Unique signals

Every company has a unique signal they look for to prioritize the best accounts that have the highest chance of converting to revenue.

It optimizes for:

1. Right timing

2. Highest chance of pain

3. Highest chance of demand

Figuring out your unique ICP signal, and being able to find it, unlocks tremendous productivity for your prospecting and closing teams.

An example of this is something along the following lines...

An example

DevTraining, Inc. sells training software. The best time for them to reach out to an account is when a company is migrating from one programming language to a new one. This is because DevTraining, Inc. helps developers learn new languages. It's the right time with the highest chance that company will need DevTraining, Inc. training software.

These signals can get hyper specific and very hard to pick up at scale because that data doesn't sit in a database somewhere. Or, it would require A LOT of manual research.

How to find your signal

You can do this by examining the characteristics or situation the company was in when they first engaged, and then closed.

1. Were they hiring for specific roles?

2. Did they just a hire a new CRO within the last 3 months?

3. Were they moving to a different programming language?

4. Did they enter into a specific FedRAMP process? (A type of certification that government agencies require in order to do business.)

5. A # of developers download something from a GitHub repo.

6. Something changed on their website.

7. A specific software platform was adopted.

8. Construction company wins a bid.

The list goes on and you’d be surprised just how many I come across (or have never heard of) before at Clay.

Another way to look at it…

1. Why did the customer buy now?

2. What changed about that company’s situation at the time?

3. What caused that situation to be more painful than the status quo?

4. What would’ve been the consequence if they did nothing?

Signals could be from companies or people at those companies.

Cross referencing with your sales cycle

The next step is to correlate this data with the length of your sales cycle and ACV. For example, the signals should come from companies that:

1. Closed the fastest relative other deals.

2. And, had highest ACV

If you can nail this, then you’re finding the signal of company that has the highest chance of converting the fastest at the upper level of your ACV.

Excited to announce I joined Clay

After 5 years in cybersecurity, I decided it was time for something completely new. Frankly, I thought I’d stay in security longer, mostly because experience and skills compound over time when you stay in one industry. But, every once in a while, a truly unique, special company emerges.

I’ve followed Clay for nearly two years and have always been a fan. So when reached out, I didn’t need much convincing to do a ‘trial’ for a few weeks. My assumptions from the outside were all confirmed when I became an insider. The story and future are compelling.

The opportunity checked many boxes for me: 1) team, 2) culture, 3) differentiated product & GTM motion, 4) position/market opportunity, and 5) a role that provides ample opportunity to get creative. (Check out the SIMPLE framework for evaluating startups.)

In addition, they were just included in Wing Venture Capital’s Enterprise Tech 30, a list of the most promising private enterprise companies decided by an invite-only group of VCs and heads of Corp. Development. One of the most interesting takeaways is that Clay is the only non-AI SaaS company in the early-stage category. That says a lot.

What is Clay?

In short - Clay is a data enrichment & orchestration platform for growth, sales, & GTM teams. Teams use Clay to scale personalized inbound, outbound, expansion, and retention experiences with aggregated data from around the web.

Data enrichment comes from 2 sources:

1. Aggregating & providing access to 150+ providers (yes, you can use Clay to replace ZoomInfo to get better coverage & a wider range of data points.)

2. Claygent is an AI agent that you can use to scrape and analyze websites and ultimately collect unique data relevant to your ICP. Give it a mission, and it will perform that mission for every company or person of your interest.

Then, you can orchestrate this data to accomplish a wide variety of things:

• Enrich your CRM (Salesforce, HubSpot, etc.)

• Write personalized emails at scale

• Send those emails and data to a prospecting sequencer

• Automate the creation of company research briefings

• The list goes on…

GTM Engineers

There are no “Account Executives” or “Solution Engineers”. Instead, Clay has GTM Engineers. Simply put, we help customers engineer GTM solutions with Clay.

Customers are blown away when we can build personalized demos on the fly and ultimately show people something they didn’t think was possible. There’s no better demo or ‘magic moment’ than combining those two.

For example, you can see how fast I built a prospecting workflow in one of my last posts: Automate Founder Prospecting in < 6 Minutes.

GTM teams need a better data source (or aggregator)

Typically, teams rely on an enrichment database to fill contact information gaps. These are the common person, title, email, and phone numbers. As a company scales, it may extend to 2 or 3 enrichment providers because its original database doesn’t have 1) enough accurate information or 2) large enough coverage for phone and email information 3) data specific to their unique ICP.

Other data comes from manual research from SDRs and AEs needing to personalize their outreach. Teams scour the web to find:

• Recent job openings

• Linkedin profiles

• Recent news about the company or person

• 10k filings

• Blog posts

• The # of engineers a company has

• Whether or not a company is SOC2 complaint

The list goes on, and each company has its snowflake requirements.

No database can provide all the information that is needed.

If we’re honest, the research aspect of these jobs is mundane and tiresome. That’s why, despite all efforts, people may do some of it, but it’s not happening as much as needed. You go crazy after a while…

In addition, there isn’t enough time to find all the needed signals and apply them at scale across many target accounts.

This area of GTM has been in need of serious modernization.

This is where Clay comes in.

Clay’s data enrichment approach

Some data providers are good for some data and bad for others. Instead of getting trapped with one solution, you can leverage the strengths of each provider to assemble the most complete set of data specific to your business.

Clay uses a waterfall approach to find the data across these providers. Check one provider; if data isn’t found, then go to the next one. Here’s an example:

The result is significantly better data coverage with higher fidelity.

This information could be anything from fully enriched LinkedIn profiles to phone numbers, funding, etc.

Now, what about all of those snowflake requirements that require manual research?

• Is the company SOC2 compliant?

• Does the company deploy its software via containers?

• How many warehouses does the company have?

• What about divisions?

• How many developers are on the engineering team?

• What persona does the company sell to?

This is all the information databases don’t have. Still, it could be critical to qualify early, personalize, or use it as a signal to identify companies that may be more ready for your solution than others.

This is where Claygent comes in.

You can use Claygent to find information anywhere on the web and programmatically bring it into your Clay table at scale. No more manual research…

Claygent - Clay’s AI agent that automates your manual research at scale.

You can use it for 3 key use cases:

1. Data missions: Find & aggregate data that can give you a competitive advantage.

2. Data insights: Ask any question about the data you collect.

3. Actions: Orchestrate the data to push it into your tooling to.

   1. Enrich your CRM

   2. Feed personalized emails into your outreach/prospecting tool.

   3. Use Zapier to prepare a research doc for the company you’re meeting.

   4. The list goes on…

Here are some examples of Claygent missions:

You can get super creative with this… literally, you never have to do manual research again.

The community also LOVES Clay

Check Clay out and let me know if you need help (alex.lindahl@clay.run).

GTM Foundry is brought to you by Spiky.ai, a video analysis platform for revenue teams. Their AI models add emotional context so you can better understand if your customers are getting excited…. or losing interest.

Designing Your Company’s Network

Kick-starting revenue at $0 is tough. I’ve experienced this at 6 Series A startups now.

The most successful at kickstarting are those who are:

• Relentlessly resourceful

• Laser-focused on a defined ICP

• Design & leverage their company’s network

Let’s focus on the networks and working through them intelligently.

Successful entrepreneurs think strategically when building their company network to help them reach their target ICP. These people and networks include:

• Venture Capitalists

• Angel Investors

• Syndicates

• Advisors

• Executives

For example, If you’re a cybersecurity startup, you can optimize for players deeply embedded within the CISO (Chief Information Security Officer) and broader security community:

• VCs: Cyberstarts and Team8.vc are known for having large networks of CISOs who lend their expertise in the company-building process, including ideation, validation, Design Partnerships, advisory, and potentially becoming customers themselves.

• Angel Investors: These could be CISOs, Executives of larger security companies, Account Executives, or Entrepreneurs. They’ll all have skin in the game and be inclined to help connect the dots.

