Imagine waking up tomorrow morning. beat. The
sun is just rising outside your window. And your
AI didn't just answer some questions overnight.
It actually ran your entire business while you
slept. It negotiated contracts, right? And optimized
your campaigns. Exactly. Welcome to today's deep
dive. We are looking at a monumental industry
shift today. We've curated some truly fascinating
sources for you. The landscape is just changing
incredibly fast right now. Yeah, it really is.
So here's our roadmap for today. We are tracking
an entirely new direction for AI. First, we explore
OpenAI's major pivot at the Cannes Festival.
They are becoming a full -fledged ad tech powerhouse.
Oh, yeah. Massive move. Next, we cover the absolute
death of prompting. We look at the rise of loop
engineering instead. Right. And the ecosystem
updates supporting those autonomous agents. Exactly.
And finally, we unpack a breakthrough in financial
data. It completely changes how AI reads complex
SEC filings. But I have a vulnerable admission
before we begin. Okay, let's hear it. I still
wrestle with prompt drift myself. You know, you
write a prompt, but you just lose the underlying
logic. Well, we all do. I mean, prompting is
inherently messy. It's unpredictable. It's exactly
why the industry is moving past it. Yeah. So
let's start right at the commercial peak today.
To understand where AI is actually going next,
we have to look at how it makes money. Always
follow the capital. It reveals the true infrastructure.
Right. So OpenAI went to the Cannes Lions Festival
recently, and the narrative around them has completely
shifted this year. They aren't just a quirky
tech lab anymore. They are arriving as a media
and advertising company. Which is huge. Credio
actually invited them to the Cannes Festival.
That is a major signal to traditional advertising
executives. Just days before Cannes, they made
a huge move. They expanded their self -serve
ads manager in the UK. Yeah. They officially
rolled out cost - per click bidding. Cost per
click means advertisers pay only when users actually
click. It is the exact model that built modern
search. And now it is being applied to conversational
AI interfaces. Exactly. And OpenAI's chief revenue
officer headlined some core sessions there. The
main topic was advertising in the age of AI.
Their core strategy here is becoming incredibly
obvious. I mean, they are building infrastructure
to monetize their massive traffic. ChatGPT generates
billions of interactions every single month.
Yeah, the scale is wild. And agencies are demanding
something entirely different these days. They
aren't just buying standalone AI models anymore.
They want end -to -end marketing operating systems.
Right. They want systems that run, orchestrate,
and optimize operations. They need an engine
that handles... So let's unpack what an end -to
-end system actually means. It doesn't just write
the ad copy for you. No, not at all. It targets
the user, bids for the placement, and deploys
it. It monitors the analytics and adjusts the
spend automatically. It essentially replaces
an entire floor of media buyers. Yeah. OpenAI
is using Khan to prove a major point. They want
to be the commercial backbone for major brands.
It's like they stopped selling the engine entirely.
They started selling the whole highway system
instead. That's a perfect structural analogy.
They were providing the roads, the signs, and
the destination. There was also some initial
buzz about a film project. An AI -assisted animated
film called Critters was supposed to debut. Right,
but that project reportedly stalled out recently.
It was tied to the shutdown of Soar's consumer
tool. It really shows where their true priorities
are right now. Consumer video tools are flashy
but highly expensive. The real story to watch
is their ad tech infrastructure. But let me ask
you this. Is this just about monetizing chat
GPT traffic or s***? something bigger oh it is
definitely something much bigger than just chat
traffic they're building the fundamental commercial
backbone for brands. They want to intermediate
every single transaction on the Internet. So
they're becoming the operating system, not just
the tool. Exactly. And that leads us directly
to how that system runs. Right. If OpenAI is
building this comprehensive marketing operating
system, how does that system actually function
in the real world? It requires AI that doesn't
wait for human commands. It requires agents that
can think and act continuously. And that brings
us to the agentic shift. We are moving away from
traditional prompt engineering completely. Thank
goodness. Yeah. Now, loop engineering is the
new standard in the industry. Loop engineering
means AI agents that keep working autonomously
until a goal is met. Forrest Cherney from Claude
Code recently highlighted this shift. The team
over at OpenClaw is seeing it too. Let's clearly
define how this actually works. You give them
a goal, and they just loop until finished. Yeah,
they evaluate the problem, take an action, and
check their work. Then they loop again until
the objective is fully met. Like Moonshot AI,
they just added goal mode to Kimi work. It is
a desktop agent that tracks progress continuously.
