Microsoft just made its biggest move yet
to prove it can compete at the AI
frontier without leaning on open AI. For
years, Microsoft's AI empire was built
around other people's models. It poured
billions into open AI, brought those
models into Azure, pushed C-Pilot
everywhere, and later backed Anthropic
2. But at Build 2026 in San Francisco,
Microsoft changed the tone completely.
It revealed seven in-house AI models, a
new agent stack, an always personal
agent called Scout, the Microsoft IQ
intelligence layer, an AI security
system for developers, and a major
quantum chip upgrade with Myerana 2.
This was Microsoft drawing a line, its
own models, its own agents, its own
intelligence layer, and eventually its
own quantum path. The headline model is
MAI thinking one Microsoft's first
reasoning model trained from scratch.
According to Microsoft, it was trained
on clean commerciallylicicensed data
without distillation from third party
systems. That detail matters a lot
because many companies are now trying to
avoid legal and business risks around
training data, model copying, and
dependence on another company's frontier
models. MAI Thinking One is described as
a midsized reasoning model with 35
billion active parameters. Some
reporting lists its context window at
256,000
tokens, while Microsoft's developer
coverage also describes it with a
128,000 token context window. So, the
exact public framing seems to vary
depending on the release. And Microsoft
is already making some pretty aggressive
performance claims. Mustafa Sullean, the
chief executive of Microsoft AI, said
that after tuning its models for
consulting firm McKenzie, Microsoft
outperformed OpenAI's GPT 5.5 on quality
while projecting around 10 times better
cost efficiency based on public pricing
data scaled across model sizes. That is
a huge statement because Microsoft is
basically saying that its own model
stack can compete with the companies it
invested in while also being much
cheaper to run. Satya Nadella framed it
very directly at the conference saying
the time has come for every company to
move from just consuming a frontier
model to fully participating at the
frontier. And that line basically sums
up Microsoft's bigger strategy here. It
does not want to only rent intelligence
from open AI and anthropic forever. It
wants to build intelligence, host it,
sell it, optimize it, and control the
economics of it inside Azure. The money
side explains a lot here. Every time
Microsoft uses thirdparty models, part
of the cost goes to outside providers
with its own models running on Azure,
Microsoft controls the whole stack. That
means lower costs, better margins, and
cheaper AI tools for developers. Now,
Microsoft also launched MAI code 1
Flash, a coding model designed to
convert text descriptions into source
code for apps and websites. This model
is rolling out across GitHub, Copilot,
and Visual Studio Code, which means
Microsoft is not just releasing a model
into some isolated lab environment. It
is putting it directly into the
developer tools millions of people
already use. On the coding benchmark
side, Microsoft says MI thinking 1 was
preferred over Anthropics Claude Sonnet
4.6 in blind evaluations run by Serge,
an independent human rating partner. The
company also says it matches Claude Opus
4.6 on coding benchmarks including
SWEBench Pro. According to developer
coverage, the model is available now in
private preview on Microsoft AI foundry
and Microsoft also released a flash
version designed for speed and
efficiency. So the strategy is familiar,
one stronger reasoning model for heavier
work and smaller or faster variants for
tasks where cost and speed matter more.
The seven model family also expands
beyond text encoding. Microsoft
announced MAI image 2.5 and a flash
variant supporting textto image and
imageto image generation. That means
users can describe what they want in
plain language or pass in sketches and
visuals to guide the generation. The
model is already live in PowerPoint and
is rolling out on one drive. Then there
is MAI transcribe 1.5 which supports
high accuracy transcription across 43
languages with streaming coming soon.
Microsoft also introduced MAI voice 2
and its flash variant available in more
than 15 additional languages and capable
of producing new voice options. Add MAI
code 1 for GitHub, Copilot, and VS Code
and the picture becomes clear. Microsoft
is building a broad AI model layer for
office work, software development,
voice, images, transcription, reasoning,
and agents. Now, you've probably noticed
how much attention Claude is getting
right now. Anthropic keeps adding new
models and features from Claude code and
Claude artifacts to skills, connectors,
design tools, and more. And honestly, it
makes sense. Claude has become one of
the most useful AI tools for turning an
idea into something real. Whether that
means building an app, creating a
presentation, organizing research,
planning your week, or speeding up work
that would normally take a whole team.
The problem is that a lot of people keep
saying learn without actually showing
you a clear way to use it properly.