• Syndicates: Silicon Valley CISO Investments is a well-known syndicate of CISOs. Cyber Club London is another one.

• Advisors: These could be anyone. For example, I’ve been a GTM advisor to several startups and have helped make introductions & recruit key players.

• Executives: Optimizing for those who’ve operated in your industry goes a long way.

This leads me to how you can effectively leverage these groups, especially since reaching almost anyone has never been easier or required fewer steps.

Will 6 Degrees Become 2 Degrees?

First, a shot detour because I found the following pretty interesting:

We forget that the “6 degrees of separation” concept was coined back in 1989. Yet, most would assume this is still true.

That was 33 years ago. It’s outdated now that we 1) have the web, 2) people are more connected, and 3) we have social networks. The math has changed.

The following read explains: The World Is Shrinking: 6 Degrees of Separation Is Now 2

To summarize the key takeaways:

• 2011: Facebook analyzes 721M users and 69B friends and finds that on average, the degrees of separation were between 3.4 to 3.8

• 2016: Facebook now has 1.6B users. The average degree of separation falls to 3.57

If that trend continues to shrink lineally by 25% every time Facebook’s audience increases by 122%, by the time it reaches all 7.4 billion of the world’s current population, then the range would fall between an average of just 1 and 2 degrees of separation and connection between all people in the world.

It’s a bit hard to believe that we’d get down to 2. But you get the point. We should be spending more time to figure out connection paths to our ICP.

Okay, I went off on a bit of tangent. Let’s get back to the playbook you came for.

The VC Warm Intro Playbook

A key playbook in Pre-seed to Series A is to leverage your investor network for warm intros to your ICP. Some VCs even tout this as one of their main value adds, especially for VCs with a platform team.

It’s a great playbook if done in the right way.

However, most startups are doing it wrong. It goes something like this:

• AEs make a list of dream accounts

• Execs send the list to the VC with no context

• Maybe 1 or 2 intros are lobbed back

• The team questions why it’s not working

Of course, that’s not going to work. You’re leaving a lot of the work for the investor to do, and they don’t have the time to connect the dots.

How to Make it Work: The Email Forward

Make it easy for the investor to:

1. Know who to connect with

2. Make the connection

3. Provide the context behind the ask

4. Create a compelling to meet

5. Do all of the above in under 2 minutes

Enter the forwardable email. The play goes like this:

1. Research your desired company

2. Create a hypothesis for why the company needs you now

3. Find a current, relevant success story (from your company)

4. Find the right contact

5. 👉Draft and send an email that your investor can forward 👈

6. Provide the email address and LinkedIn URL

7. Investor forwards the email and adds a 1-3 sentence commentary above your note

This sounds like a lot of work, but you can automate a lot all of it with Clay. Similarly to how I showed you how to automate Founder-Led-Prospecting in under 6 minutes in my last post.

I’ll show you this automation in my next post. Subscribe to get it when it drops:

Subscribe now

Forwardable Email Templates

PLG Example (to get higher):

Hi John,

We’re seeing traction at [Target Company]. Several users are actively using [Your Software].

We’re looking to connect with [Exec Name w/ LinkedIn URL] as we believe there’s a strong fit and looks like this would be under his/her purview. They’d be a strong fit because [Your Hypothesis].

For more context - similar companies such as [Customer A] & [Customer B] also had [Problem 1] . They’ve turn to [Your Company] to:

• [Business outcome 1]

• [Business outcome 2]

I believe we can help [Target Company] achieve similar outcomes.

Would you be able to make an intro?

Thank you,

Alex

Of course, add your own character and writing style. But that’s the gist of it.

Non-PLG Example

You can use most of the same construct but a different opener and positioning.

Something like this:

Hi Maria,

There’s been much excitement, specifically in the [Your Industry], for how [Your Company] is helping VPs of Revenue Operations.

I bring this up as we came across your connection to [Executive Name w/ Linkedin URL]. [Target Company] is scaling its sales org and increasing its demand gen efforts. The challenges we see revolve around data quality and minimizing repetitive research processes.

Our customers [Company A] and [Company B] saw a 150% increase in data coverage and automate over 20 sales research workflows. We could help them achieve similar benefits.

Would you be able to introduce us to [Executive Name w/ Linkedin URL]?

Thanks,

Alex

P.s. looks like he/she is located in [Exec Location]. We’ll be there next week, so timing could be good.

If you have other examples, I’d love to include them in the post and tag you in.

You can also follow me on LinkedIn + Twitter + Substack

I was inspired to write this post based on feedback that I’ve received over the past 2 weeks while working at Clay. Everyone from Founders to SDRs to Account Executives are struggling to prospect efficiently today. So, this process can really be used by anyone who needs to prospect.

Before we get to the demo today, let’s talk a bit about Founder-Led-Sales and why Founders need to prospect beyond their network in the early stages of company building.

Importance of Selling Beyond “Friendlies”

There’s a strong belief in the venture ecosystem that Founders should sell the first $1M in revenue before hiring traditional sales. Founder-Led-Sales is important for a number of reasons:

1. Customer conversations & short feedback loops help the team build the most optimal product based on customer feedback.

2. The Founder often knows the most about the market/problem/solution and is therefore the best equipped out of the gate to land early sales + build momentum.

3. The playbook and lessons will help new sales reps ramp much faster.

Obviously, the easiest way is to start with ‘friendlies’, existing network, and the VC’s & advisors’ networks. However, these customers don’t provide the most real feedback…

So, you need to pair the above approach with cold prospecting.

Jyoti Bansal, an Entrepreneur who started AppDynamics (sold for $3.7B), Harness (valued in the billions), and Unusual Ventures, explains why in an interview:

During the customer discovery process, Jyoti recommends moving beyond “friendly” contacts. He is a firm believer in cold outreach because customers outside the founder’s network are going to be more direct and blunt with their feedback. Jyoti recommends having at least 50 “cold hard conversations”— this is the best way to validate early assumptions, and iterate on early messaging, ICP, and personas. 

Founders need to learn how to sell and become comfortable with rejection. Initially, a big part of selling is reaching out to many people, articulating the pain, and the solution. This is critical to the process of finding product-market fit.

The Current Process Today is Grossly Inneficient

I know, I’ve been one of the first sales reps at 6 Series A companies (Acquia, Outlearn, Scalr, Snyk, Dazz, & Chainguard.)

The process is something like this —> find companies in your ICP —> research companies —> do a LinkedIn search for contacts —> research which people are the best to reach out to —> switch to ZoomInfo or Apollo to find contact information —> import that data into a sequencing tool like Outreach or ZoomInfo —> write a cadence —> do more research on the person to personalize —> execute the cadence (some steps are manual) —> go to LinkedIn profiles to send connect requests —> go back to those who accept and write a message to start a conversation —> land a meeting.

There’s a lot of smaller steps inbteween that compound the amount of time spent on this. It. Is. Horrible.

The research part alone takes an enormous amount of time if you’re doing it correctly.

Altogether, you need to use 6 tools at minimum:

1. LinkedIn 2. LinkedIn Sales Navigator 3. Google Searches 4. Other sources of information such as 10ks, recent news, etc. 5. Outreach or Salesloft 6. and of course, ChatGPT at some point.

That’s the bare minimum.

Founders don’t have the time to do that efficiently or effectively… especially on top of all other demands.

This is How to Automate The Process in <6 Minutes

This is possible with 2 tools: Clay + La Growth Machine

I’ll show you how to combine both of these tools to do the following (all under 6 minutes):

1. Find companies that match your ICP.

2. Enrich data for those companies.

3. Find all the people at those companies that match your desired persona.

4. Enrich those profiles with info such as emails, phone numbers, etc.

5. Create a campaign in La Growth Machine.

6. Automate sending LinkedIn connection requests to the list.

Here’s a video of the workflow and how I connect the two:

Adding in an automated LinkedIn message after someone accepts a connect request is super easy. I just didn’t show it in this video.