Right. You set one objective and the agent just
keeps grinding. You can check its progress anytime
you want. It is a fundamental shift in how we
interact with software. You aren't typing back
and forth anymore. You are managing a persistent
digital worker. But running these persistent
agents requires serious computing power. You
can't just leave a laptop running constantly.
No, you really can't. That is where new tools
like Agent 37 come in. Agent 37 offers managed
hosting for persistent AI agents. It supports
popular agents like Hermes, OpenClaw, and CloudCode.
So you don't need to run them on a Mac mini.
They live in the cloud and work around the clock.
We are also seeing tools like Atomic Male Agentic
emerge. It gives autonomous AI agents their own
real inbox. Right. They manage it fully without
human setup or ongoing intervention. They literally
read the incoming emails, graph responses, and
send them off. I have to push back on that specific
idea, though. Handing over autonomous control
of a real business inbox. Yeah. Letting an AI
reply to sensitive emails without supervision.
Right. Beat. That feels like a corporate disaster
waiting to happen. It is a very valid fear to
have right now. But the technology is adapting
quickly to address that exact risk. That is exactly
why tools like Backgrind are gaining traction.
Right. Backgrind runs your AI agents over any
app or game. But it only pings you when it actually
needs a decision. Right. And that is the crucial
safety mechanism we need. It handles the mundane
routing and basic formatting tasks. But it escalates
the high stakes choices directly to a human.
We're also seeing this autonomy inside our daily
documents. Grok for Word now generates outlines
right inside the panel. Oh, wow. It can restructure
wording and create complex tables autonomously.
It doesn't wait for you to highlight and click
format. And Cloudback MCP connects to clients
like Cursor or VS Code. It manages 300 backup
definitions through simple chat commands. So
what happens when an agent gets stuck in a loop
trying to solve a problem? Well, new tools are
being built specifically to handle agent friction.
When they get suck, they trigger a decision ping
to you. They outline the specific roadblock and
ask for your human intuition. Got it. Human oversight
shifts from micromanaging to just approving decisions.
Exactly. You become the senior editor, not the
junior writer. Right. So to make these continuous
loops run smoothly, things must change. The underlying
foundation models need a major structural upgrade.
They definitely do. They need more speed, larger
memory, and better debugging tools. You cannot
run persistent loops on slow, forgetful foundation
models. They will just lose track of the original
goal completely. Well, rumors about Cloud Sound
at 5 are really heating up now. It is currently
codenamed Fennec inside the development circles.
Word is it could launch as early as next week.
The hardware specs leaking out are highly impressive.
Yeah. It reportedly features a 1 million token
context window. Tokens are tiny chunks of words
AI uses to read and write. Right. And a context
window is the AI's active short -term memory
limit. It is supposed to have vastly stronger
coding capabilities, too. A million tokens means
it remembers massive amounts of data. It can
hold hundreds of textbooks in its active memory.
We are also seeing Grok build remote showing
up online. It looks unfinished and was likely
leaked by accident. Yeah, it is unclear if it
lives on. folds into cursor cursor is a wildly
popular ai code editor right now a grok integration
would disrupt that market significantly definitely
there is also big money moving in the debugging
space elastic is buying an ai debugging startup
called deductive ai the deal is reportedly worth
up to 85 million dollars deductive ai is a startup
from 2023 with solid revenue yeah they've reached
about 1 million in annual recurring revenue already
This will massively boost Elastic's AI observability
tools. When an agent hallucinates, you need to
know exactly why. You have to trace the error
back to its source. It's like buying an MRI machine
specifically to diagnose where the AI went wrong.
You need to see the exact layer inside the black
box. That is exactly what observability tools
provide for developers today. They map out the
AI's internal logic pathways clearly. We are
also seeing fascinating Y Combinator -backed
startups emerge. Palmier just unveiled an entirely
new video editor tool. Oh, yeah. Cloud or Codex
can directly edit videos inside the software.
It works seamlessly with Sedans 2 .0 and Cling
D3. And it is completely free to download right
now. That strategy really drives rapid user adoption
in competitive markets. There are some quick
hits for your toolbox today, too. Microsoft opened
a free 12 -week AI course online. It has 24 lessons
covering classic AI concepts thoroughly. It dives
into deep learning, neural networks, and modern
architectures. It is an excellent resource for
anyone trying to catch up. Stanford also released
a brilliant four part prompting technique. If
your chat bot gives shallow answers, try this
specific framework. Yeah, it pushes any model
to give deeper Ph .D. level analysis. It is perfect
for complex reports, interviews or big structural
decisions. Let me ask you, why do we need a one
million token window just for coding? Massive
context allows the AI to ingest everything at
once. It can literally read an entire company's
code base simultaneously. Wow. Yeah. So it understands
how a change here breaks a feature over there.
