Each tool is powerful on its own, but complexity skyrockets when multiple tools are introduced. Managing permissions, syncing data, and troubleshooting automations add unwanted challenges. Instead of accelerating work, no-code has, in many cases, turned operators into system integrators. I’ve sometimes fallen into the trap of spending less time with users because I’m deep in development mode.

The Integrator’s Burden: When No-Code Feels Like Code

No-code promised to empower business users and turn every company into a tech company. Yet, being a no-coder sometimes requires advanced problem-solving, API knowledge, HTML/CSS basics, automation maintenance, etc. While the no-code interfaces remove the need to write code, they still introduce friction, especially when you don’t get interoperability out of the box and try to connect tools never designed to work together natively.

The typical inbound lead capture and routing is one of the workflows I’ve built and maintained the most. This workflow helps startups and SMBs understand where to send a website lead. These are some of the tools and steps that have been necessary to create such a workflow:

• Capturing leads using a form like Typeform, Airtable, Tally, etc.

• Sending the lead to a CRM like HubSpot, Salesforce, Airtable, etc.

• Enriching the lead with tools like Clay or Clearbit

• Automating email communications either natively in a CRM or through SendGrid or Mailchimp

• Inviting leads to schedule demo calls with Calendly, Cal, or Google Calendar

• Recording demo calls, updating lead records, and assigning them to the next stages

By the time you finish stitching up those tools in Make or Zapier, you’ve created a system that requires constant maintenance and attention to ensure it works as designed. Want to track analytics? Good luck! You now have to go to each one of those tools every time you or a stakeholder has a question about performance, engagement, etc. You can see a real example below of a workflow built with 5+ tools inside of Make to accomplish the goal of “talking to a lead.”

The Real Job-to-Be-Done: Why Users Want Outcomes, Not Tools

You’ve probably heard the phrase: “People don't want a quarter-inch drill; they want a quarter-inch hole.” My version of that phrase in the no-code world is that people don’t wake up thinking, “I need an automation tool.” They think:

• “I need to close more deals.”

• “I need to streamline my hiring process.”

• “I need to generate leads effortlessly.”

The tech industry still sells tools, but the real opportunity is in selling outcomes. Instead of expecting users to assemble their own stack, the future lies in solutions that deliver business results out of the box and focus on helping the user progress through “Jobs to be done.”

Who is Already Collapsing the Stack?

Some companies are already solving this problem by creating end-to-end solutions that eliminate the need for multiple tools:

• Fillout vs. Typeform+Make+Airtable+Calendly+Stripe: Fillout captures user data in forms and enables downstream workflows like scheduling, approvals, payments, document creation, and emailing. It eliminates the need to stitch multiple solutions together through Make or Zapier. 

• Shopify vs. Webflow+Stripe+Zapier+Klaviyo: Shopify bundles commerce, payments, and email, eliminating the need to integrate multiple solutions. 

• Glide vs. Bubble + Airtable + Zapier: Glide simplifies internal tool creation without requiring separate data and automation layers.

See the pattern? The winners aren’t the most flexible or the most feature-rich—they’re the ones that solve entire workflows out of the box.

The No-Code Future is Not "More Tools"—It’s Fewer.

The next wave of no-code innovation won’t be about adding more tools—it will be about removing them. The winners will be those who collapse the tool stack, making technology disappear into seamless workflows.

No-code will finally deliver on its promise when the operator never even realizes they’re using it.

How Did We Get Here?

Quoting Ernest Hemingway, “It happened slowly, then all at once”. The sales function as we knew it in tech started in the late 1990s, peaked in the 2010s, and started its downfall after COVID-19. With the rise of Gen-AI in late 2022 and mainstream adoption in 2023, the sales function as we knew it collapsed.

• The Genesis Years: 1990s - 2009 — Pioneered by tech juggernauts like Salesforce, Oracle, and Dell, who transitioned from field sales to tech-enables sales leveraging phone calls and emails

• The Golden Years: 2010 - 2019 — Perfected by new entrants like HubSpot, Salesloft, Outreach, and ZoomInfo, who leveraged CRM and automation to scale inbound and outbound motions

• The Dying years: 2020 - 2023 — Declining cold email success and over-reliance on “spray & pray” and inbox saturation on email and LinkedIn, and the birth of LLMs

• The GTM Engineering years: 2023 - ongoing — AI-first, automation, and product-led-growth enabling the rise of GTM Engineering by scaleups and startups like Clay, Cargo, Pocus, and Mutiny

Seeing the Evolution From the Trenches

My product management career has focused on building product surfaces for internal teams. Some of those products were built in actual code, while others were powered by no-code and workflow automation. My “clients” have included ops teams, sales teams, marketing teams, company executives, and even founders who needed better tooling for fundraising or time management.

