Harry Stebbings

The trust economy replaces the feature factory In the era of hyper-scale AI, the fundamental unit of growth has shifted from functionality to trust. For decades, startups won by shipping features faster than the incumbent. But today, Elena Verna argues that software creation has become so democratized that functionality alone is a commodity. If any developer can prompt an LLM to recreate your core feature set over a weekend, your only remaining moat is the emotional connection you build with your user. Growth is no longer a tactical optimization problem; it is a trust problem. This shift demands a transition from the "Minimum Viable Product" to what Verna calls the **Minimum Lovable Product**. Software is now judged by the emotion it invokes. Humans naturally recoil from utilities and tools; they crave connection. As the Head of Growth at Lovable, Verna emphasizes that building a personality into the software is now the minimum bar to kickstart growth. The goal is to move beyond the base layer of functionality and security into a space where users actually vouch for the team behind the product. When functionality is ubiquitous, the "who" behind the code becomes more important than the code itself. Paid marketing is a death trap for early startups One of the most provocative stances Verna takes is her absolute rejection of paid marketing for companies in their first year. She labels it a **death trap**. Many founders, flush with venture capital, attempt to buy their way to product-market fit. Verna warns that this is essentially lighting cash on fire. Until a company has figured out stable, organic product-market fit through search, socials, or word-of-mouth, pouring money into the top of a leaky, unoptimized funnel is a recipe for a single point of failure. Furthermore, Verna dismisses Lifetime Value (LTV) as a relevant metric for young companies. Unless a business has been operating for at least five years, it does not actually know its LTV. Founders often use hallucinated LTV numbers to justify high Customer Acquisition Costs (CAC), leading to unsustainable burn rates. Instead, the focus must be on the **payback period**. If you cannot recuperate your investment within three months, the system is not self-sustaining. Relying on Google or Meta for more than 50% of your growth puts you at the mercy of their earnings calls; when they need to hit their numbers, they simply jack up ad rates, and your business collapses. The blurring of lines and the AI-native employee The traditional silos of marketing, product, and engineering are dissolving. In Verna's view, being "AI native" means every employee must be a generalist capable of shipping. At Lovable, the expectation is radical: every single employee, including those in growth and marketing, is expected to ship code to production. Verna herself now writes copy, builds prototypes, and ships apps without engineering support. This acceleration of the "blur" allows for a level of agility that larger, compliance-heavy organizations cannot match. This new paradigm requires a shift in hiring. While specialists like SEO experts still have a place, the most valuable assets are "jack-of-all-trades" problem solvers who can handle the kitchen sink. This autonomy extends to social presence. Verna advocates for **employee-led social branding**. If a founder is afraid to let their employees build personal brands because they might get poached, that founder has a cultural problem, not a marketing problem. When employees build in public, they become the most powerful, authentic marketing agents a company has. You effectively get an engineer and a marketer for the price of one. Why subscriptions fail the AI usage reality Monetization in the AI era is fundamentally broken. Most startups are currently passing through expensive LLM costs to their users via rigid monthly subscriptions. Verna argues that this model is a poor fit for the "bursty" nature of AI usage. Creativity and project needs are not consistent; a user might have a week of intense activity followed by a month of dormancy. Forcing these users into a recurring subscription creates friction and churn. Lovable's success with **top-ups**—ad hoc purchases on top of or instead of subscriptions—proves that flexibility drives incrementality. The fear that non-recurring revenue will hurt valuation multiples is a fallacy. As LLM costs inevitably collapse and become commoditized like cloud storage, the winners will be those who evolve their models toward **outcome-based monetization**. If your business is still treating pricing as a taboo subject that can't be adjusted, you are waiting for a collapse that is already imminent. The tactical return to out-of-home and community If given an unlimited budget, Verna would not double down on digital performance ads. Instead, she looks toward the physical world and the creator economy. Out-of-home (OOH) advertising is