It’s still early in the year, which means we’re in the thick of Q1 board meetings with our portfolio companies.
These conversations follow a pattern. We start by looking back at 2025 – what worked, what didn’t, what the team learned. Then we turn forward: what does success look like in 2026?
This is where things get interesting.
Most founders come prepared with updates on revenue, hiring plans, product roadmaps, and competitive positioning. All of that matters. But the conversation we actually need to have is simpler and harder: What’s the one thing that needs to be true in December for this year to have been a success?
We see this across the startup ecosystem – from companies at the idea stage to those with meaningful traction. The founders who have clarity on this question make different decisions. They know where to invest time. They know what to say no to. They build momentum instead of motion.
So we wanted to share what happens in these conversations: how we help founders think about goals, why it matters, and what “good” actually looks like when you’re still figuring everything out.
If we’re investing in you at the idea or MVP stage, you are nowhere near having product-market fit yet. You’re experimenting with everything – what to build, who to sell to, how to price it, what messaging works.
That’s exactly what you should be doing. Experimentation is the job.
The problem is that when you’re experimenting with everything, every data point feels important. You measure everything (as you should), but without a framework for what matters, you end up reacting to whatever moved last. One week you’re convinced enterprise is the play. The next week you’re pivoting to SMB. The week after that you’re rebuilding the product because an investor said the UI felt clunky.
This is what it actually feels like to search for product-market fit.
The skill you need to develop while in this phase: learn where to prioritize your focus, time, and energy. Not what to measure – measure everything. But what to actually optimize for.
That’s what a North Star metric does. It’s the one number that tells you whether you’re making progress through the noise.
In the very early days, your North Star is incredibly simple: How many customers have you spoken with?
Not sold to. Not demoed to. Spoken with. Real conversations where you’re learning about their problems, testing assumptions, and figuring out whether what you think matters actually matters to them.
You set a difficult, but achievable goal: Talk to 100 potential customers this month. The number should feel uncomfortable – like you’re not sure you can hit it, but you believe it’s possible if you’re disciplined.
This is the only way to learn what to build. Every conversation de-risks an assumption. Every conversation teaches you something about the problem, the customers who have it, and whether they’ll pay for a solution.
We’ve seen founders across the ecosystem resist this. They want to start building, start shipping, start getting traction. But if you don’t talk to your customers, you’re guessing. And guessing is expensive.
So at this stage, your North Star is simple to define: 100 customer conversations by the end of Q1. Make it bold enough that you have to change your behavior to hit it.
At some point – usually after you’ve talked to enough customers to see patterns – you make a decision. This is what we’re building. This is the product. This is the problem we’re solving and this is how we’re solving it.
The moment you do that, your North Star changes.
You’re still talking to customers (that never stops), but now your North Star reflects usage, adoption, or value creation:
Pick a number that measures whether the thing you decided to build is actually creating value. And make it bold – a number that would meaningfully change your business if you hit it, a number that would make your next fundraise obvious.
This is the shift from learning to validating. You made a bet. The North Star tells you whether the bet is working.
If the number isn’t moving, you might need to adjust what you’re building, who you’re building for, or how you’re delivering value. The metric doesn’t tell you what to change (that’s where customer conversations come back in), but it tells you that change is needed.
This is why the North Star matters. It’s not a productivity tool. It’s a reality check.
Once you have your North Star, you need structure. This is the framework we use:
One North Star. Three supporting goals.
The three goals should answer:
These aren’t separate priorities. They’re the three dimensions you need to move for your North Star to improve.
Early on, your technology goal is about getting to a working product: “Ship MVP and get 10 customers live by Q2.”
As you mature, it becomes about making the product better, faster, or more reliable:
The technology goal should answer: what must be true about our product for customers to get value?
And it should connect directly to your North Star. If your North Star is “15 units in commercial production,” your technology goal might be “achieve 99% uptime in customer environments.” If your North Star is “30 teams shipping code with our SDK,” your technology goal might be “reduce integration time from 2 weeks to 2 days.”
The connection should be obvious.
Revenue is the lagging output. Your GTM goal should focus on the behavior that creates it.
At the earliest stages: “Complete 200 customer discovery calls by June.” You’re still learning.
As you put a stake in the ground: “Convert 20% of demos to pilot agreements.” Now you’re testing whether people will actually try what you built.
As you scale: “Generate $5M in qualified pipeline by Q4” or “Convert 30% of pilots to paid contracts within 90 days.”