That's why today's sponsor is hosting
the world's first Claude Aathon, a
two-day live workshop happening this
weekend from 10:00 a.m. to 700 p.m.
Eastern time. It's a deep dive into
Claude, practical use cases, and more
than 10 other AI tools, and they're
opening 1,000 free seats for a limited
time. Inside the workshop, you'll learn
how to use Claude for deep research,
build artifacts and dashboards, create
full presentations, set up connectors
like Indeed for job search, build custom
GPTs and agents, and use AI tools for
visuals, videos, and automation. You'll
also get bonus resources like claude
codes, a prompt library, and a
personalized AI toolkit builder. Link is
in the description or scan the QR code
to join before the free seats close. All
right, now back to the video. And that
brings us to Microsoft IQ. Microsoft IQ
is now generally available and it is
meant to be the unified intelligence
layer that makes copilot and AI agents
more aware of the actual organization
they are working inside. The goal is to
move agents away from generic chatbot
behavior and connect them to business
context, company data, and internal
logic so they hallucinate less and act
with more useful grounding. Inside
Microsoft IQ, there are several parts.
Work IQ captures how users work inside
Microsoft 365. It understands people,
emails, documents, meetings,
organizational systems, external
sources, and the relationships between
them. Work IQ APIs are set to become
available on June 16th, giving agents
direct access to that kind of work data.
Fabric IQ runs on Microsoft Fabric and
works as a semantic foundation for
structured business data. Microsoft
describes it almost like an ontology,
meaning it gives business data a more
organized meaning layer. Foundry IQ then
handles unstructured information,
pulling from documents like wikis,
policies, contracts, and even the live
web. Microsoft also added web IQ, a new
member of this family. Webq gives agents
real world grounding through web search.
It is model agnostic and native to the
model context protocol which matters
because MCP is becoming one of the main
ways agents connect to external tools
and data sources. Microsoft says web IQ
returns relevant information blocks
nearly two and a half times faster than
the next best alternative. So Microsoft
is building this stack from multiple
angles. It has the models, it has the
work context, it has the business data
foundation, it has document retrieval
and it has web grounding. Then it
connects all of that to agents. One of
the more interesting announcements here
is Microsoft Scout, the company's first
autopilot agent. Microsoft describes
autopilots as a new category of always
on agents. These agents stay active in
the background, work autonomously, have
their own identity, and act on your
behalf under the permissions and
policies you or your organization set.
That own identity part is important.
Scout does not operate as some anonymous
shared service account. Every agent
works under its own governed entra
identity which means the actions it
takes can be traced back to a known
actor inside the company directory. Its
credentials are scoped to the task,
protected end to end, redacted from logs
and diagnostics and managed like a
firstparty Microsoft service. Scout is
integrated across Microsoft 365 apps
like Teams, Outlook, One Drive and
Sharepoint. It connects to chats, email,
calendar, contacts, documents, browser
resources, local desktop resources, and
MCP servers. You interact with it
through Teams, and the desktop app
extends its reach into the browser and
local system. The practical idea is
simple. Scout handles coordination work
that normally piles up throughout the
day. It can proactively schedule and
coordinate meeting times across time
zones, flag important meetings, generate
prep materials, identify upcoming
deliverables, automatically block
calendar time, and spot risks such as
stall decisions before they become
bigger blockers. Microsoft says Scout
learns over time through work IQ,
building context around how you work,
what you care about, and what needs to
happen next. The company's own employees
have already been using an early desktop
experience and Microsoft is now
expanding it to a select group of
customers in private preview and
Frontier organizations. Access requires
Frontier enrollment intoune policy
configuration and an opt-in at astation.
Users with a GitHub copilot license can
then download and install the
experience. Scout is also built on
OpenClaw, the open- source technology
that showed up in November 2025 as a way
to give agents a kind of always on
operating rhythm. Microsoft is
contributing policy conformance upstream
to Open Claw so organizations running it
can validate whether their environment
meets security and compliance
requirements and get an audit ready
answer. And Microsoft is clearly aware
that always agents raise serious
enterprise security questions. Scout can
only access resources and destinations
that have been approved. Sensitive
actions can require human signoff.
