 the
 best


insurance
 companies
 will
 fix
 their


plumbing
 and
 enable
 them
 to
 take
 full


advantage
 and
 be
 the
 most
 competitive


going
 into
 the
 next
 decade.
 Companies


have
 been
 talking
 about
 this
 for


decades.
 Why
 is
 it
 different
 now?


Primarily
 three
 reasons.
 One,
 we
 have
 to


remember
 that
 many
 of
 these
 companies


still
 live
 on
 mainframes,
 decades
 old


mainframes,
 and
 their
 systems
 were


already
 on
 the
 verge
 of
 breaking
 with


the
 scale.
 Two,
 now
 companies
 see
 that


they're
 leaving
 a
 lot
 of
 revenue
 on
 the


table
 [music]
 by
 not
 being
 able
 to
 take


advantage
 of
 AI.
 For
 instance,
 in


insurance,
 [music]
 underwriters


sometimes
 can't
 even
 get
 to
 the
 demand


that
 they
 have
 because
 they're
 not
 able


to
 process
 it
 fast
 enough.
 They
 can't


bring
 in
 the
 documents.
 They
 can't
 scan


them.
 This
 is
 a
 huge
 revenue
 upside
 that


can
 be
 captured
 if
 you
 get
 the
 right


system
 and
 you
 layer
 AI
 on
 top.
 Third,


there
 are
 strong
 [music]
 viable
 options


of
 this
 next
 generation
 of
 AI
 first


software
 built
 by
 entrepreneurs
 [music]


who
 deeply
 understand
 your
 industry,
 are


deeply
 technical,
 and
 have
 entirely


rearchitected
 your
 platforms
 to
 one


enable
 you
 to
 scale
 and
 two
 be


incredibly
 flexible
 in
 terms
 of
 how
 you


can
 add
 AI
 on
 now
 and
 in
 the
 future.
 I


see
 a
 ton
 of
 opportunity
 here
 and


potentially
 a
 dramatic
 reordering
 of
 the


winners
 and
 losers
 of
 incumbent


companies
 based
 on
 who
 become
 the
 early


adopters
 of
 some
 of
 these
 new
 platforms.


And
 we're
 already
 seeing
 it.
 There's


some
 banks
 and
 there's
 some
 insurance


companies
 that
 are
 starting
 to
 get
 the


reputation
 of
 being
 forward
 thinking,


easier
 to
 work
 with,
 wanting
 to
 lean
 in.


And
 those
 companies
 in
 some
 areas
 like


mortgage
 servicing
 have
 been
 able
 to


turn
 areas
 of
 their
 business
 from
 5%


margin
 businesses
 to
 50%
 [music]
 margin


businesses.
 And
 you
 imagine
 doing
 that


across
 your
 company
 as
 quickly
 as


possible.
 It's
 going
 to
 make
 a
 much


bigger
 difference
 against
 your


competitor
 that
 maybe
 takes
 2
 or
 3
 years


to
 catch
 up.
 One
 of
 the
 reasons
 as
 an


investor
 that
 I
 get
 so
 excited
 about


infrastructure
 is
 that
 it's
 [music]


beautiful
 infrastructure
 that
 enables


beautiful
 consumer
 experiences
 and


beautiful
 business
 experiences.


For
 instance,
 why
 does
 your
 bank
 market


products
 to
 you
 that
 you
 already
 have?


It's
 because
 your
 customer
 data
 sits


[music]
 in
 all
 of
 these
 different


sectors.
 Why
 can't
 customer
 service


agent
 A
 answer
 questions
 about
 customer


service
 B
 if
 you
 call
 in
 about
 your


banking
 operations?
 Now,
 imagine
 the


future
 of
 a
 unified
 data
 layer
 and


incredibly
 smart
 people
 supplemented
 by


agents
 that
 can
 understand
 your
 needs,


help
 you
 with
 any
 product
 you
 already


have,
 anticipate
 your
 needs
 in
 the


future.
 That
 would
 be
 a
 beautiful


experience
 for
 both
 customers
 and


businesses.
 In
 2026,
 [music]