Other examples: “Sign 8 pilot deployment agreements with target facilities by Q3” or “Achieve 40% conversion from pilot to production contract.”
The goal should be something you control. Not “close $X in revenue” – that depends on customer timing and budget cycles. Instead: “Run 100 qualified demos by Q4” or “Sign 10 pilot agreements by Q2.”
Those are the inputs. Revenue is what happens if you get the inputs right.
At the idea stage: “Recruit technical co-founder by Q2” or “Bring on advisor with 10+ years experience in target market.”
As you grow: “Hire first sales hire with $5M+ enterprise ARR track record by Q1” or “Hire Head of Field Operations with deployment experience by Q2.”
The team goal should answer: who do we need, and what capabilities must they have, for us to hit our other goals?
If your GTM goal is “generate $5M in pipeline,” you probably need someone who knows how to do that. If your technology goal is “deploy 15 units in production,” you need field operations expertise.
Here’s how we pressure-test goals:
The number test: Is your North Star actually a number? “Get more customers” isn’t a North Star. “50 active production customers” is.
The bold test: Does your North Star feel ambitious? If you’re 90% confident you’ll hit it, it’s not bold enough. Aim for 60-70% likely – possible but not guaranteed.
The causation test: If you hit all three supporting goals, would your North Star obviously improve? If not, you picked the wrong goals.
The control test: Are you measuring things your team can directly influence?
The evolution test: Do these goals make sense for where you are right now? Or are you setting goals for the company you wish you were?
We see founders across the ecosystem set goals that would make sense for a Series B company when they’re still pre-seed. They want to “achieve $5M ARR” when they haven’t validated product-market fit. They want to “hire a VP of Sales” when they don’t have a true customer pull or repeatable sales motion yet.
Your goals should reflect reality. Where you are. What you need to learn. What’s actually blocking you from the next stage.
By December, you’ll have run dozens of experiments. You’ll have tried features, tested messaging, explored customer segments, iterated on pricing.
Most of those experiments will fail. That’s startup life.
The founders who succeed aren’t the ones who never fail. They’re the ones who can recognize the failures quickly, who can look at all the noise and say: “Here’s what we learned. Here’s what actually moved the needle.”
Your North Star tells you which experiments created value. Your three goals tell you whether your actions are actually moving you forward.
Here’s the exercise:
Our North Star for 2026 is [specific metric and number].
To get there:
If you can write that paragraph clearly and all the pieces connect, you have goals.
If you can’t, you have a to-do list.
It’s still early in the year. Get your goals set right.
We’re witnessing the most consequential shift in enterprise software since the cloud migration. As compute costs plummet and GPU access becomes commoditized, a massive value capture opportunity is evolving in enterprise applications. Meta’s recent multi-billion dollar stake in Scale AI (a company we backed with their first VC check in 2016) signals what insiders already know: while infrastructure consolidates, the application layer is wide open for disruption.
But here’s the challenge every enterprise AI founder faces: navigating the fundraising maze at the idea stage. You’ll pitch dozens of firms, get enthusiastic meetings, only to hear “we love this, but you’re too early for us.” Many funds simply don’t write checks until seed or later, regardless of how compelling the opportunity.
The Outlander VC team has been backing idea-stage startups for 15 years. So, to help founders out there building towards an amazing vision…who want to know which VCs are actually cutting the early checks, we’ve put together a list of twenty-five VC funds backing enterprise AI companies at the pre-seed.
The Outlander VC team is known for making bold, founder driven bets pre-product and revenue, and has been a first or early investor in more than 15 unicorns, including Scale AI. As the first check in Scale AI in 2016, Outlander VC is a true pre-seed lead and has had a finger on the pulse of the enterprise AI space for a while and is actively deploying out of their third fund now.
Stage: Pre-Seed, Seed
Check Size: $750K–$2.5M
Focus: AI/ML, SaaS, Defense Tech, FinTech, Consumer Tech
Notable Investments: Scale AI, Rinse, Coco Robotics
Website: https://outlander.vc/
Apply: Click Here
A thesis-driven fund focused on exponential technologies including AI, deep tech, and automation. They back technical founders building enterprise solutions that leverage artificial intelligence and machine learning.
Stage: Pre-Seed, Seed
Check Size: $300K–$600K
Focus: AI/ML, Fintech, Deep Tech, Health and Bio
Notable Investments: Airspace Link, Aerdome, GlossGenius
Website: https://www.2048.vc/
Apply: Click Here
Manufacturing and industrial automation investors focused on AI-driven production and smart factory technologies. They specialize in vertical SaaS solutions that leverage AI for industrial applications.
