You know, AI is achieving AGI capabilities right
now, but maybe not where you'd expect. It's happening
pretty quietly, actually, inside production code
bases. We're talking about AI autonomously fixing
really complex bugs, submitting finalized code
changes, no direct human intervention. Welcome
to the Deep Dive. This is where we take the week's
key AI research and news, and while we distill
it into a quick, deep analysis for you, we're
really trying to jump into the progress that
often goes completely unreported. Yeah, exactly.
We're looking past the sort of viral chat apps
today, getting right into the, let's say, the
technical engine room of autonomy. So we've structured
this dive around three main things for you. First,
why coding is perhaps the real epicenter for
AI autonomy right now. Second, some key technical
highlights, stuff like major infrastructure fixes,
public performance checks, that kind of thing.
And finally, we'll take a really detailed look
at Delphi 2M. That's an AI forecasting serious
disease risk, maybe 20 years out. Okay, let's
unpack that first part, starting with software
development. The central idea from the sources
seems pretty clear. Coding, you know, writing,
debugging, implementing functional programs.
That's the real canary for AGI. And this revolution,
it's been so gradual. Maybe people kind of missed
the seismic shift happening. Well, what's really
fascinating is how these capabilities got added,
like in layers, often without even changing the
user experience much. So the change felt subtle,
you know. Back in, what, 2021, we got... GitHub
Copilot. That was basically smart code completion.
Right. Simple tab completion. And then by 2022,
things like ChatGPT were good enough to write
like short standalone scripts. Exactly. Fast
forward to now, 2025, and you've got tools like
Cloud Code, Codex Manalign Interface, the CLI
actually building pretty complex mini projects
and fixing bugs, submitting those code changes,
what developers call pull requests or PRs autonomously.
Okay, when we talk about that leap, we have to
define autonomous agents because this isn't just
glorified autocomplete anymore, is it? Oh, not
at all. We define autonomous agents as AI that
can, like, understand a task, plan out multiple
steps to solve it, run the code it needs, make
decisions if something goes wrong, and actually
complete the objective without constant human
hand -holding. It's a whole loop operating on
its own, like stacking Lego blocks of data. And
there's actual data backing this up. These projects
tracking agents in the wild, they show the Codex
web agent has merged over a million pull requests
already. One million. Yeah. Yeah. And those PRs,
they're getting merged at an impressive rate,
like 80 plus percent. That's for real production
code changes, even from agents that are basically,
you know, first time users on a code base. Yeah.
Plus, we've seen huge adoption like Claude Code.
It has something like 20 times more NPM downloads
as the node package manager developers use than
the code. Codex CLI. Honestly, I still wrestle
with prompt drift myself sometimes, you know,
when I'm just trying to fix simple bugs in my
own little weekend project. So it's genuinely
humbling to see these production systems hitting
that consistent 80 % merge rate. Wow. So if coding
really is the AGI canary, that consistent 80
% merge rate feels like a pretty loud chirp.
What does that percentage really tell us about
where software autonomy is right now? I think
it tells us the AI has achieved a level of reliability.
We're definitely past simple suggestions here.
These agents are delivering tangible, trusted,
ready -to -use output. Okay, let's shift from
that underlying code autonomy to some of the
headlines. Model performance, public scrutiny.
It often feels like the biggest perceived problems
in AI turn out to be, well, pretty mundane infrastructure
stuff. That is spot on. Like if you felt Claude
seemed a bit nerfed recently, you know, its capability
felt kind of reduced. It wasn't some secret downgrade.
Anthropic actually put out a postmortem. They
explained the perceived change was just down
to three overlapping infrastructure bugs. Simple
as that. And they're all fixed now. So, yeah,
model stability often comes down to pretty boring
infrastructure work, not some fundamental drop
in capability. Right. And speaking of control,
there is also that new feature for GPT -5. Users
can now toggle its thinking time. It's web only
for now, but it lets you choose faster answers
or maybe smarter, more deliberate quality. Yeah,
that's potentially huge for efficiency, letting
the user decide the tradeoff. And speaking of
public perception, Meta had that high profile
moment recently. During their public demo, trying
to show off live tasks, the system kind of hung
for about a minute. Left the audience a bit unimpressed,
apparently. But look, that was likely just real
-time latency or maybe an API connection issue
during data fetching. That's a really common
bottleneck. Doesn't necessarily mean the model
was hallucinating or broken. Still, takes guts
to demo that stuff live. Definitely. And on a
more creative note, there's this new tool, NanoBanana,
allows pretty sophisticated photo merging. The
interesting part is it handles images with more
than two people and lets you control the exact
aesthetic, the pose, for everyone involved simultaneously.
