The way technology products are built is undergoing a fundamental shift due to the rise of long-running agents. These tools allow both technical and non-technical people to build what they imagine with unprecedented ease. Gokul observed this change personally when he tried to build a video transcription tool. Six months ago, the process was full of errors and required manual debugging. Recently, he built a functional version in just one hour by prompting his way through it. The agents are now resilient enough to handle failures without constant human intervention.

This shift is breaking down the traditional silos of product management, design, and engineering. In the past, roles were clearly defined. Product managers told engineers what to build, and designers created the interface. Now, the capabilities of AI models are moving so fast that a strict top-down approach no longer works. Instead, product development has become a bottoms-up process. Product managers are shifting from prescribing exactly what to build to becoming the guardians of the why. They focus on articulating customer needs while working directly alongside engineers and researchers on the code.

The first thing we are seeing now happen is that PMs are starting to check in code with either codecs or Claude code into the actual production repository. Right now engineers have to review the code, but you are going to soon see that Claude code, codecs, and other tools actually review the code itself before engineers commit.

Because of this, companies are changing how they evaluate talent. A new step called the prototyping interview is appearing in hiring loops. This forces product managers to prove they can be hands-on. If a product manager does not understand what these models can do or stays disconnected from the technical work, they risk falling behind. PMs are now expected to sit with researchers and write code or build prototypes to keep up with the pace of innovation.

The roles of product managers and designers are merging and changing. Many companies now prioritize hiring engineers over designers because design systems are already well established. AI can leverage these existing systems to handle much of the design work. This shift is changing the staffing ratios in tech companies. The number of engineers per product manager or designer is growing much larger than it used to be.

Software is also changing from deterministic to non-deterministic. In the past, you knew exactly what would happen when a user performed an action. With AI, similar inputs can produce very different results. This shift makes evaluations critical. Product managers now own the process of determining if AI outputs are reasonable. They often have to write AI tools to evaluate other AI results because the scale is too large for humans to handle alone.

The product manager, there has to be somebody at the company who is a keeper of the why. Why are we building it? What customer need are we solving? Why is this a pain point? How intense is it, how deep it is? And second, how does it add value to the company?

Gokul believes the core of product management is balancing customer needs with business needs. He defines success through outcomes and changes in customer behavior. Every new feature should have a clear hypothesis. This hypothesis must explain how the feature will change a user from one state to another, such as turning a casual user into a loyal customer. If there is no clear behavior change expected, the feature should not launch.

As AI tools become more powerful, the most important human skill is judgment. We are entering an era of infinite productivity where AI can generate massive amounts of code and content. This creates a risk of AI slop or low quality output. Humans must provide the critical review to ensure the right things are being built. Judgment is future proof because someone must always evaluate if the work is valuable, safe, and correct.

The one thing I think that is going to be truly future proof is judgment. Why? Because what is the biggest challenge you have? When you have thousand AI engineers writing code, you have the big challenge of AI slop.

Building an AI application today requires starting with a deep and compelling problem. In the past, software functioned primarily as a tool for humans. Now, software has become agentic. This means it can perform the actual jobs of people. To find a viable startup idea, identify industries where highly paid professionals perform repetitive tasks. This applies to roles that once seemed safe from automation, such as architects, designers, and lawyers.

First and foremost, you got to start with the deep and compelling problem. We finally have software that is agentic in nature, which means it can do the job of people. The question you have to ask is, what industry? Are there roles of people that are highly paid, that are doing somewhat of a repetitive job and that can be done by software?

Success depends on targeting high value workflows that are complex and require custom data. If a project is too simple, foundation model companies or large corporations with horizontal tools will likely replace it. Many large companies already have internal engineers who want to build their own agents using existing tools like Gemini or ChatGPT Enterprise. Gokul suggests that you must go many steps ahead of what a standard agent building tool can do.

Durability is the biggest challenge for new entrepreneurs. A business needs to survive for more than a year. Gokul explains that achieving this requires owning a scarce asset like a specific license or controlling how data and money move within an industry. Other sources of durability include unique hardware, essential workflows, or network effects.

