The Signal Layer: What to Build When Anything Can Be Built — Lena Hall, Akamai

[music]

>> How is the conference for all of you so

far?

Great. Awesome.

Um well, I think this was the best most

productive year for so many of us.

I'm Lena. A few days ago, I solved a

production incident on a trail near a

waterfall.

My friend ran

18 agents while riding his bike.

We're literally drowning in abundance.

We have more output, more speed, more

leverage than any of us have ever had.

So, why do we have this feeling like the

ground underneath is moving too fast?

One of the engineers that I met at this

conference

said yesterday that it feels like

the opportunity cost for not working

9:00 a.m. to 9:00 p.m. 6 days a week is

too high right now.

So, we're all token maxing. We're all

working all the time.

But the same abundance that made you

fast, it also made everyone else fast.

So, now everyone can build everything.

Your competitor can build your feature

this afternoon, too.

So, the cost of the average just went to

zero and so did its value.

A year ago, the superpower, as we were

told, was to be good at using AI.

But models got so good

and they got so easy and everybody now

is a lot more skilled at using AI and

everybody's pointing AI at the same

goals.

Cuz AI gives everyone the same answer

because everybody is asking the same

question.

It It on data and data is a record of

what has already happened.

So, when you point AI at tasks like,

"Tell me what users want. Make more

money. What should we build? Make this

viral."

It answers from the common knowledge.

Very competent, very confident, but also

very identical to what it tells your

competitor.

To see something that data doesn't show

yet, we need to have a vision, a point

of view, a read on where it's going, and

then use all that automation to execute

it.

AI is a really smart convergence

machine.

So, if you leave it alone, it makes

everything the same.

There is one decision, though, that AI

can't and shouldn't make for you. It is

to decide what to point at.

So, the job, the new job for everyone of

us is deciding what it makes, being the

reason the right people choose your

version over the identical-looking rest.

But also, I'm sure many of you uh walked

around the Expo Hall at this conference,

and there are so many amazing products,

so many tools and vendors.

They're all solving important problems.

But what Why do they all sound the same?

So, when anyone can build anything, what

makes me different? What makes you

different? Why should anyone pick your

version, your product? Um I call this

work uh signal layer.

And there are two There's two halves to

solving it, to getting this right. So,

that's how we will walk through it.

The first half is knowing your signal,

being able to define it very clearly.

What you're building and why it's yours

and not the average. So, that's the

build side, the code, the product, the

road map.

And the second half is emitting that

signal without distortion. So, making

sure that your customers um making sure

what your customers come to believe

about you actually matches what you

believe and what you've built.

That's the ship side the content and go

to market engineering.

And I've had an unusual vantage point in

this. I've built products as an

engineer. I created my own as a founder.

I brought other people's products to

market. So three very different jobs

with one identical challenge. The signal

doesn't always survive. So let's start

with the build side.

So what do we even work on? Everything

is implementable.

Two years ago the best autonomous coding

agents you know solved on the fraction

of the tasks on the standard software

benchmark

and now the best agents are in the high

eighties. So we nearly tripled the

amount of

writing and shipping barely moved a

third.

The benchmark was measuring the part of

software engineering

that has a greater

and shipping is where all the ungraded

parts come back in. So

here is the rule underneath it. Anything

that you can measure you can train

against as Sarah Guo puts it.

A compiler is a free grader. A test

suite is a free grader.

And the instant a task can grade itself

you can grind a model against you know

that grade until it wins. Automation

of code was first because it's the most

checkable thing that we have.

So implementation is converging for free

for everyone at the same time

and the most buildable thing and the

most valuable thing are almost never the

same thing.

So the model will build whatever you

point it at but it will tell you nothing

about where to point. Anything visible

is replicatable.

So now, some people when they hear

everything is implementable, they panic.

Um but we can flip the question.

The pointing is actually the job. It has

always been the job. We just had so much

implementation work in the way

um that we never had to get good at it.

So, how do you decide where to point

that?

Paul Graham shared some wisdom on this.

Where you find something that people

genuinely want is by feeling the need

yourself.

Build something you and your friends

need because the market hasn't formed

yet, surveys can't see it, and your own

need is the only signal that isn't a

crap signal.

And the best ideas may sound genuinely

lame at first, like a guy uh strapped

with a with a camera on his head live

streaming his his life. That sounds

really ridiculous, but it became Twitch.