Stage: Pre-Seed, Seed
Check Size: $250K–$400K
Focus: AI-Driven Production, Vertical SaaS, Smart Factory Tech
Notable Investments: Melrose, Aurelius, Pet’s Table
Website: https://640oxford.com/
Apply: Contact form on their website
Originally focused on gaming, a16z’s accelerator program has expanded to include AI and tech companies, now investing up to $1M per company (increased from $750K). Their 12-week program has backed companies like Hedra (AI video creation) and k-ID, with applications open for their Fall/Winter 2025 cohort.
Stage: Pre-Seed, Seed
Check Size: $750K-$1M
Focus: AI, Gaming, Entertainment, Creative Tools
Notable Investments: k-ID, Hedra, Flat2VR Studios
Website: https://speedrun.a16z.com/
Apply: Click Here
Technical investors focused on AI/ML, SaaS, and FinTech with a particular emphasis on enterprise applications. They back founders building AI-powered solutions for business workflows and financial services.
Stage: Pre-Seed
Check Size: $500K–$2M
Focus: AI/ML, SaaS, FinTech
Notable Investments: Highlight, Kubecost, Factor
Website: https://www.afore.vc/
Apply: Apply
Founded by Andrew Ng, this unique venture studio doesn’t just invest—they co-found companies from scratch with entrepreneurs. With $370M+ raised and 35 portfolio companies, they actively participate in strategy, coding, and recruitment, recently launching companies like Gaia Dynamics (AI tariff compliance) and SkyFire AI (autonomous drones).
Stage: Pre-Seed, Seed
Check Size: $250K–$1M
Focus: AI/ML, Enterprise Applications, Industry-Specific AI
Notable Investments: Gaia Dynamics, SkyFire AI, Workhelix, Profitmind
Website: https://aifund.ai/
Apply: Click Here
Enterprise technology investors with deep expertise in AI/ML, SaaS, and cybersecurity. They focus on technical founders building intelligent enterprise software and have a strong track record in AI applications.
Stage: Pre-Seed, Seed
Check Size: $500K–$3M
Focus: AI/ML, SaaS, Cybersecurity
Notable Investments: Hex, Hightouch, Runway
Website: https://www.amplifypartners.com/
A $150M pre-seed and seed stage fund dedicated to backing the most ambitious founders.
Stage: Pre-Seed, Seed
Check Size: $500K-$3M
Focus: AI/ML, B2B SaaS
Notable Investments: Jamix, Planette
Website: https://www.audacious.co/
Specialists in three specific vectors: Human Machine Interaction (HMI), Machine-to-Machine Learning (M2ML), and Biological Machines (BioM). Recent enterprise AI and industrial automation investments include companies like TensorStax and Sourcetable that align perfectly with their thesis.
Stage: Pre-Seed
Check Size: $500K-$1.5M
Focus: HMI, M2ML, BioM
Notable Investments: TensorStax, Sourcetable, Deepscribe
Website: https://beepartners.vc/
Apply: Click Here
A hybrid venture capital firm and startup studio that has been building and investing in startups for over a decade. They focus on AI/ML applications and have a track record of early investments in platforms that became foundational internet infrastructure.
Stage: Pre-Seed, Seed
Check Size: $250K–$750K
Focus: AI/ML, Web3, Decentralization
Notable Investments: Airbnb, Kickstarter, Tumblr
Website: https://www.betaworks.com/
Apply: Click Here
SaaS and enterprise tooling investors with growing focus on AI applications through their operator community. They leverage their network of operators to identify and support AI-powered enterprise solutions.
Stage: Pre-Seed, Seed
Check Size: $100K–$1M
Focus: SaaS, FinTech, Digital Health, AI Enterprise Tools
Notable Investments: Pylon, Cassidy, Inngest
Website: https://comma.vc/
Platform economy and enterprise B2B investors with increasing focus on AI-powered tooling. They invest in companies building intelligent business solutions and AI-enhanced enterprise platforms.
Stage: Pre-Seed, Series A
Check Size: $500K–$3M
Focus: Platform Economies, FinTech, Enterprise B2B, AI Tooling
Notable Investments: Firsthand (AI agent platform), Rubie, DocJuris
Website: https://www.crossbeam.vc/
Enterprise AI specialists focusing on data science, ML infrastructure, and cybersecurity applications. They have deep technical expertise and focus on B2B companies building AI-powered enterprise solutions.