The landscape is shifting as legacy systems of record begin to protect their data. Companies like Salesforce and Slack are increasingly blocking API access or charging high fees to prevent AI startups from building on top of their platforms. This means the ultimate goal for a new company should be to replace the entire system rather than just adding a layer of intelligence on top of an existing one.

These companies started seeing that these agent companies, AI companies that are being built, they are starting to take on the functionality out of these companies and are treating them like a dumb database. So you started seeing last year that these companies are cutting off access to APIs.

Large platform companies are making it difficult for standalone agent companies to survive. Customers often find it difficult to use an agent that does not integrate perfectly with their primary system of record. This friction forces agent companies to expand their scope. They are no longer just building workflows that sit on top of other software.

I don't think that's an option anymore. Every company I know is now trying to figure out how do I build the entire platform and not just a system that does some workflows.

Last year, many developers focused on building systems of action. These tools were designed to function on top of existing data platforms. Gokul explains that this strategy has become unviable. Modern software companies now feel they must provide the entire platform. This ensures a seamless experience for the end user. This represents a change from the previous generation of software where companies like Slack found success.

Software companies face a major threat from horizontal AI models. The most endangered companies are those that price their products based on seats and utility. Zendesk is a classic example. It charges per seat for customer service agents. An AI agent can sit next to the software and slowly take over tasks. Instead of paying for 50 seats, a company might eventually only need 20. This siphoning happens gradually. These companies need to shift their pricing models to focus on outcomes. Moving from a monthly seat price to charging per resolved ticket is difficult. It changes the entire business model. This is why many software companies might need to go private. Making such a huge transformation is easier away from public markets.

Those are the most endangered companies. For these companies you need to change your pricing based on outcome and you need to actually build the product to be based on outcome. It's easier said than done because you are going from a $20 or $30 per seat to maybe charging a buck or 50 cents or 20 cents per ticket resolved. And you don't know how that's going to turn out.

Companies that act as systems of record are more protected. These platforms hold timeless data that runs an entire business. Ripping out a system like NetSuite is risky. Gokul explains that it is often seen as career limiting for an executive to remove a stable ERP. These established companies also have the advantage of time. They can build AI agents on top of their existing data. To compete, AI native startups must invest heavily in migration tools. It can take years to build a system that seamlessly transfers data from legacy platforms like Salesforce or Jira. Without these tools, customers will rarely switch. Even at Square, small businesses would not switch point of sale systems if they had existing loyalty and payment data that was hard to move.

In an age where AI allows creators to build new products almost instantly, software on its own has a very short lifespan. To stay relevant, a business needs specific sources of stickiness that software cannot easily replicate. Gokul identifies four main ways a company can build this kind of durability.

First, network effects create a deep moat. A company like DoorDash is not sticky because of its app design. It is sticky because it connects a complex network of restaurants, drivers, and customers. You cannot simply recreate that ecosystem with code alone. Second, durability comes from financial flows. When a business acts as a system of record for money, such as Mercury for banking or Toast for payments, it becomes very difficult for a customer to switch. These companies embed themselves into the financial operations and regulations of a business.

The half life of software today is so short that unless you are one of these things that make it durable. You have to have a few of those seven powers that basically are embedded in the business model.

Hardware is a third source of stickiness. If a company like Toast provides physical equipment, a competitor cannot just offer better software. They would have to convince the business to physically rip out the old hardware and install something new. Finally, access to a unique asset can provide a massive advantage. Gokul points to Sierra as an example. Their unique asset is Brett Taylor, whose reputation and connections allow him to open doors that a typical salesperson could never reach. Without one of these indicators of durability, software today risks becoming obsolete very quickly.

Generational companies are often shaped in the image and superpowers of their founders. At Google, Larry Page and Sergey Brin instilled a culture focused on technological superiority and massive scale. This philosophy was clear when Gmail launched under the internal codename Caribou. While competitors like Yahoo Mail offered only 10 megabytes of storage, Gmail provided a full gigabyte. This 100x increase demonstrated a commitment to building the best technology on the planet.

They were deeply technical and every product was held to technology and scale. AdSense was the fastest growing product in Google history. And we went in to reviews and Larry would be disappointed in us. He didn't care about the revenue. He cared that Google is involved in serving every single ad on the planet.

