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With each passing day, it becomes clearer that AI is making the intelligence side of startups and entrepreneurship more accessible, distributed, and abundant. As that happens, the real differentiators for success will increasingly come down to two things: agency and social skills.

The more computers can do what we previously thought of as thinking and intelligence, the more it falls on founders and team members to initiate action, make things happen, and get things done. At the same time, the human side of business becomes more important, whether that is corporate culture, relationships, trust, or the intangible feeling you get from working with colleagues, customers, and vendors.

Being smart and making good business decisions will increasingly become a commodity.

Consider agency and bias toward action. When the computer tells you the next best step, analyzes the market, identifies the trends, points out a problem, or highlights an opportunity, it is still up to the humans to act.

One of the arguments for people working less in the future is that AI will do more of the work. In the startup world, that can actually create an advantage. The more people at established companies work fewer hours, the greater the opportunity for startups to work harder, move faster, provide more value to customers, and gain ground.

If old-line companies work less, that creates an opening for upstarts to work more.

But taking advantage of that opportunity requires teams with high agency. They need to be self-starters with a strong bias toward action. They cannot simply wait for someone, or some AI, to tell them what to do next.

A similar dynamic will play out with social skills. If AI can give you a great answer, make a good recommendation, and analyze all the different trade-offs, everyone else will eventually have access to the same capabilities.

When intelligence becomes abundant, other forms of differentiation matter more.

For purely commodity or self-service transactions, much of business will become technology driven, with AI agents increasingly interacting with other AI agents. But high-consideration purchases will be different. Consulting engagements, large enterprise deals, M&A transactions, partnerships, and other high-stakes decisions will continue to involve people.

If everyone involved has powerful AI, along with some level of human expertise layered on top, the people who win the business will increasingly be the ones with the better soft skills. They will be the people who are enjoyable to work with, who build trust, who communicate well, and who others genuinely want to spend time with.

For AI-pilled entrepreneurs, this should be encouraging. The future is becoming even more oriented toward high-agency people. People who do not need to be told what to do. People who enjoy going above and beyond. People who find new opportunities and get after them.

At the same time, as intelligence becomes abundant, the things AI cannot do autonomously, or the situations that still require an expert human in the loop, will place an even greater premium on soft skills, people skills, and social skills.

Entrepreneurs would do well to invest in both sides of the equation: increase their agency and deliberately improve their ability to work with people.

Last week, I was talking with an entrepreneur who shared how he was using AI to do a much better job of identifying which prospects he should pursue.

It is easy to get a list of names and companies. It is much harder to determine which ones are the best fit for your product or solution.

This reminded me of one of our projects at Pardot 15 years ago. With a thousand customers, we knew there were certain characteristics that made a company much more likely to be a good fit for our product.

Here were the top three.

First, did the company advertise its own product through Google AdWords?

If so, that meant the company was already spending money on online marketing and direct response demand generation. Online advertising was a strong signal that the company could benefit from marketing automation.

Second, did the company have sales representatives listed on LinkedIn?

A sales team usually implied a more consultative and complex sales process involving multiple steps and touch points. Marketing automation was particularly valuable for companies with this type of sales motion.

Third, did the company’s website include a newsletter signup box or another form of email interaction?

This suggested that the company was already using email marketing, landing pages, automated responses, triggers, or similar tools. It demonstrated a basic level of marketing sophistication. Marketing automation could take those existing efforts and make them much more effective.

If a company was running Google Search Ads, employing sales representatives, and collecting email addresses on its website, the probability that it was a good fit for Pardot went through the roof. It became an obvious prospect.

What did we do next?

We built our own internal software to identify new companies and evaluate lists of potential prospects.

For example, the software would collect links from every article published on sites such as TechCrunch and TechMeme and add them to a database. It would then identify the companies mentioned in those articles, review Google Search results, analyze LinkedIn, and crawl each company’s website. It could even inspect the website’s source code for commonly used email marketing tools.

Finally, the system would produce a prioritized list of companies and make it easy to synchronize those prospects with Pardot.

We spent a good amount of time on this side project, and it was worth every penny.

Today, AI and large language models make this type of work dramatically faster and easier. They also make it possible to perform a much deeper level of analysis on every potential customer.

What words or ideas on a prospect’s website indicate that the company might be a strong fit for your product?

What broader concepts or signals can be identified through social media, job postings, press releases, customer reviews, or other public information?