Microsoft Purview policies, including
sensitivity labels and data loss
prevention rules, are enforced before
anything is sent or written. So, the
pitch is that Scout can act autonomously
while still staying inside the
organization's identity, access,
security, and compliance structure. For
developers, Microsoft also introduced
code name Mdash, which is a pretty funny
name because AI generated text is often
full of M dashes. Under the name though,
the product is serious. It is a
multimodel agentic security system that
deploys more than 100 agents to search
for exploitable bugs in code. These
agents reason about data flows, business
logic, exploit chains, and contextaware
fixes inside the developer portal. So,
Microsoft is not just using agents for
office work. It is also applying them to
software security where a swarm of
specialized agents can review code from
different angles and try to find
problems that a normal static scanner
might miss. Then, Microsoft made another
big announcement that moves outside
normal AI software, Majorana 2. Majorana
2 is Microsoft's next generation
topological quantum chip and the company
says it was developed with help from
Microsoft Discoveries Agentic AI.
Microsoft claims the chip is 1,000 times
more reliable than its previous
generation of cubits. Its mean cubit
lifetime is now 20 seconds with some
instances lasting as long as 1 minute.
That is a massive jump in quantum terms.
Cubits are extremely fragile. Tiny
changes in temperature, vibration, or
environmental noise can knock them out
of their quantum state. Many common
approaches measure cubit lifetime in
microsconds or milliseconds. Microsoft
is saying Majorana 2 can hold its state
for 20 seconds on average. The company
compared the improvement to inventing a
phone battery that would go from dying
in a day to lasting nearly 3 years on a
single charge. The analogy is dramatic
yet it gives a decent sense of the scale
Microsoft is claiming. Majorana 2
currently has just 12 cubits while a
useful quantum computer would require
millions. Still, Microsoft says the
combination of reliability, 1 microcond
operations, and very small cubit size
around 1/100th of a millimeter puts it
on a path toward a commercially valuable
scalable quantum computer by 2029. Zulfi
Alam, corporate vice president of
Microsoft Quantum, said they expect to
have a quantum machine in 2029 that can
solve commercially viable reasonable
problems. Microsoft's approach is based
on topological cubits and Myurana based
physics tied to a quasi particle first
theorized in the 1930s by Italian
physicist attoriana.
This path has been controversial.
Microsoft previously had to retract a
2018 Nature paper that claimed evidence
for the particle, and the new chip and
supporting research have not yet been
peer-reviewed. Some physicists are
asking for more information, so this is
still an area where Microsoft's claims
will face heavy scrutiny. The technical
change behind Majorana 2 also includes a
new material stack. Majora 1 used
aluminum as a superconductor, while
Majorana 2 uses lead. In this context,
lead helps shield fragile cubits from
cosmic disturbances that can make them
unstable. Cetton Nyak, a Microsoft
technical fellow, said the team needs to
keep improving each year to reach a
computer with major commercial and
societal value and compared their
progress to being 1,000 times better
than last year. Microsoft Discovery is a
big part of this story as well. The
platform is now generally available and
lets companies use teams of AI agents
for frontier R and D. These agents can
search, research, reason through
problems, generate hypotheses, optimize
experiments, and help validate ideas
while human experts stay in control.
Microsoft's own quantum team is already
using it to manage workflows, automate
measurements, improve fabrication, find
hidden flaws, and propose better
solutions. That is a big deal because
the project has nearly two decades of
data spread across different teams,
formats, and systems. AI agents can
connect that information, spot patterns,
and track how software, chip design,
materials, fabrication, physics, and
measurements all affect each other. They
also speed up experiments. Creating a
topological quantum state means
adjusting hundreds of parameters, and
manual measurement can take weeks.
Microsoft says Discovery helped build an
AI agent that cut this cycle by orders
of magnitude, adjusting voltages in
parallel and mapping conditions
continuously. One agent even found an
unccalibrated temperature sensor hidden
in fabrication data. And this all
arrives at a very interesting moment in
the broader AI market. Open AAI and
Anthropic are both moving toward massive
IPOs, while Microsoft is tied to both.
It committed $13 billion to OpenAI,
invested up to $5 billion in Anthropic,
and sells both companies models through
Azure. Now, Microsoft is their investor,
partner, distributor, infrastructure
provider, and direct competitor. That is
why build 2026 feels bigger than a
developer event. Microsoft wants more
control over the AI stack from models
and agents to developer tools,
enterprise data, research platforms, and
eventually quantum hardware. So now the
real question is how fast Microsoft can
turn all of this into products people
actually use every day and how much
pressure this puts on OpenAI, Anthropic,
Google, and every other company trying
to own the next layer of AI.