Stage: Pre-Seed, Seed
Check Size: $250K–$1M
Focus: Enterprise AI, Data Science, ML Infrastructure, Cybersecurity
Notable Investments: Ocrolus, Reonomy, EdgeIQ
Website: https://www.differential.vc/
B2B enterprise software investors with growing emphasis on AI/ML applications. They focus on technical founders building intelligent business solutions and have backed several AI-focused companies in their portfolio.
Stage: Pre-Seed, Seed
Check Size: $100K–$1M
Focus: B2B Enterprise Software, AI/ML
Notable Investments: Monterey AI, Coactive, LastMile AI, Gradient Labs
Website: https://www.exceptionalcap.com/
First Round has made 17 investments already in 2025 and focus exclusively on earliest-stage companies. With 14 unicorns in their portfolio including Notion and Uber, they back founders when they often have just an “imagine if.”
Stage: Pre-Seed, Seed
Check Size: $250K–$1M
Focus: Enterprise, AI/ML, B2B SaaS, Hardware
Notable Investments: Notion, Uber, Roblox
Website: https://firstround.com
Enterprise-focused investors specializing in AI, cybersecurity, and B2B solutions. They partner with technical founders building intelligent enterprise software and have deep domain expertise in AI applications for business.
Stage: Seed, Series A
Check Size: $1M-$5M
Focus: AI/ML, B2B Enterprise, Cybersecurity
Notable Investments: Ship Angel, Retrocausal, Telmai
Website: https://glasswing.vc/
A people-first fund that leads ~40 deals per year with a founder-friendly 6-day decision process. They focus on pre-seed and seed companies across all technology sectors, with particular interest in AI/ML and data-driven companies, and have raised over $200M+ in committed capital.
Stage: Pre-Seed, Seed
Check Size: $200K–$1M
Focus: AI/ML, All Technology Sectors, Data-Driven Companies
Notable Investments: STRATxAI, GigEasy, Paratus Health
Website: https://www.goaheadvc.com/
Google’s AI-focused venture fund that invests in early-stage startups building with artificial intelligence. They provide unique access to Google’s AI research, infrastructure, and technical expertise.
Stage: Pre-Seed, Seed
Check Size: $750K–$8M
Focus: AI/ML, Automation, Enterprise AI
Notable Investments: Lambda, Streamlit, FlutterFlow
Website: https://www.gradient.com/
A diverse-focused fund that backs underrepresented founders at the pre-seed stage. They’ve been increasingly active in AI/ML investments and pride themselves on being founder-friendly with quick decision-making.
Stage: Pre-Seed, Seed
Check Size: $50K-$750K (Sweet Spot $150K)
Focus: AI/ML, FinTech, Healthcare
Notable Investments: Webflow, NerdWallet, Branch
Website: https://www.hustlefund.vc/
Apply: Click Here
A boutique fund known for early investments in transformative companies like OpenAI, Slack, and Airbnb. They focus on enterprise applications and AI/ML solutions, requiring warm introductions due to their selective approach.
Stage: Pre-Seed
Check Size: $100K-$750K (Sweet Spot $400K)
Focus: AI/ML, Healthcare, SaaS, Enterprise Applications
Notable Investments: OpenAI, Slack, Lyft
Website: https://www.k9ventures.com/
M25 is an early-stage venture firm based in Chicago, investing solely in tech startups headquartered in the Midwest.
Stage: Pre-Seed, Seed
Check Size: $250K-$1M
Focus: AI/ML, HealthTech, FinTech, Vertical SaaS
Notable Investments: Astronomer, Arrellio, Prediction Guard
Website: https://www.m25vc.com/
Early-stage investors focused on emerging technologies including AI/ML, blockchain, and health tech. They back technical founders building innovative solutions across these verticals.
Stage: Pre-Seed
Check Size: $400K–$750K
Focus: AI/ML, Blockchain, Health Tech
Notable Investments: Alice, Parsec, Solana
Website: https://notation.vc/
The pre-seed arm of Pear VC, focusing on the earliest stage investments in technical founders. They back companies at the idea stage with strong technical teams building in AI/ML, SaaS, and climate technology.
