Flash Teams as the Future of Work: A Conversation with Dr. Melissa Valentine and Dr. Michael Bernstein
2/16/26, 13:20 Flash Teams as the Future of Work: A Conversation with Dr. Melissa Valentine and Dr. Michael Bernstein
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Resources Research Flash Teams as the Future of Work: A Conversation with Dr. Melissa Valentine and Dr. Michael Bernstein
Flash Teams as the Future of
Work: A Conversation with Dr.
Melissa Valentine and Dr.
Michael Bernstein
Dec 10, 2025
By Dr. Melissa Valentine, Dr. Michael Bernstein, Teng Liu, Allie Blaising and
Gabby Burlacu
As AI innovation accelerates and organizations infuse AI tools into
everything from product development to customer experience, many
traditional work structures– foundational to how companies hire, develop
and reward– have proven too slow for today’s rapid pace of change. One
of those traditional structures is teams.
Decades of research inform effective team development and
management. This is partly because it’s so easy for things to go wrong:
ineffective processes, confusing hierarchies, unclear decision-making
authority, and role-based limitations have long been common experiences
when working in a team. All of these create drag in a world where speed is
expected to accompany quality.
Dr. Melissa Valentine, a tenured Associate Professor in the Department of
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2/16/26, 13:20 Flash Teams as the Future of Work: A Conversation with Dr. Melissa Valentine and Dr. Michael Bernstein
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Management Science and Engineering at Stanford University, and Dr.
Michael Bernstein, Professor of Computer Science at Stanford University
and Bass University Fellow, both hold the title of Senior Fellow at the
Stanford Institute for Human-Centered Artificial Intelligence (HAI). They
recently joined us to discuss their new book presenting compelling
research on flash teams: groups of (often independent) experts who come
together quickly to achieve a specific objective, then dissolve when the
work is complete.
Our conversation is part of Upwork’s Reimagining Work, which is a lecture
series designed to provide a forum for expert practitioners and
academics to foster the exchange of views on the present and future of
work. During the discussion, Drs. Valentine and Bernstein shared what
their research has taught them about the creation of flash teams, and why
the rapid forming-focus-disband cycles inherent in flash teams are (1)
more possible than ever and (2) the right fit for the demands of modern
work.
1. Gabby: How did you first become interested in studying flash
teams, and what made you decide to write a book about them?
Michael: I had spent years working on research on crowdsourcing within
the computer science field. Everyone seemed to think that crowdsourcing
was about micro tasks like on Amazon Mechanical Turk or Prolific. This
got me incredibly frustrated, both because I felt like it was often work that
was less interesting for workers than it could be, but also because I felt
like it was fundamentally limiting the kind of work that could be done. So I
started this research on Flash Teams as a way to prove a point that
computation, and what we'd otherwise call crowdsourcing, is actually a
much broader phenomenon that is about the computational mediation of
all kinds of work, not just microtasks. And at the time, I started connecting
with a platform—what was then called oDesk, now called Upwork—as a
platform that would help me realize those ideas.
Melissa: I first became interested in flash teams years ago as a PhD
student, when I studied how emergency departments were redesigning
their work structures so that clinicians who had never worked together
could still coordinate effectively. When I later presented that research at
Stanford, one of Michael Bernstein’s students immediately saw the
connection to the coordination challenges in crowdsourcing—bringing
together experts who had never met and getting high-quality work from
them fast. That’s what sparked our collaboration and the early research
on flash teams.
We decided to write the book because we kept meeting smart, capable
people who had
never heard that this was even possible. I once helped
someone solve a painful software-integration problem in a couple of days
using a flash team, and she said, “Why have you never told me about this?”
A CEO at a leadership retreat reacted the same way: “This is amazing but
I’d have no idea how to do it.”
Those moments made it clear that the capabilities we see every day at
Stanford, including dynamic expertise, global talent pools, AI-supported
coordination, weren’t yet accessible to most leaders. The book is our way
of opening that world up and giving people a practical guide for how to
use flash teams in their own organizations.
2. Allie: Much of the existing teams research focuses on human
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performance drivers that develop over time - such as cohesion,
trust, and shared identity. What does your research suggest about
sustaining motivation and satisfaction when AI + human teams
form and disband rapidly?
Michael: The research points us at other kinds of temporary organizations
—groups like disaster response teams, ER crews, film crews—as the
examples for how we organize project-based work. There are strategies
that these kinds of organizations use for management, for motivation, for
fair payment, all providing inspiration for how we can envision a future of
work with flash teams.
Melissa: Our research shows that in teams that form and disband quickly,
motivation and satisfaction don’t come from long-term cohesion, instead
they come from designing the work so people can contribute meaningfully
right away. In fast-cycle settings like ER crews, data-collection pilots, and
flash teams, we consistently see that motivation is sustained when three
conditions are in place: rapid role clarity, small but concrete signals that
the work is being done ethically and well, and immediate visibility of
impact. Even modest design moves, for example clear handoffs,
transparent consent language, lightweight governance structures, or
quick feedback loops, can give people confidence that they know what to
do, that the work is responsible, and that their contributions matter. In
temporary teams, the work itself becomes the source of meaning and
motivation.
3. Ted: What design principles feel most important when designing
for both efficiency and belonging in AI-enhanced flash team
structures?