Um and the convergence machine doesn't

really, you know, propose proactively

these

uh weird, specific, genuinely

embarrassing ideas.

But even with the Twitch example,

it worked, but there were a thousand

other similar startups I start startup

ideas that didn't.

So, the weird specific signal is

necessary, but it is not sufficient.

It's also really tempting to say that

we just need to have good judgment and

good taste and call it safe. But taste

is really just preference under

feedback, and preference under feedback

is exactly what these systems can learn.

Anything you can demonstrate enough

times

uh with a better or worse signal

attached, the machine can eventually

imitate. So, broad good taste is not

really a differentiator.

What actually resists

training is more narrow and more

durable. So, two things.

Taste and judgement about what hasn't

happened yet.

Because there's no data for an event

that hasn't occurred. And then taste and

judgement embedded in a relationship

that the model can't observe.

What this customer in this situation

with this history that you share

actually needs.

The model has read everything ever

written about your customer, but it has

never actually met them.

So, if broad judgement is not safe, and

the AI just handed everyone the ability

to build anything, what's left to be

good at?

Richard Hamming uh spent his career

studying why some scientists did great

work and others who were just as smart

didn't.

He found that the great ones worked on

important problems.

And the problem isn't important because

it just sounds impressive.

It's important when you have a

reasonable attack on it. For example,

time travel is consequential, he would

say, but it's not important because

nobody has an attack on it.

So, Hamming would tell you to keep 10 or

20 ideas

um

on important problems in the back of

your mind so that when you finally have

an attack, a new tool, a new angle I

think that only you noticed, then you go

for it.

But in Hamming's world, the rarest thing

was having an attack. And AI just handed

everyone an attack on everything.

So, the rarest thing is knowing which

problem is actually worth attacking. And

that judgement comes from being a real

person, close to a real domain, with

your own battle scars, your weirdly

specific experience, the thing that you

care about more than is reasonable.

And you don't actually need to be first.

You just need to be genuinely close to a

problem you actually understand where

your insight is in the delta between

what AI has been trained on and what

should exist.

So, let's say you did it. You found that

sweet spot problem that

the one that you had an honest attack

on, that you built this thing. It's

genuinely good. It's genuinely yours,

not the average.

You can still lose uh because knowing

your signal is only half the job. The

other half is getting it from your head

into the head of a person that it was

meant for.

And it's about reaching the right

people.

And what do most of us

do for that?

We make content. So, let's talk about

what uh AI convergence machine does to

that.

What happened to the internet in the

last 2 years? Open any feed,

everything has started to sound the

same.

The same LinkedIn posts, the same, you

know, three bullet points and a bold

takeaway, and

the same blog post that uh says nothing

but actually looks very polished.

Um your readers can now pattern match AI

in just half a second. So, if a model

could have written your post from a

one-line prompt, your reader brain just

skips it for the same reason.

So, AI has really learned the algorithm.

It has learned the format that performs.

It has learned what gets clicks, and

everyone wants to hand the machine a

paragraph and say, you know, "Make it

viral. Make me rich." It fills every gap

that you leave with sameness.

So, what do you put in and what do you

let it fill in?

Cuz these there there are two different

ways to use this thing, and they look

very identical from the outside. One is

you give it an average prompt, and gives

you the average output.

And you ship one more indistinguishable

drop into an ocean of indistinguishable

drops.

So, you've automated your own

irrelevance very efficiently.

And two, you can bring in the part that

it can't have, your specific point of

view,

the real story that you were actually in

the room for, and then let the machine

do the converging work, the formatting,

the drafting, the algorithm

optimization, the cleanup around the

core that it

could have never generated.

The signal distorts on the way out. So,

you can have the signal perfectly clear

for you and still watch it fall apart

between your brain and your users'

understanding of it.

And in my experience, it breaks in three

places.

And there are fixes for each, but

they're different depending on product,

the type, and the size of the company.

One of them is source distortion, which

is very common in startups.

Founders, actually, they usually know

the signal so well

that they always have this accidental

gift of compressing it past legibility.

They often assume the context that the

audience doesn't have, and the room

hears something technically very cool,

but they

doesn't they don't really understand why

it matters.

I helped this one YC company with uh

this exact thing recently.

Absolutely brilliant founders, genuinely

new product, but every pitch that they

started um was you know starting with

architecture, with the clever parts,

with things that they were very proud

of. But it really landed as noise

because the customer pain has been

deleted from the whole story.