Just choose this templated campaign + compose the first message you want sent.

Additional Automated Research Examples w/ Clay

You can automate an absurd amount of research with Clay.

These are some examples:

1. Scrape websites

2. Pull in recent news

3. Enrich contact info

4. Find social profiles (Twitter, Linkedin, Instagram, etc.)

5. Pull in profile info

6. Google searches

7. Find recent LinkedIn posts

8. Find warm intros in LinkedIn

9. Find job openings

10. Pull in job info

11. Count people in a specific team

12. Find followers on GitHub

And, so much more.

Examples of Enrichment Data Points + Providers

Share

La Growth Machine is my 2nd Favorite Tool

These are some examples of automate sequences that you can build. This is my favorite outbound sender because of its simplicity and ability to visualize these outreach streams.

Linkedin + Email:

Linkedin + Email + Twitter:

I’ll be posting more Clay tutorials for GTM engineering in the future that can be used by any Founder or GTM team.

Subscribe now

Since I’m doing some contract work with Clay for a couple weeks, I’d be happy to spend time with anyone looking for help to build in Clay. Just shoot me an email: alex.lindahl@clay.com.

You can also follow me on: LinkedIn + Twitter + Substack

This is a 2 part series of how Reddit started as an NSFW site.

…I’m kidding! Though, the chart below might make you think that.

We will go over the following in this series:

1. Part 1 - How Reddit got to today with 5 stages of network effects.

2. Part 2 - How Reddit plans to expand its TAM and grow post IPO.

Reddit’s Origin Story

Reddit was founded in 2015 by Steve Huffman & Alex Ohanian in Medford, MA. Instead of your typical spring break activities, they attended a lecture by Paul Graham. This naturally led to an invite to apply to YCombinator’s first class, though their first idea “My Mobile Menu” was unsuccessful. They brainstormed other pitch ideas, and Paul Graham ended up convincing the roommates to start the “front page of the internet” and pulled in $100k in seed funding.

But, how do you start a user-generated content site with no users or content?

This is the classical “Cold Start Problem” that Andrew Chen coined. (He’s a General Partner at a16z.)

This is a problem that exists when you’re building:

1. A 2 sided marketplace, or

2. A social network (like Reddit)

The framework has 5 stages of growth:

1. Cold start problem

2. Tipping point

3. Escape velocity

4. Hitting the ceiling

5. Moat

And has ultimately led to where they are today:

Let’s dive into each stage.

Stage 1A. Build an “Atomic Network”

An atomic network is the smallest number of users needed to stay engaged & self-sustained. Once you reach this magic number, the users and their activities should self-sustain themselves. In other words, they are getting value from the network and giving back to it.

These are four examples from some of our favorite tech brands:

• Facebook: started with a tight-knit college.

• Uber: started with 1 city.

• Slack: a team of at least 3 in a company.

• AirBnB: hundreds of active listings in a specific market.

Now let’s dive into Reddit’s first atomic network.

They use the following strategies to build this

Fake profiles

Seed the site with users and content. They made hundreds of fake profiles & submitted content. A simple backend submission process helped them turbocharge profile creation. It had 3 inputs:

• “Users” (the fake ones…)

• URL

• Title

This helped the Founders accomplish 2 essential objectives:

1. Make the site feel alive

2. Set the tone, feeling, & character of the site

Simplicity

One community (no categorization). No comments. Two possible inputs for general users:

1. URL

2. Title

The simplicity of this reduced the need for users to make any type of decisions. (Extra time thinking = increased chance of user drop off.)

Reduce friction everywhere

1. No decisions to make (except to submit URL + Title)

2. No email collection

3. No censorship

Sticker the world

There is no way you could use this today. But back then… hardly anyone was using the strategy. It worked!

Yes, stickers: the soundest investment I ever made. I used to travel around the country a lot (thank you, Chinatown bus), and everywhere I went I took stickers with me. I put them on signs, poles, and even other advertisements.

“Please sticker responsibly!” we would say, and hand out these stickers at events, meetups, just to random people on the street. This may not sound like a major or even strategic marketing approach, but we kept at it. The focus was on building a great product and a community online–the stickers were just an excuse for people to show their allegiance.

How Reddit Built Its Empire On 500 Bucks, Stickers, And Giving People What They Want

All of these tactics resulted in the growth of their primary and only subreddit.

Screenshot from July 25, 2005:

Stage 1B. Build Adjacent Networks, Rinse & Repeat

This is where the NSFW stuff comes in.

The chart shows the topics of subreddits - the adjacent network to their front page.

It only looks like Reddit started as an NSFW site because they didn’t include the homepage. (That was their only community until NSFW. Then programming and science subreddits were introduced.

And, then you reach the tipping point… just after the start of 2008, they had a Cambrian explosion of subreddits.

% of posts submitted to various subreddits:

Stage 2. The tipping point

The challenge then moves to creating a repeatable strategy for building more networks to expand the market.

This was ultimately the point just after 2008. The creation of subreddits was highly repeatable.

Generally, there are 2 strategies for this stage:

1. Make the network invite only.

   1. LinkedIn, Gmail, & Facebook all used this strategy.

2. Bootstrap with a strategy of “come for the tool, stay for the network”

   1. Attract via a single-player tool and convert into more active members. (Instagram & Dropbox used this strategy)

In the context of Reddit - single-player mode was finding the most interesting content, which led to participation, and then feeling a part of the community.

2008: Retracing the Evolution of Reddit through Post Data

This year, we see an explosion in the diversity of subreddits. What the heck happened? As it turns out, Reddit added a feature for users to create their own subreddit. Almost immediately, people created /r/pics, /r/funny, /r/WTF, and many other subreddits dedicated to pictures. In just the matter of a few months, 1/3 of Reddit's content was sorted from /r/reddit.com into subreddits dedicated to specific topics. Thanks to the new "create your own subreddit" feature, Redditors were able to sort the majority of their own content out into the proper forums. The Reddit system works! Cool!

It's also pretty cool to see the spike in posts to /r/politics and /r/obama near election time in November, showing just how active Reddit was in the Presidential election. It seems even from the early years, Reddit was a politically-oriented web site.

Stage 3. Escape velocity

Success is scaling and this is when the strategy shifts to focus on strengthening 3 types of network effect forces:

1. Acquisition effect

2. Engagement effect

3. Economic effect

Stage 4. Hitting the ceiling

The rapid growth of networks causes a peculiar thing to happen. The community wants to grow, but at the same time tear itself apart.

Only new products & innovations can help kick off the next growth wave. There were a few features that helped here:

1. 2012: Pictures

2. 2014: Redditlive

3. 2015: Upvote/Downvote

4. 2017: Video

Another way to break through the ceiling is to go from single to multi-product.

Stage 5. The moat

You have 2 characters to choose to defend your fortress:

David and Goliath.

• David’s strategy is to: Pick niche segments within a larger network & build atomic networks that are defensible with key product features. He aims to become more valuable to that niche than the larger incumbent.

• Goliath’s strategy is to: Leverage scale to drive higher monetization & value for users.

Reddit took on David’s strategy. Each subreddit is a self-sustaining niche community that is hard to reproduce elsewhere.

Finally, seeing all of the growth together:

Growth of subreddits:

That’s over 2M niche forums!

Growth of content submissions (blue) and users (red)

And, now going public!

Subscribe for Part 2: Reddit’s Growth Strategies Post IPO

Congrats Reddit for getting to where you are today and opening at 38% above the IPO share price!

Follow me on LinkedIn or Twitter.

This post is brought to you by Spiky.ai, a video analysis platform for revenue teams. Their AI models add emotional context to better understand your customers & coach your sales team.

Can AI Startups Get to Defensibility?

AI is the next platform gold rush. But, will AI pioneers survive the Oregon Trail to defensibility?

The journey will be challenging with plagues of high valuations, limited defenses against the elements, and perpetual attacks by new technology developments.