You need the whole map to navigate, not just
a single street. Precisely. And navigating that
map requires flawlessly clean data. That brings
us to our final highly important topic today.
Even with huge context windows and these persistent
working loops, the AI is completely useless if
the data is garbled. Garbage. Always guarantees
garbage out. Especially in highly complex systems.
Nowhere is this more critical than in high -stakes
global finance. A joint research team just dropped
a monumental new fix. Yeah, they did. Teams from
Stanford, the University of California, and Nanjing
University collaborated. They tackled one of
the most frustrating problems in modern AI. Feeding
raw SEC filings into LLMs is notoriously difficult.
LLMs are large AI systems trained on massive
text data sets. Right. If you've tried it, you
already know the painful truth. Financial tables
usually get completely flattened by the AI models.
Rows and columns just collapse into a useless
text paragraph. Exactly. And that destroys the
actual underlying accounting logic entirely.
A misplaced decimal or a flattened row changes
millions of dollars. The research team developed
a new data set and methodology entirely. It is
designed specifically to make SEC filings readable
for machines. It allows the AI to parse the data
without losing structural meaning. The researchers
propose something they officially call SCFD.
It is a reconstructed version of the SEC's edGR
document filings. Right. It translates the raw
data into layout -faithful multi -markdown. Multi
-markdown is a text format preserving complex
structural formatting clearly. It acts as a perfect
bridge between human documents and machine code.
They successfully retain the merged headers and
the critical indentation. They keep the financial
signs, spans, and complex table hierarchies.
And incredibly, it uses far fewer tokens. The
scale of this data set is just absolutely staggering.
The team dropped 152 billion token public snapshot
recently. Processing the full archive yields
around 550 billion tokens total. That is 550
billion tokens of structurally perfect financial
documents. It is an unprecedented amount of high
quality training material. This data set has
less than a 0 .1 % overlap with Common Crawl.
Common Crawl is the standard web scrape data
most models use. Right, which means it offers
highly unique, specialized training material.
Whoa. Imagine scaling to a billion queries on
flawless financial data. Two sec silence. It
completely revolutionizes algorithmic trading
and financial risk analysis forever. This represents
a massive infrastructural upgrade for the financial
AI space. It finally provides a clean, standardized
pipeline for the industry. Millions of complex,
unstructured financial documents become high
-quality training data. But how does retaining
a simple text indentation actually change the
AI's financial logic? Well, in accounting, indentation
signifies vital parent -child relationships and
revenue streams. If operating income isn't indented
under gross profit, the underlying math breaks.
Oh, I see. The AI needs that exact visual hierarchy
to understand the financial reality. Clean data
finally replaces messy guesswork in financial
AI models. It truly is the foundation for the
next generation of financial intelligence. Mid
-roll sponsor, BreakMarker. We have covered a
tremendous amount of ground today. Let's pull
this deep dive together and look at the... big
picture. We started with OpenAI becoming a comprehensive
ad tech operating system. They are building the
commercial infrastructure for the modern web.
They're moving from a simple tool to the foundational
layer. We saw how loop engineering is fundamentally
changing our daily workflows. It is turning AI
from a passive assistant into a persistent worker.
Right. We explored the rumors of robust new foundation
models arriving soon. Models with active memory
large enough to understand entire corporate ecosystems.
And finally, we looked at how researchers are
fixing raw financial data. They are repairing
the very foundation of financial training materials
entirely. All these pieces are snapping together
like Lego. blocks right now. We are building
a fully autonomous digital economy from the ground
up. Yeah. The persistent agents, the clean data,
the monetization framework, it is all finally
connecting. It is a genuinely fascinating time
to be watching this technology evolve. I want
to leave you with one final thought today. If
AI can run your marketing campaigns at con automatically,
if it can manage your inbox without ever asking
permission, and if it flawlessly parses 550 billion
tokens of complex SEC beta, what is the deeply
human skill you need to be cultivating tomorrow?
Beat. Thank you for taking this deep dive with
us today. Out to your music.