What additional data points can AI triangulate at scale in an automated way that would have been impractical or impossible before?

My recommendation to entrepreneurs is simple. Study what your best customers have in common. Identify the characteristics shared by your ideal prospects. Then build tools and systems that continually find, evaluate, filter, and prioritize companies based on those characteristics.

You should never cold call or cold email a company again unless it has first been evaluated using your own set of characteristics.

There’s a phrase in startup land that really resonates with me: stay close to the problem.

As an entrepreneur, it’s easy to become enamored with ideas, visions, and what could be. While all of those are important, what ultimately matters the vast majority of the time is solving someone’s problem. It’s providing a solution to a business issue, identifying something that needs a better way of being done, and delivering that new way.

Entrepreneurs often want to go off into a room and start building. It’s fun to get lost in your ideas. It’s fun to build an application and see a tremendous amount of progress without being beholden to feedback or input from others. It’s easy to get excited about your own work.

The huge issue, of course, is that what you want in the product isn’t necessarily what the market wants. The longer you spend operating in a vacuum, the more likely you are to veer off course from what is actually needed.

By staying close to the problem, even when you don’t have the perfect solution or a clear answer, you develop greater understanding, gather more data, and build more empathy for what needs to get done. All of this feedback results in regular realignment with the solution the market actually needs.

In today’s world of AI, it’s even easier to go off on a quest without the necessary market input. When you ask AI questions, it is designed to give you answers. Whether those answers are correct or not, it will continue providing them. The deeper you go down the rabbit hole, the more information it will feed you.

The problem is that you may be reinforcing something that isn’t market-driven. You get the satisfaction of receiving a confident answer from the machine, but the machine is not the human being who will ultimately decide whether to buy your product.

There’s another saying in startup land that still resonates today: get out of the building.

Go see the customer. Go see the prospect. Talk to partners. Learn face to face. While this creates more effort and friction, it also reduces the information loss that occurs when seeking answers secondhand.

One of the best ways for entrepreneurs to stay close to the customer is through consulting. By doing real work for the customer, even when the work isn’t exactly where the entrepreneur wants the product or vision to go, the entrepreneur develops a much deeper understanding of the problem.

When there is a relationship in which real value is exchanged, rather than merely a free trial, the customer will provide better information. The customer will share their pain in a deeper way, and the entrepreneur will become embedded in the organization in a way that allows them to fully understand what needs to be solved.

My recommendation for entrepreneurs is to stay close to the problem and remain flexible with the solution. Figure out how to get direct feedback and ideas without going through a middle layer. Figure out how to charge a little bit of money so that the relationship is more serious and the feedback is higher quality.

Stay close to the problem. Let the solution evolve from there.

Recently, I was talking to an entrepreneur who shared that one of his side projects was building a personal AI brain. The goal was to process information across every area he cared about, automate repetitive tasks, and deploy simple agents to monitor different parts of his life.

I was intrigued, so I asked him to show me how he had built it and what it looked like.

It started with vibe coding in tools like Lovable and Replit, where he experimented with different AI applications. He also took inspiration from Google’s NotebookLM, which allows users to upload documents and ask questions based on their contents. Because the answers are grounded in those documents, the system is less likely to hallucinate or introduce information from outside sources.

He began by creating a central dashboard. Imagine a screen with several boxes, each displaying one to four key metrics. Every box represented a different area of his work. Clicking on a box opened a separate vibe-coded application designed around something he worked on regularly.

One of those applications was his own version of NotebookLM, but without the same document limitations. This app focused entirely on startup strategy.

Anything related to strategy, best practices, or mental models could be added to it. He could link to an article, upload a PDF, or connect a Google Doc. Over time, it became a personalized repository for everything he found valuable about building startups.

Whenever he encountered a new question, challenge, or piece of information, he could add it to the system and discuss it with the AI. Instead of searching across bookmarks, documents, and notes, he had one central place to retrieve and synthesize the ideas that mattered most to him.

Back on the main dashboard, a second box opened an application focused on product development. This app pulled information from several internal systems. It included data from Jira, such as trouble tickets and issue tracking, along with feeds from customer support, Salesforce, and the company’s roadmap management software.

Inside this custom dashboard, he could see the latest information from each system in one place. Through a plain-text chat interface, he could ask questions about the product, customer needs, support trends, development priorities, or roadmap decisions. The application could also generate graphs and charts related to the product management metrics he cared about.