The space is moving so fast and many startups look like wrappers around OpenAI.

What are the keys to survival?

What makes an AI company defensible and durable in the long run?

Where will value accumulate?

NFX, a VC firm that focuses on investing in companies that have network efforts, recently wrote a post, “AI is Like Water.”

Water is three things. Necessary. Ubiquitous. And the same f***ing. thing. inside every bottle.

It’s strange to compare the hottest, world-changing technology to something as simple as water. However, the ubiquity of it is valid.

The article argues that tech differentiation is harder to find and moats are shrinking, or “heading to zero.”

If the underlying tech is no longer a differentiator, then you need to start figuring out how you can build a durable business.

To answer that question, below are 5 must-read articles and key takeaways:

Subscribe now

AI Defensibility Musings from 5 VC Firms

1. CRV - How to Build a Defensible AI Startup in 2023

We believe the most delightful innovations in AI are coming from the bottom up, not the top down, as companies like Midjourney, Runway, and Stable Diffusion have captured the public imagination. Open source horizontal models like ChatGPT will form the foundation of the AI boom, but many more venture-scale businesses will be built on the application layer.

Key Takeaways:

• Ship quickly and prioritize ruthlessly

• Build novel, best-in-class interfaces

• Build community

  • “Successful products in the AI space can start out looking like consumer companies.”

• Go vertical

  • Startups can win initially by moving quickly, but long-term moats are built by going where big tech and incumbents won’t. In a world where anyone can access LLMs, verticalized solutions based on a deep understanding of a target persona is one of the best defensible ways to build in AI.

The only truly defensible items in the application layer are workflow, product, community, and rapid iteration.

2. Greylock - The New New Moats

Why does it feel like there are “no more moats” to build? In an era of cloud and open source, deep technology attacking hard problems is becoming a shallower moat. The use of open source is making it harder to monetize technology advances while the use of cloud to deliver technology is moving defensibility to different parts of the product. Companies that focus too much on technology without putting it in context of a customer problem will be caught between a rock and a hard place — or as I like to say, “between open source and a cloud place.”

Key Takeaways:

• Defensible business models can still be built around IP (these are foundation models)

• There are “Systems of Record” (CRM, HCM, ERP), “Systems of Engagement” (Slack, Amazon Alexa), and “Systems of Intelligence” (combining data sets and providing insights)

• 3 major areas where you can build a “System of Intelligence”

  • Customer-facing applications

  • Employee-facing applications (HCM, ITSM, etc.)

  • Infrastructure systems (security, compute, monitoring, etc.)

• The battle is moving from the old moats (the sources of data) to the new moats (what you do with the data).

What makes a system of intelligence valuable is that it typically crosses multiple datasets and multiple systems of record. One example is an application that combines web analytics with customer data and social data to predict end user behavior, churn, LTV, or just serve more timely content. You can build intelligence on a single data source or single system of record, but that position becomes harder to defend against the vendor that owns the data.

For a startup to thrive around incumbents like Oracle and SAP, you need to combine their data with other data sources (public or private) to create value for your customer. Incumbents will be advantaged on their own data. For example, Salesforce is building a system of intelligence, Einstein, starting with their own system of record, CRM.

3. NFX - The AI Startup Litmus Test

We were recently at a demo day where three companies sounded promising – then we looked at the open source libraries that they had just built skins on top of. Right now it can be tricky to know what is hard, unique, and defensible – versus a weekend project built on top of an open source project. Founders and investors, especially during a moment of exponential growth and consensus, need to run through a few litmus test questions when they are evaluating the next generative AI company.

Key Takeaways:

• If you took out references to AI in your pitch deck, is this still a good business?

• Businesses heavily dependent on generative AI are easily copyable. An unparalleled user experience; however, can help you here.

• Unless you have billions of dollars, it’s going to be incredibly hard to stay on the cutting edge of AI research. This means you need to focus your time on what can be built on top of the foundation models.

The Litmus Test Questions:

4. Sapphire - Vertical(ai) is the New Horizontal

As we consider where to invest, we’re having debates internally and with founders about how companies build moats and defensibility when many are using the same underlying foundation models.

Key Takeaways:

• Defensibility comes from having access to proprietary data sets that are difficult to replicate. Flywheels can help accumulate more & better data from customers that can be used to fine-tune those specific LLMs. It’s easiest to do this with vertical software.

• Law and healthcare AI startups are good examples of companies leveraging “hard-to-reach” data sources. Sector-specific data is key.

• Vertical software in general faces less competition

5. Wing - Leveraging Large Language Models to build a defensible startup

If you’re a startup building AI-native applications with LLMs, there are a few considerations that broadly relate to product approach. In general, you’ll want to think about how to build defensibility in relationship to the models and incumbent applications.

In this article, we’ll take a deep dive into these primary concerns, and discuss how you can strategically build your competitive advantage.

Key Takeaways:

• Need to provide enough value on top of the model layer so that you aren’t commoditized.

  • At a high level, the range of options for startups is (from easiest to hardest):

    • ‍Prompt engineering only: Focus on improving the model output by engineering the prompts used in the models, and by potentially selecting different vendors for various prompts.

    • ‍Fine-tuning: Improve the model by fine-tuning it with feedback and input/output data from a dataset or real usage.

    • ‍Train your own models: Train highly specialized models for specific use cases using all the data collected from the application in production.

  • Incorporating private data (or simply unique data from customers) will help to increase switching costs

• Most incumbents will be adding AI, so your solution needs to be more than than just a feature. It’s gotta go deeper and use AI better in that category

4 Approaches to building defensibility:

1. Add AI value on top of the LLMs for your use case through prompt engineering and fine-tuning — but think about whether that value will still matter once the models improve.

2. Incorporate private data/customer data in the model context to improve outputs.

3. Assume that incumbents in your space will adopt surface-level generative AI features and think about how you can go beyond their ideas.

4. Think about the right insertion point for your product and try to go deep into workflows while minimizing disruptions — while still focusing on bringing out the full value of AI.

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Summary of Strategies

👉 Go vertical - know the domain better than anyone else

👉 Private/customer/hard-to-reach data + data capture flywheels for unique data

👉 Build community + network effects

👉 Build systems of intelligence (incorporating multiple datasets from different sources for value delivery)

👉 Killer UI/UX experience

👉 Train your own models

You can also Follow Me on Linkedin.

Some fail, go sideways, and few become extremely successful. The latter can be the most rewarding experience of your career.

I've been fortunate to be a part of 2:

Acquia (15th employee. $1B exit) and Snyk (80-something employee. 2024/2025 IPO candidate). The other 4 Series A startups: 1 got acquired quickly, 1 made a pivot, and 2 are still in their early days. Learned a ton at each one!

Some of it is luck. Some of it is evaluating the right characteristics.

I use the SIMPLE framework to:

1. Evaluate the company that I’m

   1. Looking to join.

   2. Considering an angel investment.

2. Formulate better interview questions.

Reversely, Founders, GTM leaders, and recruiters can use the framework to tell a more compelling story to attract & hire better candidates.

The SIMPLE framework is not exhaustive - but covers the key points and is hopefully easy to remember.

In this post we’ll go over:

1. The SIMPLE Framework

2. The Characteristics

3. The Questions to Ask

This is part 2 of my Ultimate Sales Interview Playbook:

1. 13 Strategies to land more interviews

2. [This Post] A framework to evaluate startups

3. Questions from 50+ interviews (upcoming)

4. Challenges & homework assignments with templates (upcoming)

5. Strategies to standout & drive urgency (upcoming)

The SIMPLE Framework

S.I.M.P.L.E. stands for:

• Signals - Early signs of traction.

• Innovation - A differentiated product & GTM approach.

• Market - Beachhead and size of market opportunity.

• Problem - How painful the problem is.

• Leadership - Founder-Market-Fit, investors, supporting circle.