Larry and Sergey prioritized total reach over financial milestones. This perspective led to decade long investments in projects like Street View, TPUs, and Waymo. Gokul notes that they operated on the belief that if you invest heavily in technological capabilities for an uncertain future, good things will eventually happen.

Knowing that if you invest in technology, good things are going to happen and good things happen. But it took a decade and that's investing in technology capabilities.

Eric Schmidt had a unique approach for Google's annual strategy sessions. He tasked Gokul with presenting the company's direction using only images. Words were forbidden on the slides. Eric believed that people rarely remember specific words. Instead, they remember the emotions and feelings a presentation evokes.

People don't remember words. They remember how things made them feel. And you can put words in the speaker notes I'll use. But I want you to come up with the most compelling image that exists for what they describe.

Gokul had to find visual metaphors for every business unit. For YouTube, he chose a hockey stick graph to illustrate the massive growth in video uploads. He avoided using numbers to keep the focus on the visual impact. To show the success of the Google search appliance, he simply displayed the logo of a massive Fortune 50 company. This single image communicated that the product had moved from small businesses to the largest organizations in the world.

Mark Zuckerberg stands out for his ability to build growth and engagement in consumer products. Gokul observed him critiquing product flows with an immediate understanding of what would compel users. Zuckerberg also excels at learning by shadowing. When Gokul joined the ads product team, Zuckerberg spent a year closely following the team and attending their meetings until he began generating foundational ideas himself.

He shadowed us, he came to many of our meetings. And within a year he got to the point where he was generating ideas for the ads team. One of the most foundational ideas of Facebook ads came from what is called custom audiences.

The concept of custom audiences changed the industry. At the time, Zynga was a major advertiser looking to find more high-value players, often called whales. While traditional systems relied on broad demographics like age and gender, Zuckerberg suggested allowing advertisers to securely upload their own data to find similar users. This shift from describing customers to identifying them directly allowed the system to map users and find new people with similar behaviors.

Jack Dorsey brought a similar level of design thinking to Square. To Jack, good design means a product is so intuitive it requires no manual. This is evident in the Square point of sale system. While traditional registers require days of training for a new barista, Square is a product someone can download and start using immediately to run a business.

Good design doesn't mean visually pleasing. It means a product that is designed so well that you don't have to give your customers a manual on how to use it. They should be able to see the product and use it.

Beyond design, Square transformed financial services by rethinking risk. Most banks deny small businesses early in the application process because of perceived risk. Jack and his team decided to accept over 90 percent of applicants. They shifted the risk management to the transaction level. By using machine learning models to monitor every individual transaction, they could identify risk in real time rather than blocking people during onboarding.

Sergey Brin applied a similar philosophy of efficiency at Google. During the launch of AdSense, he challenged the team on why they needed to manually approve every website publisher. He pushed for a more automated approach that avoided unnecessary gatekeeping and questioned the need for strict manual policies that slowed down the scale of the product.

When building complex systems, there is a temptation to create extensive upfront checks to prevent errors. At Square, the engineering team spent months building a complex approval process for domains to prevent fraudulent applications. However, Jack Dorsey challenged this approach by suggesting the system should review content in real time rather than at the point of application. By waiting until a page reached a certain threshold of impressions, the team could focus their efforts on what actually mattered rather than trying to verify every minor detail at the start.

Not trying to put lots of checks up front, but being intentional about where and why most things don't even get to the level where you care about.

This philosophy extends to the role of a product manager, which Jack referred to as a product editor. Instead of constantly adding new features, the most effective product people are those who can edit down an experience. Great design involves taking ten pages of ideas and cutting them down to the few things that truly drive customer outcomes. This editorial capability is rooted in human judgment. In an age of AI, people who can act as reducers rather than just producers will be the ones who thrive.

Jack called the product manager role product editor. Why? Because he believed, rightly so, that the role of the product manager is not to add more features. Any of us can look at a product and say, here is 10 things you should build. The best designers, the best product people edit down things.

Gokul notes that this mindset applies across the entire company. The best designers and leaders minimize the number of steps and pages required to complete a task. This process of editing requires strong judgment and the ability to distinguish between what is interesting and what is essential for the user.