Another box on the dashboard focused on financials. Clicking it opened a custom application that pulled information from the company’s financial management software, Salesforce, and other financial planning and analysis tools. It also included a chatbot for asking questions, generating summaries, and exploring the company’s financial performance.

The final section he showed me focused on industry trends. He had built an AI agent that continuously checked social media, industry websites, Techmeme, and other relevant sources. The agent compiled that information into a local database and highlighted what was trending or gaining momentum.

The app displayed graphs, charts, summaries, and a chat interface that allowed him to ask the language model questions about what was happening in the market.

Time will tell whether this becomes the standard way entrepreneurs and innovators operate. But seeing it in action, updating in real time and customized entirely for one individual, gave me a glimpse of where the world may be headed.

It was one of those lightbulb moments.

The future is an abundance of software. Some of it will be off the shelf. Some of it will be handcrafted. Much of it will be interactive, personalized, and customizable in ways that were never previously practical.

Enterprising entrepreneurs should consider building their own personal AI brains, along with small vibe-coded applications for each area of the business where they spend meaningful time. These tools can support both high-level thinking and detailed operational work.

The amount of internal and external information now available to entrepreneurs is enormous. The ability to process, synthesize, and retrieve that information quickly is unparalleled. Entrepreneurs would do well to make their most important information not only accessible, but also intelligent and insightful.

One of the recurring debates I have encountered is around the role of founder-led sales.

Founder-led sales is the idea that, in the early days and even the early years of a startup, the founder should be the person selling the product on the front lines. By doing the actual work of selling, there is no telephone game about what prospects do or do not want. The founder hears the feedback directly, can coordinate closely with the product development team, and gains greater clarity about the market and the opportunity.

Assuming founder-led sales is successful, the business eventually begins to scale. The common advice is to transition from founder-led sales to a repeatable, scalable go-to-market process that does not depend on the founder.

Of course, building a sales and marketing organization that can reliably deliver new customers without constant founder involvement is ideal. But that advice misses an important point, one that is closely related to the idea of founder mode.

Founder mode suggests that founders should not simply hand off their most important responsibilities to experienced outside executives and step away. Instead, founders should remain involved in the details that matter most to the business, within reason.

In that sense, founder-led sales is a component of founder mode. The founder should remain involved in sales indefinitely, especially in enterprise software and other markets with long, consultative sales cycles.

Markets often change quickly, and competition can be fierce. When founders step too far away from the sales process, the company can lose competitiveness. It also creates a greater need for internal alignment, communication, and organizational coordination.

Just as founders should understand what is happening throughout the business, the most successful entrepreneurs I have encountered remain involved in sales even as the company scales.

They do not necessarily carry a quota or run the weekly pipeline review. Instead, they might serve as an executive sponsor on important opportunities. The sales team brings the founder into certain deals, allowing the founder to devote a portion of their time to prospects and customers while staying close to the market.

This is often a winning formula.

As one of my favorite sayings goes, nothing happens until something is sold. Understanding the market, understanding the customer, and earning the business are among the founder’s most important responsibilities.

Entrepreneurs should absolutely build a repeatable sales model. But they should not stop talking to customers. Over time, they should develop a process that keeps them involved in sales without allowing it to consume the majority of their role.

Founder-led sales is an important part of the entrepreneurial journey, and the most successful founders continue to devote meaningful time to customers and prospects, even at scale.

If you read the startup blogs and listen to the most popular startup podcasts, it is easy to think that the only way to raise money in the current climate is with an idea that can become worth tens of billions of dollars or more.

While those companies get the majority of the hype, as they should, they represent the biggest ideas, the biggest funding rounds, and the most famous investors. The reality is that the vast majority of startups that go on to raise venture money and create meaningful equity value do not fall into this category.

The most famous venture firms do strive for multibillion-dollar outcomes, and in some cases, companies worth tens or even hundreds of billions of dollars. But my guess is that 80% of venture firms would be thrilled with a 10x to 20x outcome from any given investment.

It is not that they do not want investments that turn into multibillion-dollar companies. It is that they are playing the game of finding great companies they believe can compound at high rates over a long period of time and eventually go public or get acquired by a strategic buyer at a high multiple. Simply put, this can translate into outcomes that return 10x or 20x the original investment, and everyone is thrilled.