• Execution - Signs they are delivering on their vision.

Inspecting each area gives you a better idea of the likelihood of success.

The Characteristics

You’re ultimately trying to find a company that has the right ingredients that optimize chances of success. Let’s dive into each one:

Signals

Revenue & growth are the true north stars.

However, if you are going in early (Seed / Series A), there’s not much data (or revenue) to go on.

So you need to look at other signals. There are many, but here are some examples:

• Adoption:

  • Users

  • Downloads

  • MAU (monthly active users)

  • GitHub stars (for open-source projects)

  • Initial community.

• Investors

  • Have they attracted funding from Tier 1 VCs

  • The caliber of angel investors & advisers

• Tail/Head Winds

  • What market conditions help you to sell

  • What market conditions make selling harder

• Talent

  • Where did execs come from?

Questions to Ask

• Where are you seeing the most traction & why?

• What signals are you seeing that you have found Product-Market-Fit or on the path to it?

• How do you know customers are realizing value in your product?

Innovation

Innovation is more than just technology invention.

Today, it is harder to differentiate and build defensibility with technology alone.

True innovation is coming a differentiated product with a differentiate GTM strategy (think distribution, too) with the right timing.

Tomasz Tunguz, a Venture Capitalist at Theory, shared a formula for innovation in one of his blog posts:

Innovation = Invention + Go To Market + Timing

Timing is hard to assess, but you can come up with some hypotheses about why timing is good. This also goes back to headwinds and tailwinds.

Companies may not have defensibility from day 1.

They do need a plan to build it over time.

Market

Marc Andreesen says this is the only thing that matters (when compared to Team & Product, the obvious ones).

In a great market—a market with lots of real potential customers—the market pulls product out of the startup.

The market needs to be fulfilled and the market will be fulfilled, by the first viable product that comes along.

The product doesn’t need to be great; it just has to basically work. And, the market doesn’t care how good the team is, as long as the team can produce that viable product.

In short, customers are knocking down your door to get the product; the main goal is to actually answer the phone and respond to all the emails from people who want to buy.

And when you have a great market, the team is remarkably easy to upgrade on the fly.

How I think about it:

• Total Addressable Market (TAM) - Total market.

• Serviceable Available Market (SAM) - Where you can expand.

  • By adding product.

  • Tangential markets.

• Service Obtainable Market (SOM) - Who you can sell to now,

  • Outside of your EVG.

  • Your beachhead.

• Earlyvangelists (EVG) - Family, VC intros, your earliest adopters from your network.

Questions to Ask

• How are you differentiating your GTM approach to break through the noise in the market?

• What tailwinds are you feeling that signals the timing is right for you?

• What is your beachhead & how do you expand?

Problem

Painkiller or vitamin.

This market is all about must-haves (the painkillers).

The MEDDICC framework has a good way to think about pain. Enterprises have 3 types. What type of pain is the startup alleviating?

1. Financial Pain

   1. High operational costs

   2. Revenue losses or stagnation

   3. Inefficient capital allocation

   4. High customer acquisition costs

   5. Regulatory compliance costs

2. Efficiency Pain

   1. Process inefficiencies

   2. Inadequate data management & utilization

   3. Engineering inefficiencies (too much time fixing bugs)

   4. Technology gaps

   5. GTM inefficiency

3. People Pain

   1. Recruitment & retention challenges

   2. Lack of team collaboration

   3. Communication breakdowns

   4. Employee disengagement

   5. Skills & training deficits

How does the solution solve one, two, or all of these?

Questions to Ask

• What customer metrics do you help improve?

• What happens if the customer doesn’t improve those metrics?

• How do you make the business case for your solution?

Leadership

There are 3 groups of leaders to look at:

• Founders - Is there “Founder-Market-Fit”?

• Executive team - Have they scaled similar companies before?

• Investors - Are they specialists in the specific domain?

  • These could be VCs or Angel Investors.

NfX - a Venture Capital firm that focuses on network effects has a nice framework to think about Founder-Market-Fit:

1. Obsession

   1. Iconic companies almost always have obsessive Founders at the helm.

   2. A sign of health obsession is knowledge.

   3. They are a student of the market: trends, competition, why previous companies failed, etc.

2. Founder Story

   1. “Customers have to identify with the Founder’s story and believe that there’s a compelling “why” inside the Founder - that there’s a human behind the company.

3. Personality

   1. Markets attract similar personalities. Does the Founder’s personality fit in?

4. Experience

   1. Industry experience, but not too much. Too much might mean they don’t have any disruption left in them.

Questions to Ask

• What’s the story behind why you’re building this product?

• What market or customer insights are you leveraging to build your product/company?

• How are you becoming a thought leader in your category?

Execution

“Execution is everything.” John Doerr

Sam Altman thinks about execution and evaluating execution in the following way:

A sign of good execution is progress.

You can measure progress with metrics.

The actual metrics and results against those metrics can tell you a lot about:

• What the company is focused on.

• Are they making progress toward hitting those goals?

• How do you really know they are finding PMF.

• How fast are they learning, iterating, and shipping?

Questions to Ask:

• How do you know your strategy is working?

• What metrics are you using to track success?

• What’s holding you back from growing faster?

🤞🏼That’s a wrap.

Next up in the Ultimate Sales Interview Playbook:

1. Questions from 50+ interviews (steal and use for either side of the table)

2. Challenges & homework assignments with templates

3. Strategies to standout & drive urgency

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The Ultimate Sales Interview Playbook - Part 2

This is part of my Ultimate Sales Interview Playbook series that I’m assembling from experience. The following strategies have helped me land interviews in the past several months without applying to job postings. Yes, you still gotta play that game - but it’s absurd, some of those postings have 100-200+ applicants! How the hell can you stand out?

In upcoming posts, I’ll be writing about:

• A framework to evaluate startups

• Questions from 50+ interviews

• Challenges & homework assignments (templates provided)

• Strategies to standout & drive urgency

13 Strategies… LFG.

The VC Playbook

VCs differentiate by providing value-added services on top of the capital that they provide to their portfolio companies. Recruiting is one of these services and you can create a win/win for you and them.

This is how I’ve partnered with them… both myself and to send them candidates when I have friends looking.

Strategy 1: The VC Referral

If you’ve worked at a venture-backed company, reach out to the previous investors. Provide a summary of your success at that company and point out which portfolio companies & roles are of interest. (They all have job boards.) The referral from the VC to the company can easily put you at the top of the resume list.

Hi Brad,

I worked at your portfolio company Snyk for 3 years in sales.

Your portfolio & job opening [insert posting] caught my attention. [Insert VC name]’s investments are always the most interesting for your thesis around developer tooling.

My background in the developer tooling and security spaces would be a good fit at [insert company], especially contributing insights from the GTM motions at Snyk.

Would you be able to connect me to [insert name] over there?

Thanks,

Alex

Best done over Linkedin.

Strategy 2: VC Slack Communities

I’ve posted resumes for friends in the “available talent” channel several times, resulting in job offers at their portfolio companies. This one is a bit harder to uncover, but if the firm has a community manager, then it’s likely they are doing something along these lines. This works best for VCs that build local networks.

The Underscore.vc team does a fantastic job of making connections within the Boston startup ecosystem. I’m part of their Sales Core and community if you’d like help.

Strategy 3: Who’s performing best?

You can then ask for an intro and go directly to the company and reference who pointed you in that direction. It’ll also help you optimize for a faster-growing startup and a company that has a better PMF.

The Outbound

If you’re in sales… this is a great way to demonstrate your PG skills. You know what I’m talking about… cold outbounding. It still works.

Strategy 4: Connect with the leader of the department on LinkedIn.

Then send a brief note explaining your background and hypotheses as to why you’d be a good fit. Good leaders are constantly building their network ahead of open positions. This is actually how I landed a job at Snyk… there was no position listed, but the VP of Sales was interested in a conversation and he knew there’d be positions opening up.