Communication within a startup changes drastically once the company expands beyond a single room. In the early days, everyone can hear what is happening. However, as soon as people are separated, leaders must create specific artifacts to keep everyone informed. Gokul suggests two essential tools: a weekly all-hands meeting and a CEO email. The all-hands is a simple way to share what people have built and allow a leader to address the entire group together.

The second thing is a weekly CEO email. And I think this is a very powerful way for the CEO to get across to the team what is on their mind. The best way I think is that I've done myself is during the course of the week you start jotting down things that you think you want to communicate and then you'd spend Sunday taking all of those things and adding it into two or three things that matter that you want to get across.

Most business updates can be structured around three dimensions: product, business, and team. Gokul advises leaders to look at how the product is serving customers, how the business is performing, and what changes are happening within the team. Repetition is also key. A message often needs to be repeated several times before it truly resonates with the organization.

Gokul recommends a specific email structure for leaders that has been adopted by many successful CEOs. The format consists of three main sections. The first and most critical is Top of Mind, which covers concerns about the product, the business, and the team. This section should receive the majority of a leader's focus because it reveals what is actually keeping them up at night. Documenting these thoughts is a powerful way to provide clarity to the entire organization.

The most important section where you should spend 60 or 70% of your time is top of mind. This is the thing that everyone is hanging on to. Seeing it put in an email is just so powerful.

The second section is a Performance Update. Employees at a startup often feel removed from the actual state of the company. Providing a regular update on how the business is doing helps them understand their progress. The final section is Miscellaneous, which includes recognition for specific people, customer quotes, and administrative announcements.

When deciding how much to share, Gokul favors high levels of transparency. While leaders often worry about causing stress by sharing sensitive information, being candid allows talented employees to offer solutions. When the team understands the real problems the company faces, they can rise to the occasion and provide feedback on critical decisions.

I personally think more candid is better than less. If you are candid, you can actually ask people to suggest ideas. If you have good talent at the company and ask them what they think the company should do in a situation, people will rise to the occasion.

There are only three ways to succeed in the advertising business. The first path is to own a specific surface where a coveted group of users interact. Google is the gold standard for this because it captures high intent through search. Facebook succeeded by capturing identity. Now, ChatGPT is entering the space with a combination of intent and identity that is unparalleled. These complex, multi-phase natural language queries are ripe for amazing targeting.

ChatGPT is the dream of any advertising person. Google had intent data but not identity. Facebook had identity but not intent. These things being brought together is unparalleled.

The second way to win is by driving specific outcomes. You do not need to own the inventory if you can deliver results for advertisers. AppLovin built a massive business by mastering mobile app installs. They control the infrastructure and the auction to deliver an outcome at a specific cost. The third way is to become the exclusive provider for a large source of demand. The Trade Desk does this by managing the display budgets for giants like Procter and Gamble. To make money this way, you must be the exclusive provider for how that demand is distributed.

Building a business as a middleman on top of massive platforms like Google, Facebook, or OpenAI is a risky strategy. These platforms have the best engineers on the planet and can easily replicate and incorporate new capabilities into their own systems. This leads to smaller companies getting squeezed out once the platform learns what they are building. Gokul suggests that companies focusing on things like optimizing ads in ChatGPT or answer engine optimization will struggle to create durable businesses.

If you are trying to build your business on top of Google and Facebook or probably soon OpenAI as an ad company, you are going to get squeezed every time you build a new capability. Google learns what you are building. They will take your capabilities and incorporate into their platform.

The rise of AI agents and agentic interfaces poses a major threat to traditional ad networks. If consumers start using agents to automate repeat transactions, they will stop opening apps like Uber, Amazon, or DoorDash. This shift breaks the direct relationship with the customer and eliminates the opportunity to serve ads. When customers start trusting an AI agent more than a specific brand, the platform loses its power.

The thing that would scare me is consumer behavior change where they do not open up the apps anymore, but they use agentic interfaces to do their transactions. You lose opportunities to then advertise and you lose the relationship with the customer over time because the customer starts trusting the AI agent.

Companies must experiment with these new interfaces to understand how they affect user behavior. Gokul notes that commerce platforms are already testing integrations with ChatGPT. By monitoring early adopters who link their accounts to AI platforms, companies can see if these users stop visiting the native app. If the AI experience is more compelling, the companies must decide whether to incentivize app usage or change the experience entirely.