we're
 going
 to
 see
 a
 dramatic


acceleration
 for
 any
 company
 that
 has


built
 a
 new
 AI
 first
 platform
 that
 sells


into
 this
 large
 industry.
 But
 the


opportunity
 is
 massive.
 So
 if
 you
 are
 a


founder
 who
 deeply
 understands
 or
 is


deeply
 curious
 about
 any
 archaic
 aspect


of
 banking
 or
 insurance,
 the
 opportunity


is
 now.
 You
 can
 build
 your
 software


faster
 and
 customers
 are
 ready
 to
 buy.
</summary>
</details>

### 动态代理层：颠覆传统记录系统

Sarah Wang 提出，在企业软件领域，“记录系统”（systems of record）正逐渐失去其主导地位。她认为，当AI代理（agents）能够独立执行未明确指示的意图时，被动式的记录系统层将不再合理。她预测，一个全新的“动态代理层”（dynamic agent layer）将出现，它将比传统的记录系统更具优势，并最终取代它们。这一发展是企业智能化进程中的一个重要里程碑。

Wang 强调，她并非轻率地认为记录系统正在失去价值。她曾在一个主要投资于企业资源规划（ERP）和其他记录系统的公司工作，深知这些系统因其“数据引力”（data gravity）而具有极强的粘性。此前的SaaS 2.0浪潮曾试图通过改进用户界面（UI）来挑战记录系统，但大多未能成功。然而，现在情况不同了，因为意图（intent）与执行（execution）之间的距离正在迅速缩短，这不仅带来了20%到50%的用户体验提升，更重要的是，它改变了用户达成目标的方式。

以IT服务管理（ITSM）为例，这曾是**Service Now**等巨头的传统领域。一位IT主管告诉Wang，在其二十年的职业生涯中，他首次相信IT支持将发生根本性变革，并在五年内面貌全非。究其原因，传统的系统在处理诸如申请新软件访问权限等任务时耗时漫长。相比之下，新兴的ITSM代理能够直接接入企业技术栈，使这类请求几乎瞬时完成。得益于**大型语言模型**（Large Language Model: 基于海量文本训练的AI系统）的进步，AI代理现在能够提取用户意图、分类请求类型、映射到已知工作流、识别用户实体，并高效准确地满足用户请求。

Wang 认为，这一新范式包含几个有价值的层面。首先是基础模型层，她相信其价值将持续存在。但更关键的是新兴的代理层，它紧密贴近用户，收集用户数据，理解用户偏好，从而在未来创造价值。基于实际观察，她认为这是新玩家进入市场并获胜的巨大机会。当前产品迭代速度极快（每周甚至每日都在改进），需要快速响应的团队。要实现意图与执行的融合，关键在于提供准确可靠的解决方案，否则用户不会使用，也不会信任AI代理。因此，即使是构建在**Datadog**等经典平台之上的代理，也可能输给**Resolve**或**Traversal**等新兴的AI SRE（Site Reliability Engineering）公司。Wang 对此机遇感到非常兴奋，并预言2026年将是动态代理层超越记录系统的关键年份。

<details>
<summary>Original English</summary>
I'm
 Sarah
 Wang,
 general
 partner
 on
 A16Z