There is a dirty secret in the venture and investing world that does not get talked about as much because, frankly, it is both true and not fun to discuss: most startups, including venture-backed startups that raise millions of dollars, will sell for less than their last valuation.

That means the vital few that make it and return the fund are necessary to pay for all the losses from the ones that do not make it. We need more startups. We need to let a thousand flowers bloom. And as part of that, we have to pay for it. The way to pay for it is through the mega success stories subsidizing the ones that do not work out.

Most venture firms are thrilled with a 10x to 20x return on an investment, and they know that most investments will not even return 1x or 2x.

My recommendation for entrepreneurs is not to get caught up in the hype around AI companies with unfathomable growth rates. Instead, focus on delivering real value to real customers in a way that is sustainable over a long period of time.

Most venture investors and venture-style investors are not looking for companies that can be worth a trillion dollars. Most are looking for companies that can deliver an incredible return within 5 – 10 years.

The AI growth rates and valuations are inspiring, but they are not the standard for the vast majority of venture investors. Focus on building a great business.

I’ve always enjoyed learning about different business best practices, strategy documents, and ways of thinking through a company game plan. One example I’ve seen more frequently over the last year is the concept of “right to win.”

Right to win is the idea of understanding your competitive positioning and what makes your product distinct from competitors. It is often broken down into a table with three columns. The first column is the capability or functionality provided. The second column is why it matters, which articulates why this particular capability or functionality is needed by the market and your specific customer base. The third column is why your competitors struggle with it. Here, the goal is to describe why other competing products in the market have a hard time doing the same thing.

Put more simply, the framework is: the capability, why it matters, and why competitors struggle with it.

My general approach to competition is to be competitor aware and customer obsessed. Even when obsessing over customers, there are often competitive new deals where, in order to win the business, you have to articulate how your product is different from others in the market. Merely being competitor aware doesn’t solve the entire issue. You really have to understand what makes your product unique and how that connects with the prospect and their goals.

The right to win strategy should be used to align team members, investors, partners, and advisors. Many entrepreneurs even include a right to win slide in their investor updates or board decks. It’s a great way to communicate the product strategy internally in the context of competition.

For entrepreneurs, my recommendation is to think through this right to win idea and use it to consistently deliver a winning strategy against the competition. Competition is what makes free markets such an incredible way to produce the best products. Regularly revisiting your right to win strategy and updating it in the context of the market is something every entrepreneur should do.

Last month, I was talking to an entrepreneur about his upcoming fundraising round, and he shared an approach that I hadn’t heard before. His ultimate goal for the funding round was to secure a lead VC from a national, brand-name firm combined with a strong regional fund.

My curiosity piqued, so I asked him why. He told me it comes down to getting the best of both worlds.

Here is why this hybrid strategy makes so much sense for growing startups.

What the National Brand Brings to the Table

For the national firm, the entrepreneur was looking for macro-level advantages. When you bring a top-tier national firm onto your cap table, you gain access to resources that smaller firms simply cannot replicate:

• Instant Cachet: Broad brand recognition that immediately validates your company to the rest of the market.
• Deep Pockets: The financial reserves required to comfortably anchor subsequent funding rounds as you scale.
• Specialized Resources: Dedicated operating partners, industry-specific expertise, and connections to related portfolio companies.
• Network Perks: High-level ecosystem benefits, including exclusive CEO summits and regular founder get-togethers.

Why the Regional Firm is Indispensable

Knowing those massive national benefits, I then asked why he wanted a strong regional firm as well. His answer was brilliant. Since his company was not based in one of the main venture capital hubs, and he intended to keep growing the business exactly where he was, he needed local muscle.

A strong regional firm provides the boots-on-the-ground advantages that a distant national firm might miss:

• Hyper-Local Networks: Deep roots in the local ecosystem, including connections to regional vendors, partners, and corporate allies.
• The Talent Back-Channel: Regional firms have worked with hundreds, if not thousands, of professionals across their local portfolio companies over the years. They know the talent pool intimately.
• Recruiting Power: When you need to scale your team locally, a regional VC can actively help you vet and recruit top-tier talent, from executives to early-stage employees.

The Takeaway for Founders

Thinking more about this approach, it really resonated with me. A national brand-name firm brings the prestige and macro resources, while a strong regional firm drives local execution and relationships.