Stage: Pre-Seed
Check Size: $250K-$2M
Focus: AI/ML, SaaS, Climate Tech
Notable Investments: Affinity, Aurora, DoorDash
Website: https://pear.vc/pearx/
Apply: Get notified when applications open
Known for backing underrepresented founders and making 75-100 investments per fund, they focus on first institutional rounds for B2B and B2C software applications with a particular emphasis on diverse founding teams.
Stage: Pre-Seed, Seed
Check Size: $250K–$500K
Focus: B2B Software, B2C Software, Connected Hardware
Notable Investments: The Athletic, Bobbie, Carrot Fertility, Modern Health, Clearco
Website: https://precursorvc.com/
London-based early-stage fund with 20+ years of experience backing breakthrough companies like Revolut, Rippling, and Just Eat. While known for consumer successes, they actively invest in enterprise applications and recently backed AI companies like SponsWatch, with 10 investments in 2024 and continued deployment in 2025.
Stage: Pre-Seed, Seed
Check Size: $500K-$2M
Focus: Enterprise Applications, Consumer Tech, AI/ML
Notable Investments: Revolut, Rippling, Just Eat, SponsWatch
Website: https://venrex.partners/
AI is evolving quickly, and the best investors are the ones who are willing to commit before the rest of the world catches on. The firms listed here are backing real companies, at the very beginning, with capital and conviction. If you are building something ambitious with artificial intelligence at its foundation, we at Outlander VC would love to hear about it. Apply now.
As an investor, I’ve seen a lot of startup pitches—the good, the bad, and the ugly. And as a former founder, I feel for them. I know how daunting it can be to take the vision in your head, condense it into a clear explanation, and get others to quickly believe in it too. Early on, I also struggled to succinctly communicate the vision I had for my company.
At Outlander VC, I combine my experiences as a founder and investor to help our portfolio companies perfect their pitches. These are some of the best tips and tricks I’ve learned along the way to prepare, perfect, and present your pitch so that investors want to hear it.
When you’re raising capital in the early stages, there isn’t much of a company to pitch yet. It’s just a few folks, an idea, and maybe an MVP. You probably don’t have customers, users, or meaningful quantitative data to inform the investment decision.
Early-stage investing decisions come down to evaluating how the founders answer the following questions, based on Outlander’s Founder Framework model:
Ultimately, early-stage investors are looking for a founding team worth betting on. Early-stage founders with compelling answers to the above questions demonstrate they have the intelligence, vision, character, and ability to execute necessary to build on their thesis and scale it into an industry-disrupting powerhouse.
Helping early-stage founders perfect their pitches, I’ve noticed a common disconnect: passionate founders who eat, sleep, and breathe their startup perceive the cadence of their pitch as clear, confident, and compelling—but investors often hear it differently.
For example, I was recently working with a founder on his pitch. He has the big vision and passion necessary to convince investors, but his delivery needed fine-tuning. Seeing is believing, so I suggested he record his next pitch and send me the recording.
The next time we spoke, he told me that he’d realized, watching the recording, that he’d had no idea how his pitch sounded. With the recording, he could self-critique and correct every part of his delivery he didn’t like. He could sit on the other side of the pitch deck and, for the first time, see exactly what his potential investors would see.
We decided to take this exercise a step further. Instead of booking time with peers to get their feedback, he sent them links to his recorded pitch. Recording his pitch practice worked well for several reasons:
Recording yourself isn’t novel—it’s a time-tested tool, and it’s still highly effective. Plus, with new WFH tools like Loom’s simultaneous camera, microphone, and desktop recording technology, sharing a virtual pitch has never been easier. If you want to perfect your pitch, consider tapping “record” first!
As I said, I’ve watched a lot of pitches. The most painful to watch are those where the investor struggles to understand what the founder is trying to communicate. But I’ve helped founders whose pitch crashed and burned in the morning regroup, adjust their approach, and nail the same pitch a few hours later.
The difference? At the beginning of the second meeting, the founders sought to understand their audience by asking a few simple questions, which allowed them to deliver the pitch in the way the audience preferred. The change was small but powerful.
Here are a few questions founders can ask to understand their audience:
Understanding the audience goes a long way toward helping founders communicate effectively and gain support. The questions above will help accomplish that, but it’s by no means an exhaustive list. Plenty of other questions would also work. Regardless of what you ask, I’d limit it to two or three questions.
Now that you’ve perfected your pitch, we want to see it! Here’s what you need to know:
Outlander VC is inviting the most innovative early-stage tech startups in the United States to out-vision, outsmart, and outpitch the competition at OutPitch 2.0! Apply now to compete for $100,000 investment from Outlander VC during the live event on December 7, 2021.