So thinking about that GPT -5 toggle, why is
giving users control over thinking speed actually
such a useful option? Well, it really lets you,
the user, consciously prioritize. Speed for simple
stuff or deep analytical quality for complex
tasks. Tailoring it. Okay, let's pivot now to
the strategic side. Financial moves, where the
money's flowing, and why so many projects seem
to fail. Right, we're seeing some serious investments
still. Databricks, for instance, just raised
a billion dollars. One billion. And they launched
an AI accelerator program. They're giving early
stage startups like $50 ,000 in platform credits,
helping them scale compute quickly. That sounds
fantastic. Pure opportunity. But then there's
this other data point that kind of balances that
optimism. Research showing that, what, 95 % of
AI automation projects actually fail. That seems
incredibly high. And the successful ones, they
apparently rely on a process -first method to
get a positive ROI. Whoa, just imagine Databricks
scaling that accelerator funding globally. That
could cause a massive, almost immediate shift
in compute allocation for startups everywhere.
Yeah. But yeah, that 95 % failure rate is sobering.
It really underscores the need for something
called context engineering. Okay, let's dig into
that. For listeners who are informed but maybe
don't use that term daily, what exactly is context
engineering and why is it apparently the key
to avoiding that 95 % failure rate? Context engineering
is basically about connecting the AI directly
to your stuff, your company's internal data,
your live workflows, your proprietary databases.
You're giving the large language model the specific
relevant context it needs to do its job well
for you. Not just feeding it generic web data.
That's how you get consistently high quality
outputs that actually, you know, matter to the
business process. So based on the research then,
what's the core strategic reason the successful
projects manage to avoid that huge 95 % failure
rate? It seems they really focus on defining
the underlying business process before they even
think about implementing the AI solution. Process
first. All right, let's turn now to what feels
like one of the biggest scientific breakthroughs
mentioned in the sources. Delphi2M, this new
AI system from Europe. researchers, it seems
genuinely set to redefine proactive health care.
Oh, its capability is absolutely stunning. Delphi2M
forecasts the risk for 12 ,258 distinct diseases.
Think diabetes, neurological disorders, heart
conditions, the whole gamut, up to 20 years into
the future. And it does this just using a patient's
standard electronic medical records. Nothing
more exotic than that. The training data must
have been immense. It was initially trained on,
what, data from over 400 ,000 UK patients, including
everything from doctor visits, hospital records,
even known lifestyle choices factored in. Exactly.
And critically, this is super important to make
sure it wasn't just good for the UK population.
They validated it. They tested it against 1 .9
million entirely separate Danish patient records.
That validation step is crucial. It proves the
model has generalizability. It proves it's not
just hallucinating predictions based on some
bias in the original UK data set. The potential
implications for actual patient care seem profound
here. Delphi2M could help doctors shift from
just reacting to symptoms that already exist
to actively anticipating future health risks
years in advance. That could fundamentally change
medicine towards really personalized prevention.
Yeah, but it's really important to stress the
caveat here. Human doctors are still absolutely
necessary. They need to interpret the AI's predictions.
The system analyzes risk. The physician provides
the judgment, the empathy, the actual treatment
plan. It's designed to augment the doctor, definitely
not replace them. So going beyond the headline
number of diseases or years, what's maybe the
biggest operational shift Delphi2M could bring
to something like a standard annual checkup?
Well, it fundamentally redefines that checkup,
doesn't it? From just a snapshot evaluation of
current status to proactive long -term risk mapping
for the individual. Okay, so just to quickly
synthesize everything we've covered for you today.
First, AI encoding. It's quietly crossing some
really crucial autonomy thresholds. That 80 plus
percent PR merge rate is a key example. Second,
model stability issues, often tied to boring
infrastructure fixes, not fundamental nerfing.
And users are getting more control, like toggling
speed versus intelligence. And finally, predictive
AI like Delphi 2M is really starting to redefine
human health and the whole concept of prevention.
So what does this all really mean for you? I
think the takeaway is that the most impactful
AI progress is often happening silently. It's
in the background, deep inside complex systems,
the infrastructure, the code bases, the hospitals.
It's not just in those viral chat apps that grab
all the headlines. You really have to pay attention
to the infrastructure, the less flashy stuff.
Thank you so much for joining us for this team
dive today. We really hope you continue learning
about these advancements. They're fundamentally
reshaping tech and health right now. And maybe
here's a final thought for you to chew on. Consider
that China is apparently already teaching children
AI principles from age six, and that the DeepSeek
foundational model, a pretty capable one, reportedly
cost only $294 ,000 to train. Not millions, thousands.
So how rapidly do you think the center of gravity
for foundational AI breakthroughs might shift
in, say, the next five years? Something to think
about. Until next time.