Most of the time, entrepreneurs do not have the luxury of choosing between multiple ideal firms. When you are raising money, you often have to take the best deal available to keep the lights on.

However, if you are in a position to shape your round, this approach is highly worth considering. My recommendation to founders is to think deeply about what you actually need from a capital partner. Consider a strategy that combines two different firms, allowing each to bring their unique strengths, resources, and talents to the table.

Over the last week, I’ve enjoyed reading some of the different VC horror stories sparked by Greg Isenberg’s tweet. There’s everything from crazy bad behavior to unusual interactions to a variety of positive and uplifting stories.

While my time pitching VCs was many years ago, I’ve done it dozens of times and have had a few memorable experiences of my own.

The first one that comes to mind is when a VC in the Northeast pursued us aggressively. He kept reaching out and asking for updates, and not knowing any better, I kindly obliged and spent time with him. At one point, he wanted to get more serious and do a deeper dive on our business, so I shared a little more information. Then he went quiet.

He had already asked for and received a bunch of intel from me. Then, six weeks later, the announcement came out. He had just led a huge funding round for our direct competitor.

Lesson learned. Or not.

The next story comes from pitching one of the most famous venture firms on the West Coast. I had gone out there several times and had a decent rapport with the partner. After meeting three or four times, he shared that they had never seen venture-backed returns in the marketing software market and didn’t believe it was going to be a big opportunity. It wasn’t per-seat pricing, and they didn’t see a mechanism for a different type of financial model based on usage.

They politely passed.

Then, of course, six months later, they funded one of our main competitors, a different one than the Northeast firm.

My takeaway after those experiences is to always assume that VCs are looking at all of the players in the space, and that the information you share is going to the investment committee and will be used as part of their decision to invest in the space, regardless of whether they invest in your company.

My final story is one on valuation. We had just cleared $1 million of recurring revenue, were growing over 300% year over year, and pitched a regional VC. After a number of conversations, we received a term sheet. It was for a $2 million pre-money valuation.

This was after tons of paying customers, clear product-market fit, and over $1 million in recurring revenue. We negotiated a little and they upgraded the offer to $2.5 million pre-money valuation. Then we shared that it wasn’t worthwhile to raise money on those terms.

We left on a good note, but it helped me understand that there’s a huge timing element in the market based on what’s popular and what’s not.

We decided to bootstrap and continue building the business without raising outside capital.

At the end of the day, raising money from VCs is a tool, not a milestone. For the right company, in the right market, at the right time, it can help accelerate growth and create something much bigger than would have been possible otherwise. But it also comes with expectations, pressure, dilution, and a different definition of success. 

Recently, I saw a headline in The Wall Street Journal that said, “$3.6 Million an Hour and Other Ways to Measure Musk’s Fortune.”

A catchy headline like this definitely grabs your attention. It also makes you initially believe that wealth creation, in this case the most extreme example, happens in a linear format with a simple hourly rate.

While that framing is useful for dramatic effect, in the startup world, value creation is dramatically backend loaded. Often, the early years are spent toiling away, trying to find product-market fit, then building a repeatable customer acquisition process, and finally scaling the business.

Those first two phases, product-market fit and a repeatable customer acquisition process, can take years and yield limited value. Yet once you make it through them and the business starts scaling, there is strong potential for dramatic value creation.

Battery Ventures popularized the “triple, triple, double, double, double” framework for SaaS companies, often referred to as T2D3. The idea is that after reaching roughly $2 million in annual recurring revenue, a company triples to $6 million, triples again to $18 million, then doubles to $36 million, $72 million, and $144 million in annual recurring revenue. Battery describes this as one path for a SaaS company to grow toward a billion-dollar valuation.

By historical software standards, not AI-type software businesses, a company that reaches that scale has built something wildly valuable. In this example, the later years create far more value than the early years because the business is scaling from a much larger revenue base. Going from $72 million to $144 million in annual recurring revenue adds $72 million of new recurring revenue in a single year. Assuming a 5x valuation multiple, that final doubling alone adds $360 million of enterprise value to the startup.

The early years were minimal from a value-creation perspective, but necessary to get to the scaling years where value creation is strongest.

For entrepreneurs, when reading headlines or stories about wealth creation that imply a linear rate, know that the framing is incorrect. By and large, almost all value creation for entrepreneurs occurs in the latter years of the business.