For more expert advice on building and scaling your startup, check out our event library and Field Notes.
When I began investing over a decade ago, I evaluated deals using a holistic lens: a well-built company with an innovative product made for a good investment. Using this company-centric framework, I invested $1M into 20 companies during my first year that I thought had a good shot at success, but within 12 months, all but a few (shoutout to ExpenseCloud, Klout, Ticketmob, and Burstly—all 4 of which I later sold my shares in for $2.7M) were clearly failing.
Frustrated, I began taking inventory of the different factors that could’ve prevented them from succeeding, but their solutions, business models, and market influence all checked out. And yet they were still failing.
At the time, I was meeting with founders and listening to traditional pitches and predictive cash flows, which always painted a really pretty picture of the future if all went well. But over time, I understood that expecting things to go according to plan was unrealistic. The only real predictable factor with startups is that their industries will inevitably shift, and their founders will have to find ways to effectively respond. So if change is as inevitable and uncontrollable as the weather, then what aspect of each startup can we look to for constancy?
I decided that if I wanted to be an adept predictor of startup success, I would have to develop a specialized talent in evaluating the founders themselves.
I started observing and analyzing the factors that set successful founders apart from others. What psychologically differentiated them, and how did the stories they told us about themselves differ?
The answers that I found won’t come as any surprise to literature buffs: the most successful founders had shared characteristics rooted in the defining moments of their lives. In other words, they all followed the archetypal hero’s journey. Their personal stories often hinged on a moment (or moments) when they struggled deeply but somehow found a way to persevere or overcome an obstacle they couldn’t control or foresee.
These kinds of stories gave me a glimpse into a founder’s internal GPS, which could be used to predict how they would handle the chaos of a startup’s lifecycle. I began to see that some founders are more wired for adaptability, so I stopped asking for their pitches first and started asking them to tell me about themselves—their stories, their challenges, their fears. The answers they shared in conjunction with the success or lack of success they inevitably experienced provided me with the evidence I needed to build Outlander’s Founder Framework.
Our Founder Framework is the most important evaluation tool we use when deciding whether or not to invest in a startup, and like the industry we work in, it’s constantly evolving as we learn more about founders and their psychological makeup. Currently, the Framework consists of 38 characteristics divided into four categories: vision, intelligence, character, and execution. Like any good literary hero, outstanding founders need strength in each area.
Vision: Think of a founder’s vision as their map. It might not be extremely detailed, but it gives them a necessary outline for the journey that lies ahead of them. It helps them predict hurdles, and it gives them a compelling way to convince others to join in on the quest. Founders with great vision aren’t as focused on the ways they want to get to their destination; they tend to focus on the fact that they’re dedicated to getting there by any means necessary.
Intelligence: When I refer to intelligence, I’m not talking about how high their IQ is or where they got into college, although those things don’t hurt. What I mean is that they have a great internal compass—meaning knowledge of their venture’s industry, the skills to navigate through complications, and experiential knowledge that led them to create this particular solution. When their ship goes off course (and it will), they have the intelligence to get back on course and to learn from the detour.
Character: A lot of extremely successful founders have been what people might refer to as “a real character,” but when I’m evaluating a founder’s character, I’m actually trying to find out what fuels them. I’m looking for homegrown mental fortitude, something akin to grit and determination. A lot of people say they’re ready to give all of their time and energy to see a venture through, but only a handful of them really mean it.
Execution: A founder’s ability to execute is all about their ability to make choices. Founders with this strength know how to combine their vision, intelligence, and character in order to arrive at the next course of action quickly and deliberately. They must be open to adapting their approach or perhaps even looking for opportunities to experiment.
At Outlander VC, we believe in the methodology behind our Founder Framework, and we apply it early when determining whether or not we will invest in a company. So what does that look like in a practical sense?
Founders must score highly in all four categories or we will not invest no matter how good their idea is. That may sound extreme, but let me ask you:
Without a visionary founder, who will entice investors to give the startup an opportunity?
Without an intelligent leader, how will a company know what the next best move is?
Without strong character, who will keep pushing the team and find ways to renew morale?
And without someone ready to execute, how will the startup progress to new levels?
After 14+ years of investing, I know this much: even the greatest ideas are doomed to fail without a well-rounded and dynamic founder to lead the way.