Strategy 5: Do the same thing as 4, but do it at the Founder/CEO level.

The goal here is to get referred to the right person and mention who you sent your way.

Strategy 6: Send your territory plan.

You’ll likely have to build one at some point if you’re in sales. I highly doubt that 99% of applicants proactively send a customized deck to the company. 80% can likely be templatized for easy reuse.

The Bottoms Up

Strategy 8: Informational interviews with the team.

This approach starts by reaching out to team members you’d be working with. Informational interviews can provide you with better insights. Then apply to strategy 4 or ask to get that person to refer you. There’s often a cash bonus in it for them. I typically frame these as “I saw your company is hiring. I’m doing my homework and would love to learn more about what it’s like to work there.”

Recruiter Networks

Strategy 9: Leverage outside recruiters.

Many markets, like cybersecurity, have recruiters that just focus on that domain. I’ve found recruiters have access to job openings that aren’t on LinkedIn or other places. They can also help with job market insights, and how companies are doing, and help point to companies you haven’t discovered yet. You might be missing out if you don’t work with them.

Strategy 10: Ask recruiters where they have connections.

(They don’t have to be under contract, they’ll likely still help you if they’re playing the long game.)

Similarly to sales, recruiters are looking to build relationships and prospect into new accounts. A recruiter that I’ve been working with was open to introducing me to a company that they knew but wasn’t formally under contract. The recruiter may not get any commission, but they’ve added value and built a stronger relationship. Find the right recruiters who play the long game.

Your Network (duh)

Strategy 11: Reach out to your network & former colleagues.

Have them check with their leaders to see if any openings are coming up soon. You don’t have to see a job opening to inquire.

Strategy 12: Write about your experience in public.

I wasn’t expecting this one… but openly posting about this very topic helped start several new conversations and reopened others when I posted I’m doing this series.

There’s something to being vulnerable in public and showing who you are.

I’ll be writing about a framework I use to evaluate startups (that will also help inform questions you can ask to interview the company.)

Happy hunting and good luck out there. Keep your head up.

My goal is to share everything I’ve learned while interviewing over the last couple of months during my career break. Hopefully, this can help folks on both sides of the table sharpen up.

This deep dive will have several posts over the next couple of weeks:

1. Strategies to land interviews

2. A framework to evaluate startups

3. Questions from 50+ interviews

4. Challenges & homework assignments (templates provided)

5. Strategies to standout & drive urgency

To get things started, I’m sharing some of my favorite questions that I ask companies as I’m evaluating the opportunity.

After all, companies interview you.

And, you need to interview them.

Questions also demonstrate curiosity and how you think.

Below are my 5 favorites that weave into conversations. Yes, the first 3 come from the MEDDICC playbook’s 3 whys; Why do anything? Why choose Startup, Inc., Why buy now?

5 must ask questions

1. What causes your customers to do anything?

2. Why do your customers choose you over the competition?

3. What propels your customers to buy now vs. put off the decision?

4. How will you build defensibility over time?

5. How is your GTM strategy differentiated to break through the noise?

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The world is either being taken apart or being put together.

Bundling can be a successful business model in software development and this is happening in every domain in the form of “unified APIs”. This is simply due to the explosion of API-first companies and the growing need for new technologies & SaaS solutions to connect with others.

The growth of API-first companies has led to the need for easier ways to integrate with more APIs. This is where unified APIs come in. These are companies like Stripe, Plaid, and Rutter, that bundle access to a group of APIs.

In this post, we’ll go over the following:

1. A brief history of APIs

2. The strategy

3. Why it matters

4. Unified API companies

5. Continued reading

   ---

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A brief history of APIs

What is an API?

In the words of ChatGPT for an easy-to-understand definition:

API stands for Application Programming Interface.

An API can be thought of as a mediator that allows two different applications to talk to each other. For example, when you use a social media app on your phone to display weather information, the app uses an API to retrieve the weather data from a remote server. This enables the seamless integration of features from one service (weather information) into another (the social media app) to enhance the user experience.

Or, you can just think of APIs as providing building blocks for applications.

Early days

• Point-to-Point Integrations (2000s): APIs initially emerged as point-to-point solutions, allowing one application to connect directly with another. Building and maintaining these custom integrations was time-consuming and expensive, hindering wider adoption.

• API Management Platforms (2010s): API management platforms like Apigee and MuleSoft streamlined API development and deployment, but integration complexity remained a challenge, especially for businesses using numerous applications.

The dawn of unified APIs

• SaaS Explosion and Integration Fatigue (2010s-2020s): The proliferation of SaaS solutions led to "integration fatigue," with businesses struggling to manage the growing web of connections. This paved the way for a new breed of unified API companies.

• Vertical-Specific Solutions (2020s onwards): Unified API companies emerged, focusing on specific software categories like CRM, HRIS, or accounting. These platforms act as abstraction layers, providing a single API endpoint to access data and functionality from multiple vendors within that category.

The strategy

Bundling is the practice of grouping multiple products or services and then selling them as a single unit at a combined price. It can be a product and/or a pricing strategy.

A unified API bundles many APIs and provides access to those services via a single API. For example, if you want to build a FinTech app, you may want to integrate with many of the e-commerce sites for your service. Instead of building all of those integrations yourself, you can leverage a bundled API to integrate with all of those services from one source. It’s an abstraction that reduces repetitive tasks, and effort, and helps to accelerate time to market.

An example in our daily lives is Amazon Video. They bundle access to different subscriptions and access to various channels all through one interface.

The business model is being applied to almost all industries from finance to automotive to AI.

There are numerous benefits to customers who consume a unified API:

• Reduced engineering effort to build integrations

• Lower operational drag (reduced engineering for maintenance)

• Reduced complexity (abstracting away differences of each service)

• Reduced effort to learn different platforms that have numerous nuances

• Reduced effort to maintain access to many services or platforms

• Accelerated time to market for new products

• A better developer experience

Unified APIs typically have the following characteristics:

• Vertical & horizontal approaches

• Typically designed for developers

• They normalize data

• Have a common interface

• Language or platform agnostic

Why it matters

We’ll continue to see more unified APIs brought to market for 3 reasons:

1. New platforms (think Gen AI) spur the creation of new vendors, which bring new APIs to market.

2. New APIs within verticals present opportunities to apply the business model.

3. Applications continue to become more interconnected

For example, if you are building an AI application. You may not want to rely on just one LLM for the ‘brains’ of your application. New LLMs will continue to be birthed and people will want that optionality, thus providing an opportunity to build a unified API and commercialize it into a company.

Or, perhaps… AI will just write the integrations for us.

This will lead to new investment opportunities for angels and VCs.

Understanding the model and where they are, or aren’t, will help you spot new investment opportunities.

Unified APIs provide a way for legacy vendors to play with new vendors.

This is simply through abstracting away the clunkiness or complexity of those vendors by providing a more modern experience via the new bundled API interface.

Integrations are often a significant requirement when evaluating SaaS vendors.

SaaS companies will continue to adopt unified APIs to ship integrations faster to stay competitive & to increase stickiness, thus positively impacting NRR.

Value creation through application networks connected through APIs

The largest transformations in the world economy have often been the result of network effects. Throughout human history, remarkable progress has been made by networks: railway networks in the 1800s; telephone networks in the 1900s; computer networks in the 1980s; internet networks in the 1990s; and social networks in the 2000s. Networks are so powerful because every new node within a network increases the value of the entire network since the data and capabilities of that new node are discoverable and consumable by all other nodes in the network (Metcalfe’s Law). A few years ago, we at NEA began investing in a new network: the application network. We expect application networks to transform enterprise software and create a tremendous amount of value for businesses, consumers, and users alike. - NEA & Mulesoft: the rise of application networks - NEA

Companies in the Unified API movement

Below is apideck’s website for tracking unified API providers at unifiedapis.io and several company examples. I was not expecting to find companies in the construction or automotive markets.