Michael: It all comes down to the human element. If you optimize for just
one of those two objectives, you're going to get something that will not
work. You ultimately are looking for something that jointly maximizes both
objectives. There's no magical design principle here, but ultimately you
have to benchmark each system or intervention against those two
objectives: How is this change impacting performance? How is this
change impacting belonging?
Melissa: I see the same need to hold both goals at once. In practice, the
design choices that matter are the ones that create smoother
coordination
and reinforce people’s sense of agency and contribution. In
AI-enhanced flash teams, that often means structuring rapid role clarity,
transparent handoffs, and visible impact so that AI accelerates the work
without sidelining anyone. The systems that work best reduce friction,
keep expertise legible, and make it easy for people to understand how
their actions fit into the whole, which is what sustains both efficiency and
belonging.
4. Gabby: What are the potential drawbacks of too much structure in
a flash team?
Michael: We actually wrote a whole paper about that! If you put too much
structure into a Flash team, then the team can't turn on a dime. In other
words, an over-structured team is doomed to follow exactly the process
that you laid out for it, whether or not that's the right process. We would
see predefined Flash teams continue on their track, creating more and
more compounding problems for each other with no way to fix it. That's
why successful Flash teams need to have the ability for the team to
propose changes to their own structures, to change the tasks, the goals,
the personnel, and to have a hierarchy that can review and approve those
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changes. Without that, you're limited to only tasks that are so simple that
you can carefully predefine every single step without worrying that it will
go off the rails.
Melissa: Too much structure can lock a flash team into a workflow that no
longer fits the problem. I’ve seen teams keep executing a scripted
process even as the work shifted around them, simply because there was
no built-in way to revise the plan. When structure becomes rigid, small
mismatches compound into real coordination failures. The safeguard is to
pair structure with lightweight mechanisms for proposing and approving
changes to tasks, roles, or goals. In fast-moving flash teams, structure
should orient the work, but there need to be tools that enable people to
constantly update and realign the structure.
5. Allie: How do we balance individuals' desire for control over their
reputation in a team when AI agents become part of the flash team
loop?
Michael: I think that many enterprising workers are already attempting to
integrate AI agents as part of their work. The smart ones are realizing that
sometimes these things slow them down more than they speed them up,
or are figuring out exactly what kinds of work these agents can actually
accelerate. Ultimately, in every project, there is going to be someone who
is responsible for the ultimate work product. And it's going to be that
person whose reputation will be on the line if the AI agent makes a
mistake. So ultimately, I think the biggest challenge is making sure people
are calibrated in when and how to use these agents.
Melissa: Like Michael said, the challenge is calibration. People are
learning quickly where AI helps and where it introduces risk. No matter
what AI contributes, responsibility still lies with the human expert, so
judgment becomes critical. The opportunity is that workers who use AI
thoughtfully can extend their capabilities without compromising their
professional reputation.
6. Ted: What open questions are you most excited about as AI begins
to provide an intelligence layer to how teams form, coordinate, and
evolve?
Melissa: I’m especially energized by how this moment aligns with the “AI +
Organizations” Grand Challenge that we are organizing with leaders at
Google DeepMind. Together, we’ve issued a call to build the basic
discipline of organizational design for the age of AI. This frame
recognizes the need to move beyond individual use of AI tools toward
systems that make teams and organizations collectively more intelligent.
The big opportunity is to rethink how we coordinate, lead, and structure
work when AI becomes an intelligence layer in organizational design. We
have the chance to design human–AI teaming systems that elevate our
collective intelligence and value.
Michael: At the end of the day, for me, it's most exciting to think about
what kinds of collaboration and coordination we could do better than we
do today. So many of our most ambitious goals are managed completely
based on experience and intuition about how we ought to be working
together. Could AI help us make better decisions? Could it help us foresee
when there are going to be issues? I think that if it could do that, it could
make our work lives happier, healthier, more fulfilling.
To learn more about flash teams, check out Flash Teams: Leading the
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Future of AI-Enhanced, On-Demand Work.
About Dr. Melissa Valentine
Stanford Professor Melissa Valentine is a leading expert on technology
and business. Her research delves into the evolving interplay between
artificial intelligence, algorithms, and organizational design. Through in-
depth field studies, she explores how technology reshapes workplaces,
providing valuable insights into the future of work in a digital and
specialized world. Her award-winning research has been featured in The
New York Times, The Wall Stre
et Journal, Harvard Business Review,
Wired, Fast Company, and the Financial Times.
About Dr. Michael Bernstein
Michael Bernstein is an Associate Professor of Computer Science at
Stanford University, where he is a Bass University Fellow, Senior Fellow at
the Stanford Institute for Human-Centered Artificial Intelligence, and
Interim Director of the Symbolic Systems Program. His research focuses
on designing social, societal, and interactive technologies. His research
has been reported in venues such as The New York Times, TED AI, and
MIT Technology Review, and Michael himself has been recognized with an
Alfred P. Sloan Fellowship, the UIST Lasting Impact Award, and the
Computer History Museum's Patrick J. McGovern Tech for Humanity
Prize. Michael holds a bachelor's degree in Symbolic Systems from
Stanford University, as well as a master's degree and a Ph.D. in Computer
Science from MIT.
About Allie Blaising
Allie Blaising is a Lead User Experience Researcher at Upwork, where she
leads customer research that shapes design and business decisions
across multiple verticals, with a recent focus on Small Business Growth
and Generative AI product initiatives. She holds a Master’s in Science
from University of Pennsylvania.
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