seeing a resurgence because it captures latent majority attention in a way that saturated digital feeds cannot. However, the execution must be characterful and risky. Boring, corporate AI slogans like "collaborative cloud transformation" are invisible. Marketing must make people chuckle or provide a memory to be effective. Community is another area where most startups fail by turning their forums into "holes of depression." When a community is used primarily as a support outlet for people whose problems haven't been solved, it becomes a dumping ground for negative sentiment. To build a real community, founders must identify their **early super-users** and empower them as ambassadors. This seeds the environment with positivity and inspiration rather than just troubleshooting. The goal is to create an earned channel that competitors cannot buy. Relevancy is the only sustainable moat In a world where OpenAI, Anthropic, and Google hold a terrifying grip on distribution, smaller players must stay top-of-mind through sheer velocity. Verna reveals that Lovable's strategy involves **launching something every single day**. This isn't just about shipping code; it's about constant noise. While marketing only puts its full firepower behind "Tier 1" launches every few months, the daily releases keep the product in the "habitual zone" of the user's mind. Falling into the "forgettable zone"—the monthly or quarterly check-in—is death for a startup. You have to be a living, breathing entity that evolves in real-time. This level of activity creates a "beeswarming" effect where employees support each other's updates on social media, amplifying the brand's reach organically. The future of growth isn't found in a playbook of the past; it's found in the agency of your team to experiment, take risks, and connect with customers on a human level.

The Great Software Shakeout and the Return of Fundamentals The current state of the SaaS market has triggered a widespread panic often referred to as a "sassacre." As public market valuations for software companies compress, many observers are questioning the long-term viability of the seat-based pricing model in the age of Artificial Intelligence. However, seasoned growth equity investors view this not as an apocalypse, but as a long-overdue correction. The reality is that the public markets are purging the excesses of the previous bull cycle, where revenue growth was prioritized over unit economics and sustainable free cash flow. Incumbent giants like Workday and Salesforce are being pummeled by Wall Street analysts who behave like squirrels, shifting their sentiment the moment numbers need to be adjusted. But these incumbents possess three things that startups struggle to replicate: distribution, data, and massive balance sheets. While the law of large numbers naturally forces a deceleration in growth, the profitability of these businesses remains a fortress. The "dead money" phase for these stocks is a gift for disciplined buyers who recognize that the infrastructure of global business does not vanish overnight just because a new technology emerges. The China AI Hegemony and the ByteDance Advantage Western markets consistently underestimate the technological prowess emerging from the East. ByteDance is currently the most advanced AI company in the world, yet it remains underappreciated by Western investors who view it through a narrow geopolitical lens. The sheer volume of AI integration within their platforms, combined with a relentless focus on growth and massive earnings power, positions them to dominate the next decade of technological evolution. China has structural advantages in the AI war that the United States is only beginning to realize. The ability to build nuclear power plants and massive solar farms in a fraction of the time it takes in the West provides the energy backbone required for the next generation of data centers. AI is a power-hungry beast, and the U.S. will likely face significant local pushback as power prices spike and environments are impacted. Furthermore, the sheer number of PhDs and the cultural value placed on science and technology in China cannot be ignored. While OpenAI and Google command the headlines, the underlying infrastructure and execution speed in China may ultimately win the AI race. Solving for the Liquidity Crisis: DPI Over Marks There is a fundamental difference between a "mark" and math. In the venture world, valuations are often just opinions until a liquidity event occurs. The industry is currently facing a reckoning because too many fund managers treated unrealized gains as final victories. The reality is that buying is the glamorous part of the job, but selling is the actual work. A disciplined investor must constantly re-underwrite their positions, asking whether they would buy the stock at its current price today. Limited Partners are shifting their focus exclusively toward Distributed