Unified API Market Map

Financial services - Codat

Software development - Nango.dev

Artificial Intelligence - Grafbase

Security - Pangea Security

Human Resources - Finch

Education - Edlink

Construction - Agave

eCommerce - Rutter

Automotive

Travel - Duffel

Continued reading

Unified APIs, building connections - Activant Capital

What is a unified API? - apideck

Explaining the rise of unified APIs - Anthony Lagana

Finding the best unified API - Nango.dev

NEA & Mulesoft: the rise of application networks - NEA

That’s the latest question in tech.

AI technologies develop so fast that many question whether AI-first companies can build real moats. A new "10X" startup today, may not be that impressive tomorrow.

Value creation doesn't always mean value capture.

Example: many companies who have built the "ChatGPT for X" may no longer have a product advantage because now OpenAI has plugins and the equivalent of an "App Store."

That week even raised the question of whether foundation models are defensible. A team of Stanford students took a very clever approach of starting with LLaMA 7B, an open source LLM, and fine-tuning it on 52,000 instruction-following demonstrations from OpenAI’s text-davinvi-003. The resulting LLM, dubbed Alpaca 7B, can run on a MacBook and has roughly comparable performance to OpenAI’s GPT-3.5, which relies on massive cloud compute. It took OpenAI 4.5 years and over ~$1B raised to launch GPT-3. Alpaca? Training costs totaled less than $600 (not a typo). It’s a very big deal. -

In this post, we'll look at:

1. The strategy at play: build a device, not an app

2. Why it matters for defensibility

3. 3 additional examples of AI wearable startups

First, what is a moat? competitive advantage? defensibility?

• Competitive advantage - The leverage a business has over its competitors.

• Defensibility - The ability to maintain a competitive advantage.

• Moats - The economic or market moats represent long-term defensibility.

The Strategy

The first wave of hardware tech with AI started a decade ago with smart speakers such as Alexa and Google Home. The 2nd wave is now wearables with AI.

The strategy - build a device and accompanying software instead of another app.

Let’s take a look at Rabbit, your new AI pocket companion that decided to build a wearable instead of an app.

This was a strategic business decision for them with a significant driver being defensibility.

The strategy helps make AI-first companies more defensible while simultaneously opening options for how the technology is used. New hardware = new possibilities. Hardware = harder to build = harder to copy.

Why it Matters

AI-first startups are hard to defend as we outlined before. I believe we'll start to see more personal devices and AI delivered via hardware in general as AI proliferates.

Hardware is more defensible for the following reasons:

• Higher barrier to entry/copy - Building hardware & software is more complex, harder, and requires additional skill sets, thus more defensible.

• Reduced competition - The Rabbit won't need to compete with every app in the App Store. Though, the trade-off to this is competing for space in your pocket.

• Higher switching costs - Switching to another app is easier and less expensive compared to switching hardware.

• Optionality - Specialized or purpose-built hardware could be added that Apple may not want to add to the iPhone. Thus, other apps would be limited to the hardware that Apple can provide. For example, Hu.ma.ne's device has a small projector that displays information in the palm of your hand.

• Better control of IP (Rabbit’sLarge Action Model) - Apple won't be able to see Rabbit's code.

AI requires a new approach to how we interact with the technology.

Examples in the Wild: AI Wearables

Rabbit

A personal AI device that leverages LAM (Large Action Model) to understand human intentions and to get things done for you.

WHAT!?! Thought we were all talking about LLMs...

Here's the difference - with most AI apps, you provide an input and you get an output with one app. Ex. "What are the best interview questions to ask "

But, what if you want an output that requires interacting with multiple apps and steps? Ex. "I'm planning a business trip to San Francisco. Please book the optimal plane ticket (based on time, convenience, and cost), and hotel, and prearrange an Uber pick-up to and from the airport. Update my calendar with the itinerary. "

This would require interacting with multiple apps: Airline, Expedia (for hotels), Uber, Google Calendar...

Rabbit uses a LAM to operate apps on your behalf. You don't have to open those apps or do anything else, just push a button and give it instructions in natural language - like a walkie-talkie. The cool part about this is that you can also teach it new actions.

The Rabbit is AI hardware that replaces app-based operating systems by introducing a new experience between humans and machine.

The Numbers:

• Funding: $30M

• Cost: $199

• Subscription: TBD

• Total revenue: $4M (20,000 sold as of 11/11/2023)

• Investors:

You can check out the announcement and pitch here.

Rewind AI Pendant

Rewind is a personalized AI powered by everything you've seen, said, or heard as you use your computer. (Note: I'm a small angel investor.)

Rewind currently has a desktop app that records everything on screen, heard, or what you've said. The data is compressed and stored on your computer. You can ask Rewind.ai about anything you've captured. Ex. "What was the topic of conversation and next steps with Johnny back in March?"

The Pendant extends this capture ability to your person so you can rewind on a greater surface area of captured data around what you do.

The Numbers:

• Funding: $33M

• Cost: $59

• Subscription: $19/month (if paid annually)

• Total revenue: not disclosed

• Investors: First Round, Andreessen Horowitz, NEA

Humane AI

Hu.ma.ne’s AI Pin is built by ex-Apple designers and engineers and is taking a different approach. Eliminate the screen and use a projector instead.

Not only is there a projector from a differentiation standpoint, but you can also use gestures to control the device in addition to spoken language.

AI gets to know you and helps filter information based on what's most important - like trusted vs. non trusted contacts.

There's a ton of additional use cases:

• Take photos of the moment

• Scan a fruit to get nutritional value

• Messaging

• Interpreter

The Numbers:

• Funding: $230M

• Cost: $699 for base model

• Subscription: $24/month (if paid annually)

• Total revenue: not disclosed

• Investors: Kindred Ventures, Microsoft, Volvo Cars Tech Fund, Tiger Global, Qualcomm Ventures, and OpenAI CEO, Sam Altman

Tab

Tab has the same use case as Rewind AI's pendant, but comes in a much nicer aesthetic.

Listen, transcribe, and then provide insights that are specific to and for you.

The Numbers:

• Funding: $1.9M seed

• Cost: $600

• Subscription: $50/month

• Total revenue: $100,000 in orders so far

• Investors: Caffeinated Capital, Rief Capital, Cory Levy, and other notable investors such as Perplexity AI's Aravind Srinivas.

Key Takeaways

1. We’re in the early innings of AI Wearables. There will be much more to come.

2. Startups are using new devices to weave AI into our daily lives and make the tech behind it more defensive.

3. Hardware is more defensible for many reasons; optionality, additional complexity to copy, and more expensive switching costs.

4. AI is enabling new types of devices and services.

5. AI wearables are selling, but they still have a long way to go before they reach the mass market

What I’m Reading

Defensibility in the Age of AI

How to Build a Defensible AI Startup - CRV

Rabbit sells out two batches of 10,000 R1 pocket Ai companions over two days - The Verge

YouTube: Introducing r1 - Rabbit

GTM Foundry is for anyone bringing a new product or technology to market. We cover strategies, frameworks, and trends that shape the technology & GTM frontier. Subscribe for semi-weekly posts.

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Palantir’s Bootcamp GTM strategy is genius.

They recently highlighted their bootcamp strategy on their latest Q4 quarterly earnings call. It’s their primary driver for customer acquisition.

But first… If you’re not familiar with Palantir…

Palantir makes products for human-driven analysis of real-world data.

The AI operations software is best known for its help in correlating massive unstructured data sets to find Osama Bin Laden.

The company was founded by Peter Thiel who wanted to apply fraud recognition software (the kind that was developed at PayPal) to fight terrorism. Initially, they were focused on providing capabilities for the intelligence, defense, and law enforcement sectors. Now they are helping public and private enterprises adopt and scale their advanced AI platform (AIP for short).

Over the past 2 years, they’ve been scaling their latest GTM strategy - AI Bootcamps.