Being the first to market with an ad network for AI interfaces is not necessarily an advantage. Since companies control their own first-party inventory, they have the luxury of observing the mistakes of early movers and iterating based on those lessons. For example, Google could strategically keep Gemini as a zero-ad platform for a significant period to attract users who value a clean experience. This flexibility allows platforms to wait until monetization is truly necessary before introducing advertisements.

The good news is being first doesn't matter because you control your first party inventory. In fact, being second or third, you can learn from the iteration mistakes that the first one makes. Your inventory is not going anywhere.

Gokul emphasizes that ads must remain relevant without influencing the actual recommendations or content an AI provides to a user. There is an inevitable trade-off between monetization and user interaction. Data from various companies shows that introducing ads to a previously unmonetized surface consistently leads to a drop in engagement over time. To manage this, companies should use holdout groups that never see ads to understand baseline behavior and determine exactly how much engagement is lost with each ad shown.

You need to figure out what the engagement hit is from each quantum of ads. And you need to then give your ads team a certain engagement budget. At Facebook, there was an engagement budget every year. We wanted this much revenue, but the check metric on the revenue was we can't take more than a certain percentage dip in engagement overall.

By establishing an engagement budget, platforms can balance revenue goals with user experience. This approach ensures that the pursuit of profit does not degrade the core product to the point that it alienates the user base.

A good North Star metric balances customer value and business growth. It should not be revenue. Instead, it should be something directly correlated with customer value. When customers do well, this metric moves up and to the right, naturally leading the business to success. At Square, the team focused on Gross Payment Volume rather than revenue. This metric showed that the amount of payments processed through the platform was growing, which signaled health for both the merchants and the company.

A North Star metric is an indicator of company growth and customer value. It balances customer value and business value nicely. North Star metrics should not be revenue. It should be something that is directly correlated with customer value.

Incentives drive behavior, so a North Star metric must be paired with check metrics to serve as guardrails. Without these, a team might optimize for one goal while accidentally damaging the business. For instance, at DoorDash, the team might focus on Gross Merchandising Value. While they could grow this by setting delivery fees to zero, doing so would destroy the company revenue. Using gross margin or customer retention as a check metric ensures that growth remains sustainable and healthy.

If you tell a team, go and optimize this North Star metric, it is going to go up 100%. But then many things that you don't want to go down could go down. You basically want a check metric around the health of the customer and the health of the company that are the guardrails around this North Star metric.

Great investments often share four specific qualities: high gross margins, low customer acquisition costs, high retention, and a tight sales cycle. These traits frequently connect back to the concept of self-serve products. Gokul learned the importance of self-serve during his time at Google when the team was building internal systems to help sales and operations teams manage large customers. When Larry Page saw these internal tools, he insisted they be made available to everyone. He wanted to ensure that small customers had the same capabilities as large ones.

End it right now. I want to make sure that everything you're building for large customers is also available to small customers.

Opening a system to self-serve users provides an incredible learning opportunity. Smaller customers, entrepreneurs, and agencies often prove to be the most sophisticated users because they find creative ways to exploit a system to make money. When you allow customers to use a product without human intervention, you gain a deeper understanding of your product's true capabilities. For example, Square saw Nike sign up and onboard itself in a single store without any sales outreach.

True self-serve means a customer can onboard and use a product without ever speaking to an employee. This requirement forces a company to obsess over the onboarding experience and ensure users reach a moment of delight quickly. While a sales team might reach thousands of customers, a self-serve motion can reach millions through word of mouth. This bottom-up approach is often more powerful than top-down sales. Gokul tried to push Figma to the design team at Square, but they refused. Years later, a mid-level manager brought Figma in from a previous job, and it naturally displaced the incumbent tool. This shows how a self-serve product can infiltrate a company as an insurgent in a way a direct sales motion never could.

The focus in the AI era is shifting toward doing and building. Many leaders are moving away from hiring middle management. Instead, they want people who can act as functional experts. The most important skill in the next year or two will be the ability to orchestrate an army of AI agents to perform a specific function well.