to Paid-In capital (DPI). The era of raising subsequent funds based on flashy internal rates of return (IRR) that exist only on paper is coming to an end. Investors must be willing to take chips off the table during liquidity windows, even if they believe in the long-term potential of a winner. Returning capital to investors is the only way to ensure the longevity of a firm. If you aren't returning money, you aren't in the investment business; you're in the asset collection business. Smaller, more nimble funds have an advantage here—they can sell secondaries without triggering the negative signaling that plagues massive firms like Sequoia Capital. The Most Critical Metric: Gross Dollar Retention In the search for the next breakout success, investors often get blinded by net dollar retention, which includes upsells and expansions. This is a mistake. The single most important metric for a software company's health is Gross Dollar Retention (GDR). GDR measures how much of your existing customer base you keep without the masking effect of new sales. Anything below 80% GDR is a red flag, indicating a "leaky bucket" where the company must spend aggressively on sales and marketing just to stay in place. A company with 95% or 98% GDR can grow exponentially because its base is stable. These are the businesses that survive technological shifts. The "living dead" of the venture world are companies that scaled to $100 million in revenue but have GDR in the 60s or 70s. They are churning through customers and will eventually hit a wall where they can no longer outrun their own attrition. The Purge: Why 50% of VCs Must Go The venture capital industry is bloated with "tourists" who entered the market when capital was cheap and every idea seemed like a billion-dollar opportunity. At least 50% of people currently in the venture business likely add negative value to their portfolio companies. They overpromise, under-deliver, and often push founders to burn cash at unsustainable rates to justify inflated entry prices. True value-add doesn't come from a VC pretending to know how to run a sales team; it comes from being a "switchboard." The best investors connect founders with the talent that has actually done the work before. They get out of the way and let the entrepreneurs execute. The next three to five years will see a massive contraction in the number of firms as LPs stop funding managers who fail to produce liquidity. This culling is necessary. It will return the industry to a state of discipline where price matters, and the pursuit of the power law is balanced by fundamental business sense. The Inevitable Downturn and the AI Productivity Boom Markets do not move up forever. We are likely staring down a significant downturn within the next decade, fueled by geopolitical tensions and the eventual exhaustion of current government policies. While this sounds dire, it will represent the greatest buying opportunity in a generation. The first generation of AI companies—those raising billions on napkins—will likely go bust, much like the first wave of internet companies in 1999. However, the companies that emerge between 2024 and 2027 will be the giants of 2035. This downturn will coincide with a massive productivity boom as AI is finally integrated into the back offices of traditional industries like healthcare and manufacturing. We are still in the "early innings" where companies are restricted by regulation and infrastructure. Once these barriers fall, the efficiency gains will be staggering. The investors who survive the current purge and maintain their capital will be the ones to ignite this next market cycle. Stay liquid, stay disciplined, and be ready to move when everyone else is paralyzed by fear.

The public markets are currently treating the software sector like a terminal patient, but Eran Zinman isn't interested in the funeral rites. As the co-CEO of monday.com, Zinman has watched his company’s valuation compress even as fundamentals remain resilient. The disconnect between business operation and market sentiment has birthed a series of doomsday prophecies: that AI will allow everyone to build their own software, that foundation models will swallow the application layer, and that agents will render interaction platforms obsolete. Zinman dismisses the noise, arguing that we are entering the most aggressive growth phase in the history of technology. Death of the seat-based economy The most structural threat to the legacy SaaS model isn't just the existence of AI, but the collapse of the headcount-linked pricing model. For twenty-five years, software value was tethered to the number of human beings clicking buttons. If AI can perform 80% of the work previously done by humans, the traditional per-seat license becomes a liability for the vendor and a resentment for the customer. monday.com is currently navigating a pivot toward consumption-based pricing, acknowledging