In many ways, I was a GTM Engineer before the concept was even understood, let alone coined. During 2022-2023 at MarketerHire, I built the infrastructure for a new business line with an initial ARR of $200k and helped scale it to $2M ARR. I was effectively the “engineer” for the sales team, piping website leads to the CRM and beyond throughout the sales cycle. Below is the tech stack and steps a lead traveled through from inbound prospect to becoming a client***:

• Mutiny: A/B testing copy and landing pages

• Segment*: sending website and lead traffic data to CRM

• HubSpot: managing leads with 100+ workflows and fully automated to minimize AE data input

• Clearbit: enriching capture leads to avoid form churn and keeping the form short

• Webflow**: hosting forms and scoring leads before sending to Chili Piper

• Typeform: lead capturing on the website

• Chili Piper: lead routing depending on scores, geo, and other parameters

• Airtable: matching demand requirements with supply (contractors)

• PandaDoc: automating contract creation

• Make: connecting and managing automation when not available natively in tools

I’ve seen firsthand how GTM Engineering has been the missing piece in the GTM strategy for startups. Building the infrastructure above for MarketerHire took more time aligning stakeholders and getting the green light than the actual buildout. Team leaders can be incredibly siloed and not know how intertwined their tech and operations are. While running user interviews, one of the sales leaders told me they hadn’t talked to a product person in over a year. That’s how disconnected sales and engineering teams can be! GTM Engineering is the glue merging the technical and non-technical efforts of startups.

*A former engineer had integrated Segment to HubSpot and it was never refactored, but this step would’ve been “cleaner” passing lead parameters straight to HubSpot without going through Segment

**Similar to the above, a former engineer wrote JS code to do lead routing on the front-end (not needed) when it should’ve been done in Typeform.

***One major thing missing from this workflow was giving high-score leads a chance to talk to a sales team member immediately. The leadership had little appetite for experimentation, and this was a missed opportunity. Nobody wants to wait days to talk to someone when interested in a product!

Implications for Startups

Collapsing the talent stack for any function is a good outcome for startups. The obvious benefit is a reduction in workforce. Whereas the original inside sales playbook required SDRs, AEs, and Sales Engineers, the modern sales stack led by a GTM Engineer requires one person owning those three functions.

Another benefit for startups comes through the breakdown of silos. Whereas inside sales required a lot of data transfer between all the people involved in a sale, a GTM Engineer will hold all the necessary knowledge about who the lead is, where it came from, and how to engage in running a demo or onboarding them into the product.

Finding and managing GTM engineers is a challenge for startups. This function is less than three months old. Technical people rarely want to get involved with sales, and salespeople rarely have the patience or skills to build software. Managing GTM Engineers will also pose a challenge to startups. Who should they report to? Should they report directly to Ops, Sales, or the founders? Should they manage a team and own P&L?

Implications for Talent

The significant implication for talent is rising to the occasion. As the sales function evolves, individuals in each role will see pressure to grow and learn adjacent roles. Not everyone will be able to level up to becoming a GTM Engineer, but below is a roadmap for those who are ambitious and want to bet on this function:

SDRs/BDRs:

• Master AI-driven prospecting tools like Apollo, Clay, and LLMs (ChatGPT, Anthropic, etc.)

• Leverage predictive analytics and intent data like Koala and RB2B to engage with warm leads

• Get comfortable with video and async selling tools like Loom and Vidyard to stand out in a crowded prospect inbox

Account Executives

• Automate data entry and lead cycle management with native AI capabilities of existing CRMs

• Leverage lead insights and predictive data to engage with warm leads

• Get comfortable with technical lingo and be a power user of the product you’re selling

Sales Engineers

• Automate demo creation and workflows

• Leverage LLMs to draft custom responses and even mitigate live pushback

• Get comfortable with RevOps data to prioritize high-impact demos and work with product to lean into PLG motions

Business Name

• Hourly Hive

Tagline

• Hourly Hive is your on-demand tactical partner. Our experts do the work that advisors won’t do, and ChatGPT is erratic.

Business Overview

• Mission Statement: To increase business velocity by matching functional experts in tech with startups and SMBs. We match the supply and demand of knowledge through transparent engagements that companies trust.

• Vision Statement: To lower the barrier of knowledge and execution for businesses around the world.

• The Problem: Speed is the biggest advantage startups and SMBs have in the race to create value in the economy. When entrepreneurs need help, they have options, but talking to an Hourly Hive expert is faster and more accurate. Advisors are great at sharing knowledge, but they don’t usually get in the weeds. ChatGPT, Claude, and internet forums have good content but typically lack the context and tool permissions to do the work.

