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In partnership with
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In This Edition:
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AI Creates More Jobs
-
How VCs Really Make Money
-
Bay Area Dominates Global
AI Funding
-
How Prompt Politeness
Affects LLM Accuracy
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Walmart x OpenAI
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AI Creates
More Jobs
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AI Won’t Take
Your Job — It
Will Transform
It
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Will AI wipe
out jobs — or
create more of
them?
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You’ve heard the
doomers: mass
unemployment, 20%
jobless rates,
half of all
white-collar jobs
gone in five
years. You’ve
heard the
dismissers too:
“AI is just hype,
not real
AGI.”
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Both are
wrong.
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History shows us
something
different: every
time technology
makes work
cheaper, demand
explodes.
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Containerization
(1960s):
Dock jobs
fell, but
global trade
boomed —
birthing
trillion-dollar
logistics
empires.
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Cloud
Computing
(2010s):
Servers
vanished,
but DevOps
engineers
and cloud
architects
became
indispensable.
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Radiologists
(2020s):
Predicted to
disappear.
Instead? AI
made scans
cheaper,
demand
soared, and
radiologists
are more
needed than
ever.
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This is the Jevons
Paradox
at play:
efficiency doesn’t
kill work — it
reveals massive latent
demand.
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AI won’t erase
jobs. It will refactor
them: automating rote
tasks, while
humans take on
higher-value work.
Already, YC-backed
startups like Aoka
(AI sales agents)
and Tenor
(healthcare
paperwork
automation) are
freeing people for
more impactful
roles.
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The real question
isn’t whether
AI will take jobs.
It’s whether
you’ll be the
founder or builder
who sees the
opportunity to
create the next
wave of
work.
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Watch the
full breakdown
here — and see
how founders
can seize the
AI
shift.
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How VCs
Really
Make Money
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How VCs Really
Get Rich
(Founders Should
Know This)
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It’s not just
about big checks
and bold bets —
there’s a hidden
economic engine
behind every
venture fund. From
LPs and GPs to
management fees
and carried
interest,
understanding VC
mechanics can
change how you
view startups,
investments, and
exits.
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This week’s
feature breaks
down venture capital
economics
in the simplest
way possible —
perfect for founders,
operators, and
aspiring
investors.
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Key Insights
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The Power
Law
Play:
Out of 20
bets, most
crash—but
one breakout
200x win can
repay the
entire fund
(and
more).
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LPs vs
GPs:
The silent
backers
(LPs) fund
the show,
while the
dealmakers
(GPs) call
the
shots—splitting
profits only
after LPs
get their
money back,
with GPs
pocketing up
to 30% of
the
upside.
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Fees
&
Carry
Secrets:
Those 2–3%
annual fees
keep the
lights
on—but the real
fortune
lies in
carried
interest,
where
performance,
not
promises,
pays off
big.
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For founders,
operators, and
emerging
investors,
mastering VC
economics is key
to understanding
what drives
funding
decisions.
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Read the
full
breakdown
&
explore the
course
here.
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Bay Area
Dominates
Global AI
Funding
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The Next
Silicon Valley?
Still Silicon
Valley
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Despite
predictions of its
decline, the Bay
Area continues to
dominate global
startup
innovation. Data
shared during SF Tech Week by
a16z
reveals that
Silicon Valley’s
ecosystem not only
endures—it’s
accelerating its
lead in funding,
talent, and
startup success
rates.
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Key
Insights:
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Startup
Density:
1 in 4
top-decile
U.S.
startups—and
1 in 8
globally—operate
within 50
miles of the
Bay
Area.
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Funding
Surge:
The region
now captures 58% of
all U.S.
startup
funding in
2025, up
sharply from
35% in
2021.
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Momentum
Loop:
AI-driven
exits are
creating a
flywheel—successful
founders
reinvest,
launch new
ventures,
and attract
more
capital.
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Talent
Magnet:
The Bay
remains the
top
destination
for top-tier
founders,
engineers,
and
operators,
fueled by
proximity to
Big Tech and
a
world-class
innovation
infrastructure.
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For investors, it’s where the
best returns are
found. For founders, it’s where
success odds
multiply.
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Explore the full
analysis to see
why Silicon
Valley
remains the
world’s
startup
capital—and
why it’s not
slowing down
anytime
soon.
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How Prompt
Politeness
Affects LLM
Accuracy
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⚡Founders: This
Simple Prompting
Trick Boosts
Accuracy
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A new
study
reveals a
surprising twist
in how large
language models
respond to human
tone. Contrary to
expectations, aggressive or
direct
prompts
appear to improve
AI
accuracy—especially
in advanced models
like ChatGPT-4o.
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Key
Insights:
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Study
Scope:
Researchers
tested 250
prompts
across math,
science, and
history—each
repeated 10
times using
varying
tones.
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⚡ Results:
“Very rude”
prompts
achieved 84.8%
accuracy,
outperforming
“very
polite” ones
at 80.8%.
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Model
Behavior:
Earlier
systems like
GPT-3.5
showed no
tone-based
variation—suggesting
the effect
is unique to
newer
architectures.
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Interpretation:
The
improvement
may stem
from how AI
models
interpret urgency
and
assertiveness
in user
intent.
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This discovery
challenges
assumptions about
how humans should
communicate with
AI—hinting that
tone could shape
performance.
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Download
the full
report
and explore
expert
resources on
effective
prompting
at here
.
️
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How Prompt
Politeness
Affects LLM
Accuracy
336.52 KB •
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