that value must be tied to output rather than payroll size. This shift is radical. It requires a total re-engineering of the go-to-market strategy, the product interface, and the revenue recognition models that investors use to judge health. Critics argue that moving away from seats will cannibalize revenue, but this perspective ignores the massive expansion of the Total Addressable Market. Zinman contends that while headcount spend might decrease, software spend as a percentage of corporate budgets will skyrocket. Companies that currently spend 8% of their budget on software and 70% on humans will see those ratios invert. The opportunity isn't about protecting the existing $1.3 billion in revenue; it’s about capturing a piece of a market that is set to expand by two orders of magnitude as software moves from being a tool for tracking work to a tool for doing the work. Vibe coding and the illusion of simplicity The concept of "vibe coding"—the idea that non-technical users can simply describe a software requirement to an AI and have it perfectly manifest—has become a viral existential threat. When a journalist built a functional clone of monday.com in a few hours using AI, it sent a shockwave through the investor community. Zinman views this as a fundamental misunderstanding of what makes enterprise software valuable. There is a massive delta between generating a user interface and maintaining a scalable, collaborative, and secure infrastructure that works across a ten-thousand-person organization. Building the first 10% of a tool is easy; maintaining the remaining 90% through years of organizational change is where the value lies. While consumer-grade apps might be vulnerable to this democratization of development, enterprise environments demand a level of cohesion that fragmented, self-coded tools cannot provide. monday.com is positioning itself not as a tool that can be replaced by a vibe-coded script, but as the underlying operating system where those agents and scripts are orchestrated. The goal is to move from being a system of record to a system of action, where the complexity is managed in the background while the user focuses on the strategy. Why the model companies won't kill the apps A persistent fear in the VC world is that OpenAI, Anthropic, and Google will move up the stack and render application companies like Salesforce or monday.com irrelevant. History suggests otherwise. Zinman points to the early days of AWS, when skeptics predicted Amazon would capture all enterprise value because they owned the infrastructure. Instead, the ease of infrastructure created a boom in application development. The model providers are focused on the massive opportunity of being the "backbone" of intelligence. Selling, implementing, and supporting complex enterprise software requires a completely different DNA—a sales-heavy, handheld process that model companies are ill-equipped to execute at scale. Furthermore, intelligence without context is useless. An LLM is brilliant but blind to the specific, undocumented strategies and workflows that live within a company's walls. The application layer provides that context. monday.com sees its future as the bridge between raw intelligence and the specific context of a business. By being the horizontal platform where humans and agents collaborate, they capture the data that makes the AI effective. The model providers might provide the engine, but the application layer provides the fuel and the steering wheel. Playing offense in a defensive market While most SaaS companies are cutting headcount and hunkerng down to survive the "SaaS Apocalypse," monday.com is maintaining a mid-teens headcount growth. This decision seems paradoxical to some, but Zinman views it as an offensive necessity. You cannot capture a 100x TAM expansion by playing defense. The company is aggressively integrating AI into its own internal operations—replacing its 100-person SDR team with agents and automating its customer support—not to reduce the total number of employees, but to reallocate human talent toward the high-leverage tasks of building the next generation of the product. Internal morale during a 60% stock drawdown is a management hurdle, but Zinman uses the low valuation as a psychological reset. When the market prices your company at a level that implies the business is worth nearly zero after accounting for cash, the only response is to go "all in." This involves taking big, calculated risks on vertical offerings like CRM and Service, and betting the entire platform on an agentic future. The companies that emerge from this cycle as winners will be those that didn't just survive the transition, but used the chaos to rewrite the rules of their industry. For monday.com, the objective is to move past the era of being a work management tool and become the essential orchestration layer for a world where agents do the majority of the heavy lifting.