The number one skill that is going to be relevant two years from now is to become a functional expert that knows how to build AI agents to do that function and orchestrate an army of AI agents to do that function well.

Gokul points to a non-technical product manager at Meta as an example. This person built AI agents to handle his job so effectively that even the engineers asked for his help. This highlights a shift where humans act as managers of AI agents rather than managers of other humans. This also changes how we think about traditional management roles. If a leader manages fewer than 15 people, they likely have enough time to also be an individual contributor. Management should only be a full-time job if the span of control is significantly larger.

Hiring should focus on builders and doers rather than managers for as long as possible. While engineering roles typically use coding interviews to assess skills, other functions often allow candidates to talk their way through without demonstrating actual work. Gokul suggests that every function needs a work project. This involves putting a candidate in a room to complete a task very similar to what they will actually do on the job.

Every function needs to have a work project where you put them in a room and get them to do the work that is ideally very similar to the work they are going to do. For product managers, we would take a product we were thinking about and say, here is a product we are thinking about. Figure it out. Should we build it?

A vital trait to look for in these projects is agency. The best candidates often reject the premise of the assignment entirely. For instance, a great product manager candidate at Square once interviewed actual customers on the street and concluded that a proposed product should not be built at all. This shows they are taking the voice of the customer seriously rather than just following instructions.

The best PM candidates rejected the premise completely and they did it in a beautiful way. They went and talked to ten customers on the street. They said, I talked to ten customers and we found that none of them want this product. So we do not build it. That is what you want to see. You want agency. You do not want people to just say, give me what to do and I will do it.

DoorDash used a unique work project where candidates were given a small amount of money and tasked with acquiring a thousand customers in a few hours. While almost no one hit the target, the project effectively filtered for people who were willing to try many different tactics quickly. This approach clearly separates those who want to do the work from those who just want to talk about it.

Building a successful career requires staying at a job long enough to produce meaningful results. A common mistake in the current job market is the rise of job hoppers or job optimizers. These individuals often switch roles every 12 to 18 months. This pattern is a major red flag for hiring managers because it suggests a lack of commitment and an inability to achieve something of lasting value.

I don't think you can achieve anything of value. You can't have any impact on a company in 12 to 18 months. I think it takes minimum three to four years to have impact at the company.

Gokul suggests that people should aim for a minimum of three to four years at a company to truly have an impact. While one short stint might be acceptable if a situation does not work out, multiple short stays in a row will likely lead to rejection from future employers. Hiring managers want people who stick around to build things. Moving too quickly is short term thinking that prevents you from building a solid network or a reputation for creating value.

People want people who stick around and build. Who's going to hire you if they see that's your behavior? You've got to build something of value and that comes with time.

When evaluating a potential investment, the most critical quality to identify is founder authenticity. This trait often manifests as a genuine curiosity about a specific problem rather than a desire to simply build a large company. Many iconic businesses, including Google, Facebook, and Doordash, began as small toy problems that the founders were simply curious about solving in a college setting.

The most important thing I look for is founder authenticity. Three of the four companies I worked with, Google, Facebook, and Doordash, all started in colleges and they all started as a way to just a toy problem almost that the founders are curious about. They started with an authentic curiosity.

Gokul Rajaram notes that these successful founders typically begin by asking if something can be built and then following that curiosity. Whether solving a personal pain point or a technical challenge, the foundation of a great company is often rooted in this authentic drive to find a solution.

Gokul asks every founder to share their founding story. This story reveals why they chose a specific problem and highlights their unique strengths. It is a mistake to start a company just because you want to be an entrepreneur or work with a friend. Instead, a founder should have an authentic lived experience that compels them to solve a particular problem.

I want to understand, is there an authentic lived experience that they have had in their life that compels them to work on this problem?

Dylan from Figma is a great example because he is deeply focused on design and has a clear vision for how to make things more compelling. Max Rhodes, the CEO of Fair, found success by drawing on a challenge from his past. When he was a student, he struggled to get distribution for his umbrella company in local shops. He realized that many other manufacturers faced this same hurdle and decided to build a B2B marketplace to solve it.

Gokul shares a key question he asks during his first meetings with founders: "Tell me about how you navigated the idea maze." Every product starts with a problem, but there are many ways to solve it. Gokul wants to know why a founder chose their specific path. He often challenges them by suggesting several alternative solutions to see if they have done their research. He looks for founders who are students of their industry and its history.