• The Solution: Hourly Hive is a vetted marketplace where founders, functional leaders, and business owners can book hourly sessions with experts from the tech world. We connect supply and demand through “atomic units of work,” meaning doing work that takes one hour or less, which is critical to keeping the momentum of a team or business. Our experts are vetted and required to share their screens when working, creating trust during the engagement. The price is fixed, so no one on the platform can compete for the price. The only way for experts to increase their bookings is through more availability and client reviews.

Product/Service

• Description: a marketplace to connect supply and demand for business knowledge.

• Unique Selling Proposition (USP): Our experts must share their screens while executing work for companies, and the price is fixed. This builds transparency from day one and will keep the demand returning for more.

• Development Stage: early beta, currently seeding the supply side.

Target Market

• Demographics and Psychographics: our target market is startups and SMBs across the world who don’t have the resources or the capacity to hire functional experts full-time and don’t want to get overcharged by agencies or consultants

• Customer Need: Companies need external help to fill in the gaps in knowledge and execution. Agencies, consultants, advisors, and talent marketplaces have built their businesses by providing value but usually taking advantage of companies by arbitrating knowledge. Founders and business owners need more options when hiring talent for short and specific work in hourly, transparent engagements.

Business Model

• Revenue Streams: there are three main revenue streams for Hourly Hive

  • Take rate: 50% of the transactions (currently priced at $199/hour for launch)

  • Recruiter fee: 20% of the first year’s salary of an Hourly Hive member who ends up hired by one of the companies they worked with through the platform

  • Membership: TBD on the amounts and whether this would be charged to one or both sides of the marketplace

• Pricing Strategy: the price for the beta launch will be $199 per hour. Rates will go up as the platform increases its reputation.

Marketing and Sales Strategy

• Marketing Channels: Hourly Hive will build a repository of knowledge throughout time. This knowledge will be turned into content marketing materials to share across networks and communities to increase awareness.

• Sales Tactics: we will leverage online communities to avoid the cold-start problem. Once the platform gains momentum, we will build partner channels and inside sales teams to sell upmarket.

Competition

• Competitors: our main competitors are advisors, consultants, agencies, and talent marketplaces.

• Competitive Advantage: we have a few advantages over the legacy business models mentioned above.

  • Speed: our vetted experts are ready to work on demand. It’s like opening up Uber or Lyft but for tech operators. By decreasing the scope to atomic units of work we’re able to reduce the time to value for the demand side.

  • Transparency: our experts share their screens with clients. That’s how much we believe in our vetted experts and sharing knowledge.

  • Pricing: talent can’t compete with each other on pricing. This builds trust between the platform and both sides of the marketplace while incentivizing the supply side to be readily available for work.

Team

• Key Team Members: Pablo Pinto and Pablo Martínez-Falero are the co-founders. Both Pablo’s have been working together in startups since 2021. See their brief bios below:

  • Pablo Pinto: exited founder turned product manager building talent marketplaces for 5+ years. Pablo is experienced in startup GTM, strategy, and recruiting. He has built a deep network in the tech talent ecosystem, which is his distribution advantage.

  • Pablo Martinez-Falero: mechanical engineer turned full-stack no code/low code builder. Pablo is a systems thinker with a knack for finding software-driven solutions to business problems.

How Did We Get Here

Before we dive deeper into the new talent stack it’s worth doing a quick walkthrough of how we got here. In the last five years leading up to the launch of ChatGPT in late 2022, the most impactful tectonic shifts that got us here are listed below. I won’t go into detail about each one since there’s enough content written online about those shifts but they are worth mentioning as the context for this post!

Tectonic Shifts in the Tech Talent Ecosystem

• The gloating years: 2017 - 2020 — Team size as a proxy for growth

• The COVID rebound: 2020 - 2021 — Hiring wars

• Raising interest rates: 2022 - 2023 — Layoffs

• ChatGPT: 2022 - ongoing — Efficiency

The Impact of Talent Dynamics on Tech Teams

If I had to summarize the talent dynamics of the last five years it would be through an inverted U graph. On the X-axis you’d have a time horizon and on the Y-axis you’d have team size. Startups grew their teams aggressively through the post-covid boom until interest rates began to rise. Rising interest rates led to massive layoffs between 2022 and 2023 that have so far impacted ~500k individuals according to Layoffs.fyi.

But austere measures in startups and the VC ecosystem weren’t the death sentence of tech talent. The nail in the coffin for tech employees was ChatGPT. The emergence of ChatGPT and the hundreds of Gen-AI tools and LLMs that followed since late 2022 have completely reshaped the talent dynamic of tech companies.