So, we rewrote the opening to include

the thing that the user hated, and this

product actually killed. So, the same

product, the same week, the next

conversations converted into pilots, and

then we turned that into repeatable GTM

system.

Organization distortion is another type

of distortion that almost every big

company has.

As signal travels through layers of

management, through legal, through

sales, through every department, at

every hand handoff, it gets rewound

towards the average.

And this really doesn't come from

incompetence, it comes from investment.

So, hand a founder and the person three

layers down the same task and the same

AI, and you get two different things.

Um the founder really sweats the

unaverageable details because the

outcome is really theirs and they're

personally invested and affected by it.

And others just ship it to spec, they

close Jira tickets, they were asked for,

you know, something like compliance, not

as much conviction.

So, a long delegation chain plus

convergence machine is really a factory

for automating the signal out of your

own company.

So, the first instinct usually is to add

more process, which adds layers,

bureaucracy, and slows everything down.

And we don't want that. Um

to fix this, we have to help take the

signal back

and reattach it to the outcome like a

founder and add the very thin signal

layer to your go-to-market engineering,

where its only job is to validate and

carry the original intent across the

handoffs intact.

Machine distortion is another way you

can lose signal. You write one careful

launch,

your claim, your evidence, and your

scope is very clear, but then of course

AI remixes it

um into a tweet, into a sales deck, into

a partner one-pager. For example, you

might have had this one very narrow eval

that scored 94%

but it was repeated enough times that

your customers actually heard it as a

promise.

So, we see the same through line. Your

signal has to survive the trip

undistorted.

And this is something you can engineer.

So, we need a thin signal layer, a small

deliberate function whose job is to make

sure that what your users take away is

still the specific thing you meant.

Say you're building a monitoring tool.

There are 12 other tools in this

category, but yours does something

different. It tells you what not to wake

up for, for example. It stays quiet on

the noise, so when you

get paged at night, you believe it. So,

that quiet, that trust earned by silence

is your signal.

So, first, say it in one sentence with

the limit built in. Definitely don't say

intelligent AI-native observability

platform.

Say something like uh stays quiet on

anything it can't tie to a real user

impact and shows you everything it

silenced so you can overrule it. The

promise and the scope are welded

together here.

Then make sure that the limit can't be

edited out. So, in the product, every

suppressed alert is visible. In the

launch, statements like 90% fewer pages

uh live next to statements like every

silence is visible and reversible. So,

when AI chops your launch into a tweet,

it can keep the impressive number, but

also remove the part that

keeps the part that uh keeps the product

honest.

And before you scale it, check what

people actually heard. So, give a readme

to an SRE who has never seen your

project and ask a person to describe the

product back to you. The gap between

what they say and what you meant is the

distortion that you were about to

broadcast.

And it's a very lightweight signal

layer, and a lot of it is buildable, So,

you can automate more of the checking

and the catching and the surveying than

most people realize.

So, step back and ask what all of this,

the building, the shipping, the

undistorted signal, is actually for.

It's for one thing of getting a human or

increasingly an agent to choose you and

rely on you when they have an infinite

identical-looking alternatives. So,

that's trust. Trust is the one thing

that's left with no greater. There's no

benchmark for it, no reward signal. It

can't be entirely automated because it's

granted slowly through relationship with

consent. For example, doctors who

open one particular tool every morning,

they didn't have that habit trained into

them.

And what happens if we get this wrong?

Getting your signal wrong is actually

not neutral. It's negative.

Producing averageness is not free. You

actually pay for it in tokens, in infra,

in the salaried hours of good people,

you know, with with customers that

take a look at your product once, decide

once, and never come back. So, every

generic post teaches them that your name

isn't worth the click. So, you spend

real money to make yourself harder to

choose.

So, back to the main question.

We got faster,

but the speed is not where the value

went.

Uh the value moved up to deciding what

is worth building, what is worth saying,

what deserves trust. And where does the

thing that you actually

um that that you meant survives the trip

to the people that it was for.

So, you don't need to be first. You need

a real problem and enough conviction to

carry the signal clearly through to, you

know, right people to find it.

So, when you can build anything, you

should build trust.

Have the strongest conviction, define

the signal yourself, protect it from

distortion, and use AI aggressively for

everything else.

Thank you. Let's connect and happy to

chat with you afterwards.

Thank you.

>> [applause]

[music]