AI is separating winners and losers in GTM faster than before. At Clay, after watching thousands of companies’ GTM strategies, we've identified three laws that govern success as GTM enters the AI era.
The same laws have always applied to GTM, but AI amplifies them. In a world where your competitors can copy and scale your best tactics in weeks, as opposed to months, you need to evolve faster.
The first law: you need to be unique
Generic outreach is just noise. You win by knowing something about your customers that competitors don't.
This first law is foundational—and always happens to be the one most teams violate. If you’re doing something like commodity targeting—reaching the same prospects as their competitors with the same generic messages—you’re violating this law.
At Clay, we call the alternative finding GTM alpha, i.e. your unique competitive edge. To find GTM alpha you need to get hyper-specific and obsessively understand your customers: knowing exactly who they are, when they need you most, and how to reach them at that precise moment.
AI makes the bar for uniqueness higher than ever. Personalizing emails with a company's name or recent news is table stakes. Any SDR can do that with ChatGPT in seconds. Being unique now means going beyond what AI makes easy for everyone else.
For example, one of our customers is a logistics company. They wanted to target warehouses of specific sizes, but traditional databases were stale and inaccurate.
The most successful teams we work with understand that go-to-market is creative, not mechanical. Unlike engineering, where you build something once and it works consistently, growth is subjective. You're competing for human attention in an increasingly noisy world.
Instead of buying more data, those teams got creative: they used Google Maps to identify warehouses, then deployed AI to count parking spots in satellite images. It turns out parking spots predict warehouse size better than any database ever could, and they found a lot of success taking a creative approach that AI couldn’t replace. That's GTM alpha.
The second law: no creative advantage lasts forever
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Every GTM winning strategy contains the seeds of its own destruction.
Whatever works today will stop working as others copy it or you exhaust the channel. The cold email template that got 15% response rates six months ago? It's probably getting 3% now—and not so new anymore.
AI makes saturation happen faster. Your competitors can copy and scale winning tactics across thousands of companies in weeks, not years. Your brilliant LinkedIn outreach strategy is likely being automated by your competitors before you've even finished your quarterly review.
Success breeds obsolescence. The companies that win are simply the ones that can discover, test, and scale new tactics fastest.
The third law: the fastest to iterate wins
Your only sustainable advantage is your ability to discover new advantages faster than competitors. This is the reality of modern growth: there is no permanent competitive advantage, only the continuous pursuit of temporary advantages.
The companies that win adapt to this reality. They don't get attached to their current tactics; they build systems that assume their current approach will become obsolete.
This is where the concept of GTM Engineering becomes critical. GTM Engineering is building revenue engines using AI and automation. GTM Engineers have what we call "ambidextrous brains"—they can think creatively about business problems and build technical systems to execute on those ideas rapidly.
Instead of having individual reps manually executing one-off tactics, GTM Engineers turn creative insights into testable, scalable processes. One GTM Engineer can take a single SDR's breakthrough insight and transform it into an automated workflow that serves the entire organization. For example, Canva’s GTM AI team automates workflows such as transcript summarization, while a separate enrichment team pipes novel data into those plays.
The companies that win aren't necessarily those with the best current strategy—they're the ones with the best systems for constantly discovering new strategies.
Building for the age of acceleration
The three laws of GTM have always existed, but AI has amplified them. What used to happen over quarters now happens over weeks. What used to require teams now requires entire systems.
This is why we've built Clay as an IDE—a command center—for GTM. It's designed to help GTM Engineers iterate at AI speed with:
- 150+ integrations for any data you need
- AI agents that can research anything (like our Claygent, which has powered over 1.5 billion research runs and is on track to hit 2 billion by end of year)
- Connections to activate insights across your entire tech stack
But creativity needs systems to scale. Just like engineers program code, GTM engineers can program revenue. The difference is that revenue programs need to constantly evolve.
AI is separating winners and losers in GTM faster than before. At Clay, after watching thousands of companies’ GTM strategies, we've identified three laws that govern success as GTM enters the AI era.
The same laws have always applied to GTM, but AI amplifies them. In a world where your competitors can copy and scale your best tactics in weeks, as opposed to months, you need to evolve faster.
The first law: you need to be unique
Generic outreach is just noise. You win by knowing something about your customers that competitors don't.
This first law is foundational—and always happens to be the one most teams violate. If you’re doing something like commodity targeting—reaching the same prospects as their competitors with the same generic messages—you’re violating this law.
At Clay, we call the alternative finding GTM alpha, i.e. your unique competitive edge. To find GTM alpha you need to get hyper-specific and obsessively understand your customers: knowing exactly who they are, when they need you most, and how to reach them at that precise moment.
AI makes the bar for uniqueness higher than ever. Personalizing emails with a company's name or recent news is table stakes. Any SDR can do that with ChatGPT in seconds. Being unique now means going beyond what AI makes easy for everyone else.
For example, one of our customers is a logistics company. They wanted to target warehouses of specific sizes, but traditional databases were stale and inaccurate.
The most successful teams we work with understand that go-to-market is creative, not mechanical. Unlike engineering, where you build something once and it works consistently, growth is subjective. You're competing for human attention in an increasingly noisy world.
Instead of buying more data, those teams got creative: they used Google Maps to identify warehouses, then deployed AI to count parking spots in satellite images. It turns out parking spots predict warehouse size better than any database ever could, and they found a lot of success taking a creative approach that AI couldn’t replace. That's GTM alpha.
The second law: no creative advantage lasts forever
Every GTM winning strategy contains the seeds of its own destruction.
Whatever works today will stop working as others copy it or you exhaust the channel. The cold email template that got 15% response rates six months ago? It's probably getting 3% now—and not so new anymore.
AI makes saturation happen faster. Your competitors can copy and scale winning tactics across thousands of companies in weeks, not years. Your brilliant LinkedIn outreach strategy is likely being automated by your competitors before you've even finished your quarterly review.
Success breeds obsolescence. The companies that win are simply the ones that can discover, test, and scale new tactics fastest.
The third law: the fastest to iterate wins
Your only sustainable advantage is your ability to discover new advantages faster than competitors. This is the reality of modern growth: there is no permanent competitive advantage, only the continuous pursuit of temporary advantages.
The companies that win adapt to this reality. They don't get attached to their current tactics; they build systems that assume their current approach will become obsolete.
This is where the concept of GTM Engineering becomes critical. GTM Engineering is building revenue engines using AI and automation. GTM Engineers have what we call "ambidextrous brains"—they can think creatively about business problems and build technical systems to execute on those ideas rapidly.
Instead of having individual reps manually executing one-off tactics, GTM Engineers turn creative insights into testable, scalable processes. One GTM Engineer can take a single SDR's breakthrough insight and transform it into an automated workflow that serves the entire organization. For example, Canva’s GTM AI team automates workflows such as transcript summarization, while a separate enrichment team pipes novel data into those plays.
The companies that win aren't necessarily those with the best current strategy—they're the ones with the best systems for constantly discovering new strategies.
Building for the age of acceleration
The three laws of GTM have always existed, but AI has amplified them. What used to happen over quarters now happens over weeks. What used to require teams now requires entire systems.
This is why we've built Clay as an IDE—a command center—for GTM. It's designed to help GTM Engineers iterate at AI speed with:
- 150+ integrations for any data you need
- AI agents that can research anything (like our Claygent, which has powered over 1.5 billion research runs and is on track to hit 2 billion by end of year)
- Connections to activate insights across your entire tech stack
But creativity needs systems to scale. Just like engineers program code, GTM engineers can program revenue. The difference is that revenue programs need to constantly evolve.
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