It’s too early to tell if the talent dynamics are more or less favorable for tech employees or companies. What is evident is that the talent ecosystem is different. Both companies and individuals can become more productive by leveraging AI and by collapsing the talent stack, as Scott Belsky coined, in order to keep talent resourcing efficient.

Enter, the new talent stack for the next ten years.

The New Talent Stack

The benefits of AI and leaner teams have gotten founders and investors excited about increasing the performance of their teams. Individuals should be excited as well. AI is the first platform innovation in more than a decade and the promise for individuals to improve their day-to-day is pretty stark.

Operators should think about their value as a pyramid of value (aka “Productivity Pod”) instead of individual value. Depending on your role and seniority, there might be some permutation of the productivity pyramid which looks like this:

The Three Layers of the Productivity Pod

1. Foundation layer: Automation

   1. Personal or company GPTs

   2. AI-generated content

   3. OpenAI’s API

2. Middle layer: Virtual Assistant (VA)

   1. Repetitive tasks that can’t be automated

   2. Vetted, offshore VAs with functional expertise

3. Top layer: functional expert

   1. Strategic thinking

   2. Creative thinking

   3. Connecting the dots

   4. Managing teams

Examples of Functional Productivity Pods

Product

• Product Lead’s pod instead of:

  • Product Manager

  • Product Ops

  • UX designer

Recruiting

• Head of Recruiting’s pod instead of:

  • Recruiting Lead

  • Recruiting Coordinator

  • Recruiting Operations Manager

Sales

• Sales Lead’s pod instead of:

  • Account Manager

  • SDR

  • Sales Ops Associate

Marketing

• Content Marketer’s pod instead of:

  • Content Marketer

  • Copywriter

  • Graphic Designer

Increasing Your Productivity

Productivity pods should increase the performance of most teams. For the most data-oriented, you can demonstrate this numerically by using Katie Dill’s formula: Performance = potential - interference. Katie is the Head of Design at Stripe and goes in-depth about team performance in this episode with Lenny.

💡 Formula for performance: performance = potential - interference (potential minus interference)

There’s no doubt that AI increases individual potential. With higher potential, the numerator in the formula increases, therefore, increasing productivity for individuals and teams. It’s as simple as that.

Unit Economics of Productivity Pods

The figures will vary depending on functions and seniority but will hover at around the following numbers:

Resourcing for Traditional Teams (per year)*

• Cost of functional expert: $150k - $200k

• Cost of functional associate or manager: $100k

• Cost of ops associate or ops manager: $100k

💡 Total cost of running traditional teams*: $350k - $400k

Resourcing for Productivity Pods (per year)

• Functional expert: $150k - $200k

• Virtual assistant: $30k

• Tooling: $10k

💡 Total cost of running productivity pods: $190k - $240k

*The range depends on whether the traditional team operates with two or three people

Finding or Building Your Productivity Pod

If you’ve read this far, you’re probably thinking about how to find or build your productivity pod. There are multiple ways to leverage AI and the talent-collapsing approach. At the risk of simplifying it, here’s how I think founders and operators should approach building out their productivity pod:

Founders

1. Make sure your functional leaders are experienced and understand this vision of a productivity pyramid

2. Provide the resources to equip functional leaders with VAs. There are dozens of companies offering VAs either for specific functions like MarketerHire or more general VAs like Athena

3. Encourage your technical team to build foundational automation, including OpenAI’s API, and coach your functional leaders to maintain those systems. Alternatively, if you prefer to offload the technical automation work you can leverage talent marketplaces like WeLoveNoCode to find expert no-coders to help you build your infrastructure.

Operators

1. Invest in your personal development by joining cohorts or online certifications. Examples of these could be DemandCurve for Growth or Product School for Product Managers.

2. If you haven’t used a VA before, ask your network for help. There are so many leaders in tech who have now learned the ins and outs of working with overseas VAs. There’s a learning curve so make sure you don’t overlook this one!

3. Pick up or sharpen your technical skills by learning how to use automation tools like Airtable, Make, and Zapier, or if you’re a newbie, start with the basics with courses like the ones offered by No Code MBA

Conclusion

We’re in the early days of the AI revolution. We can’t yet tell if the impact of AI will benefit the supply side or the demand side more in the talent ecosystem. What is certain is that AI is here to stay, and as with all platform revolutions before this one, the earlier you embrace it, the further ahead of the curve you will be.

Who am I? I’m a former founder turned product leader who enjoys the chaos of the startup journey in the early-stages. I have experience in product development, GTM, Ops, and recruiting. Also, I’ve been an angel investor since 2022.

You can expect my background to shape the content of this publication, exploring things like:

• Product adjacencies

• Shifting talent dynamics

• GTM strategies

• Operationalizing your functional teams