Growth,
 and
 my
 big
 idea
 for
 2026
 is
 that


systems
 of
 record
 start
 to
 lose
 their


edge.
 A
 passive
 system
 of
 record
 layer


stops
 making
 sense
 when
 agents
 can


independently
 execute
 on
 unsigned


intent.
 [music]
 I
 expect
 to
 see
 a
 new


dynamic
 agent
 layer
 that
 actually
 makes


sense
 for
 employees
 to
 replace
 legacy


systems
 of
 record.
 This
 is
 a
 very


exciting
 development
 on
 the
 long
 road
 of


inserting
 intelligence
 into
 companies.
 I


don't
 say
 that
 systems
 of
 record
 are


losing
 privacy
 lightly
 at
 all.
 I
 used
 to


work
 at
 a
 firm
 that
 almost
 exclusively


invested
 in
 ERPs
 and
 other
 systems
 of


record
 because
 of
 the
 stickiness
 of
 the


data
 gravity.
 There
 was
 a
 wave
 of
 SAS


2.0
 that
 was
 wellunded
 and
 tried
 and


failed
 to
 take
 on
 the
 system
 of
 record


mostly
 through
 a
 better
 UI.
 This
 is
 the


first
 time
 that
 we've
 seen
 a
 genuine


threat
 to
 that
 and
 that's
 because
 the


distance
 between
 intent
 and
 execution
 is


collapsing
 and
 that's
 creating
 not
 a
 20


to
 50%
 better
 experience
 for
 the
 user


but
 how
 you
 get
 to
 that
 magical
 TEDex.


Let's
 take
 the
 concrete
 example
 of
 ITSM


IT
 service
 management.
 This
 has


traditionally
 been
 the
 domain
 of


powerhouse
 company
 Service
 Now.
 I


chatted
 with
 a
 head
 of
 IT
 recently
 who


told
 me
 for
 the
 first
 time
 in
 his
 two


decade
 long
 career
 he
 believed
 that
 IT


support
 was
 fundamentally
 going
 to


change.
 It
 will
 look
 completely


different
 in
 5
 [music]
 years.
 So
 why
 is


that?
 If
 you
 think
 about
 the
 way
 that


the
 old
 systems
 work,
 how
 long
 it
 takes


to
 do
 something
 like
 request
 access
 to


new
 software
 in
 the
 firm
 and
 you
 [music]


contrast
 that
 with
 the
 ITSM
 agents
 that


are
 arriving.
 They
 plug
 into
 your
 stack


and
 this
 type
 of
 request
 becomes
 nearly


[music]
 instantaneous.
 Through


advancements
 in
 LLM,
 you
 can
 now
 extract


intent.
 You
 can
 classify
 the
 request


type.
 You
 can
 map
 it
 to
 a
 known


workflow,
 identify
 user
 entities,
 and


the
 request
 from
 the
 user
 becomes


fulfilled
 in
 a
 way
 that
 is
 efficient
 and


accurate.
 So,
 we
 think
 there's
 a
 couple


of
 valuable
 layers
 in
 this
 new
 paradigm.


Of
 course,
 there's
 the
 foundation
 model


layer.
 We
 believe
 that
 stays
 valuable.


Um,
 but
 it's
 really
 the
 emerging
 agent


layer
 that
 sits
 as
 close
 as
 possible
 to


the
 user
 and
 is
 collecting
 data
 on
 that


user,
 understanding
 user
 preferences


that
 we
 think
 acrru
 value
 in
 the
 future.


Based
 on
 everything
 that
 we're
 seeing
 in


the
 wild,
 we
 believe
 this
 is
 a
 huge


opportunity
 for
 new
 players
 to
 come
 in


and
 win.
 [music]
 Why
 is
 that?
 We're
 in
 a


phase
 right
 now
 where
 the
 product
 is


getting
 better
 on
 a
 weekly,
 if
 not


daily,
 basis,
 and
 you
 need
 teams
 that


move
 fast.
 If
 you're
 going
 to
 collapse


[music]
 intent
 and
 execution,
 what


bridges
 that
 is
 actually
 having
 an


accurate
 or
 reliable
 [music]
 solution


for
 your
 customer.
 Otherwise,
 they're


not
 going
 to
 use
 it.
 They're
 not
 going


to
 trust
 the
 agent
 that
 you're
 building.


That's
 why
 we're
 [music]
 starting
 to
 see


even
 agents
 built
 on
 top
 of
 classic


iconic
 platforms
 like
 Data
 Dog
 lose
 to


some
 of the
 new
 AI
 SRE
 companies
 like
 a


a
 Resolve
 or
 a
 Traversal.
 We're


extremely
 excited
 about
 this
 opportunity


and
 2026
 is
 going
 to
 be
 the
 year
 that


the
 dynamic
 agent
 layer
 overtakes
 the


system
 of
 record.
</summary>
</details>