Tell me about how you navigated the idea maze. You want to tackle this problem because, again, this is a classic product thing. You start with the problem, but then there are many different solutions, many different ways to solve it. Why did you choose this way versus the other way you could have chosen?

The best founders study why previous companies in their space succeeded or failed. Gokul points to the Collison brothers at Stripe as a great example. They studied the history of payments to understand why past companies made specific decisions. This deep understanding allows founders to stand on the shoulders of giants rather than repeating old mistakes. They should be able to explain why their problem cannot be better tackled through a different historical approach.

Serving on a board offers a unique perspective that can improve a leader's ability to manage their own executive team. For CEOs, joining another company's board is a valuable exercise. It helps them understand the dynamic from the other side. A strong board should include people who can help the company in its most critical areas. Historically, tech boards lacked product or engineering expertise. Today, having one or two people with that background is essential for any modern tech firm.

It is also vital to include a member who represents the voice of the customer. Gokul points to the example of Square, where they added the CEO of Shake Shack to the board to provide this perspective. When selecting new members, CEOs should treat the process like a marriage because it is difficult to undo. It is best to spend at least a year working with a potential candidate in an advisory capacity before inviting them to join formally.

Never, ever, ever invite anyone to join your board before spending at least a year with them. Have them join an advisory board. Have them meet with everybody on the management team. Spend time with them. Have them come to a few board meetings. If you feel they are adding value, then make them a board member.

The relationship between the board and the management team has also evolved. While boards used to meet primarily with the CEO, it is now a best practice to have the entire management team attend meetings. This allows board members to evaluate potential successors and understand the capabilities of different departments. A board buddy system can further strengthen these ties. In this model, each board member is paired with a specific executive to serve as a sounding board between formal meetings. Most of the real work and progress happens in these interactions between board sessions rather than during the meetings themselves.

Influencers have become the most powerful force for consumer-focused companies, particularly with younger audiences. TikTok has effectively turned into a highly efficient local search engine, often surfacing unique destinations that traditional tools like Google Maps or Yelp overlook. The challenge for brands is scaling these connections across a long tail of influencers who might go viral unexpectedly. New companies are emerging to help brands manage these viral waves and connect with influencers in more scalable ways.

In the enterprise space, the most significant shift is toward outcome-based selling. Rather than simply explaining what a product does, companies are collaborating with customers on specific goals. Gokul notes that Palantir uses this model effectively to build confidence with potential clients.

Palantir goes to customers and says, what is your most important business problem? Okay, great. Give us six months to solve it. Engage with us. If we cannot solve it, fire us. Do not pay us anything. If we solve it, pay us a lot of money.

Founders must stop leading their pitches with product features. Instead, they should lead with the outcome they can deliver or have already delivered for others. This approach works best when combined with a strategy of vertical targeting. Success in one vertical does not necessarily translate to another, so focusing on winning a lighthouse account in a specific industry is crucial. This creates a ripple effect where other companies in the same field feel compelled to follow suit.

If you get JP Morgan to use your product, I promise you every single bank will then evaluate your product. But if you get Procter and Gamble, JP Morgan does not care if Procter and Gamble uses your product.

Gokul Rajaram recalls a pivotal moment when Bob McDonald hired him out of business school. At the time, Gokul was a student on a visa with no prior experience as a product manager. Despite having his pick of any candidate for a high profile Sequoia-funded company, Bob chose to take a chance on him. This single act of kindness brought Gokul to Silicon Valley and changed the trajectory of his life.

Bob saw a spark in me and said I am going to make a bet on you and I am going to hire you and I am going to bring you to Silicon Valley. He could have had a pick of anyone, but he bet on me.

This experience led Gokul to adopt a philosophy of paying it forward without any expectation of return. He stays grounded by recognizing that he exists in a very fortunate situation. He believes that in most other versions of reality, things could be much worse. This perspective fuels a deep sense of gratitude and a feeling of responsibility to others.

You basically realize that you are very lucky to be given this one life and you have a responsibility to the world and yourself to be grateful and to lead the best life you can.