The Bootcamp Strategy

Palantir is using Bootcamps for top-of-funnel demand gen and to land & expand faster with new & existing customers.

Alex Karp, CEO, explained in the earnings call that AIP bootcamps are designed to challenge customers to compare what they developed internally (no matter how many resources or years) to running their data on Palantir’s offerings for 10 hours to see the difference.

Palantir’s blog, Deploying Full Spectrum AI in Days: How AIP Bootcamps Work, explains:

Palantir Artificial Intelligence Platform (AIP) enables new and existing customers to dramatically accelerate high-value AI use cases. To meet the intense requirements of the moment, we have introduced AIP Bootcamps. In these immersive, hands-on-keyboard sessions, participants can expect to go from zero to use case in just one to five days.

The results speak for themselves. Our partners have been able to solve problems that they have been struggling with for years in a matter of days. They are able to build their own intuition and skillset to be able to independently identify and execute on AIP use cases in subsequent weeks. AIP Bootcamps are also fun for all involved, as winning tends to be.

Bootcamps = the new “pilots.”

End of 2022 - 92 completed for the year.

End of 2023 - 500+ completed for the year with over 465 organizations participating.

The Bootcamps are run by Palantir and their partners such as Accenture.

Bootcamps are used to accelerate and ease AI adoption for both new and existing customers. For example, Alex Karp explained that they landed a $3M contract via outbound prospecting, then deployed a bootcamp, and at the end of the same quarter expanded the contract to an enterprise-wide agreement likely to be significantly larger than the $3M. That is fast!

Why The Bootcamp Strategy Matters

Ultimately, Palantir is providing an exciting way for customers to try powerful software while shortening time-to-value and use case development for the business case.

The strategy is successful for a few reasons:

• Reduces friction in the buying process.

• The pitch goes from theoretical (slide decks) to real-life applications.

• Helps to figure out which use cases are most valuable to the customer.

• Accelerates expansion in new & existing customers.

• Helps to cross-pollinate & increase discovery of Palantir products, which leads to selling multiple products in the first sale, and thus higher TCV.

Participants have been able to “solve problems that they have been struggling with for years in a matter of days.”

“Demand is off the charts for AIP and bootcamps are the delivery mechanism.”

How Palantir Integrates the Bootcamp into the Sales Process

Step 1: Identify impactful use cases to unlock

Step 2: Deploy an MVP with your data in a Bootcamp

Step 3: Demonstrate ROI by developing workflows for production

Step 4: Scale deployment across organization

Step 5: Continuing building the AI-powered foundation for the customer

The Feedback

It’s clear bootcamps are generating buzz and excitement around Palantir’s products:

A Bootcamp Trend?

While there are plenty of virtual bootcamps, only some follow the Palantir model of doing them in person.

It looks like Snowflake has taken note and is doing an LLM Bootcamp:

Learn how to use generative AI to create natural language interfaces for your data and apps. In this LLM Bootcamp you’ll set up a pre-trained, open-source large language model, fine-tune it with private data, build an app to interact with it, and deploy it to production—securely and with proper governance.

I’d love to find other bootcamps to add here as examples. Have you come across any? Drop them in the comments.

It can also be used be used to define company strategy, which Jack Dorsey, Founder of Twitter, did when he returned to the company in 2015:

Jack Dorsey, Founder of Twitter, explains why he used it in 2015 when he returned to the company:

“It cleared something up that was missing for me, which was how do we plan and build around a customer-centric framework that would focus the organization on why our customers are coming to us in the first place” - Jack Dorsey

The result of this is what he described as the company's strategy in Twitter's second-quarter earnings call of 2015:

We intent to build an ecosystem of connected features and services focused on serving three core jobs: news, which is happening now; discussion, conversation, and helping people get paid." - Jack Dorsey

He developed this strategy by following these steps:

1. Understand the full set of jobs for which Twitter was already being "hired."

2. Prioritize the jobs Twitter wanted to focus on.

3. Communicate the results and use them to allocate resources.

In this post, we’ll go into greater detail and go over:

1. The framework

2. Why it matters

3. JTBD applied at GitLab

4. ChatGPT prompt you can steal

The Framework

Clayton Christensen, who wrote “Crossing the Chasm”, defines the framework at his institute as the following:

It’s a framework to think about customer behavior and priorities, breaking them down by framing consumption as “jobs” to be accomplished. The job architecture is made up of three levels: the job itself, the experiences in purchase, and the integration of resources.

Tony Ulwick describes it as:

A framework for categorizing, defining, capturing, and organizing the inputs that are required to make innovation predictable.

Before diving into these “jobs”, you first need to define who your customer is. There are generally 3 types:

• The job executor - the person who ‘hires’ the product to the ‘job’ done.

• The buyer - the person responsible for the financial purchase & decision.

• The product lifecycle support team - the groups of people who support the operations of the product such as installation, integration, maintenance, etc.

These people have 5 types of jobs that need to get done:

Job Executor:

• The core functional job

• Related jobs

• Emotional jobs

The Buyer:

• Purchase decision job

The Support Team:

• Consumption chain jobs

We then finally add the outcomes for each section:

From a sales perspective, the framework helps sellers understand how to tailor value propositions and metrics to the users, buyers, and supporting teams.

Why it Matters

People don’t buy products or services; they hire them to do a job.

1. The JTBD framework helps align the entire company:

• Product - what to build & what matters most

• Marketing - how to position & message to each type of job

• Sales - how to talk about value prop, metrics, outcomes

2. Understanding the “job” helps allocate resources to what matters most.

3. Consistent messaging to the right people can reduce customer acquisition costs and help to build stronger champions.

4. The approach often leads to new insights such as risks, opportunities, and competitive environment through a wider aperture.

Let’s dive into #4 with an example:

How GitLab uses JTBD:

The structure of their personas and incorporation of JTBD is the following:

Each persona in GitLab’s persona page should have the following traits:

• Job Summary - Should include major focus areas and a general description of skills and responsibilities

• Alternative Titles

• Motivations - It’s suggested to keep to this format: When [situation], I want [feature] so that I can [task]

• Frustrations - A frustration should focus around a common blocker to their responsibilities

• Jobs to be Done

(Optional)

• Personal Skills & Traits - Positive attributes that give life and empathy to your user persona

• Key Tools - Important software that helps the user complete their tasks

• Workflows - A series of steps the user will normally take to complete their tasks

• Collaborative Teams - Teams in their organization that they may have overlap in responsibilities with or are dependent on in their workflow

The following are some examples:

• Platform Perry

• Compliance Cooper

GitLab takes the framework further by then mapping the personas and their jobs to the different stages of the software development lifecycle. This gives the right context so you can talk to a person about how their specific job is accomplished within their role in the software development process.

ChatGPT Prompt to Steal: Persona Buildout

Instructions: Copy and paste the full text below into ChatGPT and replace the [ ] to specify your directions.

I'm a [Your Title (ex. Product Marketer] and I'm using the "Jobs to be done" framework to build out user personas to [Purpose (ex. educate the sales team)]. Each user persona should include the following as the output:

1. Job summary
2. Titles of who this persona typically reports to
3. Alternative titles of the persona
4. Motivations of the persona
5. Frustrations of the persona
6. The jobs that the persona needs to get done
7. Typical tools or software that is used by the persona

For #6, include 3-5 examples. Each example should have the following:

1. The job
2. Description of the job
3. Metrics for measuring the success of that job
4. The challenges to achieve the job or improve the metric Do this for the persona [Persona (ex. “Cloud Architect”)]

Then create a table that summarizes all of the information.

What I’m Reading

How Twitter Applied the “Jobs to Be Done” Approach to Strategy - HBR

Jobs-to-be-Done: A Framework for Customer Needs - Tony Ulwick

What is an Ideal Customer Profile; 5 Benefits of Clearly Defining ICP & Personas - Unusual Ventures

Success is Hiding in Your Customers Untold Stories - Christensen Institute

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