The Autonomous Agent Tsunami Hits the Beach Jerry%20Murdock, the visionary co-founder of Insight%20Partners, views the current artificial intelligence wave not as a steady rising tide, but as a massive tsunami. For years, the water has been receding, pulling back to sea while the industry watched from the shore with a mix of curiosity and complacency. That period of observation is over. Murdock argues that the real danger of a tsunami isn't when it's out at sea; it's when it hits the beach. We are currently in the messy, violent transition where the "pre-peak" waves are beginning to dismantle established software structures. While the general public focuses on chatbots, Murdock identifies Autonomous%20Agents as the specific force that will redefine the next decade of enterprise value. These are not merely digital assistants; they are probabilistic entities capable of writing code, making purchasing decisions, and executing complex workflows without human intervention. This shift represents a transition from software as a tool used by humans to software as an employee that operates on behalf of the organization. Companies that fail to move to higher ground by becoming AI-native risk being swept away by a "Sassacre"—a systematic devaluation of traditional Software-as-a-Service (SaaS) models that rely on seat-based pricing and human-centric interfaces. Why Cursor and Legacy SaaS Face Instant Obsolescence The velocity of this disruption is perhaps best illustrated by the sudden vulnerability of yesterday's darlings. Murdock points to Cursor, a company currently valued in the tens of billions, as an example of a product that many AI-native founders already consider obsolete. While Cursor is a sophisticated tool for developers, the next generation of startups, such as E2B and Lotus%20AI, are utilizing autonomous agents to write the code itself, effectively bypassing the need for human-augmented coding environments. This isn't just about coding; it's a fundamental challenge to the "System of Record." Historically, companies like Salesforce derived their value from being the immutable source of truth for customer data. However, if autonomous agents begin to bypass these platforms or if new agents create their own decentralized systems of record, the massive market caps of legacy players could evaporate. Murdock compares Salesforce to Mount Everest—it won't melt overnight—but its value is directly tied to the health of the ecosystem built on top of it. As those smaller, integrated companies are disrupted by agents, the mountain itself begins to lose its stature. The bolt-on AI strategy, where legacy firms simply add a chatbot layer to their existing stack, is a defensive maneuver that Murdock suggests will rarely result in "gold medal" performance. The Migration from Nvidia to Custom Silicon One of the most provocative claims Murdock makes involves the eventual decline of Nvidia's dominance in the compute market. While Jensen%20Huang currently sits atop the world's most valuable hardware empire, the rise of open-source models like Llama%203 and DeepSeek is paving the way for ASIC%20chips (Application-Specific Integrated Circuits). As autonomous agents become more specialized, they will require chips tuned for specific workloads rather than general-purpose GPUs. Murdock suggests that the orchestration layer of the future will triage workflows: expensive, high-reasoning tasks might go to Claude%203.5%20Sonnet, while routine operations will run on cheap, local ASICs. This shift is already visible in the strategies of major tech players; Meta has notably pushed back against complete reliance on Nvidia, betting instead on custom silicon to gain an edge in efficiency. Even Nvidia’s acquisition of Grock (not to be confused with Elon%20Musk's Grok) signals their awareness that memory-on-chip capabilities and ASIC support are the next battlegrounds for CUDA viability. Parallels to the Dot-Com Bust of 2000 To understand the current market volatility, Murdock looks back to March 2000. He recalls the era when tech stocks dropped 40% in a single quarter, followed by a multi-year "malaise" that was eventually finalized by the tragic events of 9/11. The core issue in 2000 was a lack of infrastructure; the world wasn't ready for commerce on dial-up. Today, the infrastructure is here, but the speed of change is creating a similar environment of "cautious sidelines" investing. Public markets are reacting with extreme sensitivity to AI updates. When Anthropic releases a security feature, established players like CrowdStrike see their stock prices swing wildly. Murdock doesn't see this as simple panic; he sees it as a rational pause by investors who realize they don't have enough information to pick winners in a world where the application stack is being eaten by the model layer. The "Sassacre" isn't just a catchy term—it's a recognition that the metrics we used to value companies (revenue growth and margins) have become transient in the face of agent-driven automation. The Labor Market and the Rise of UBI The most significant implication of autonomous agents is their impact on the white-collar labor force. Murdock predicts that the first jobs to disappear won't be the ones currently held by senior staff, but the "next in line" roles: junior developers, executive assistants, and marketing coordinators. Because agents don't require sick leave, don't feel entitled, and can work 24/7 at the speed of compute, the incentive for small and medium businesses to replace human input with agent orchestration is overwhelming. This shift will move beyond the boardroom and into the halls of government. Murdock boldly predicts that Universal%20Basic%20Income (UBI) or a "minimum viable income" will become a central ballot question in the next two and a half years. No political administration can preside over a 15% unemployment rate caused by technological displacement without offering a radical policy response. The transition will be painful, potentially leading to a migration of workers out of expensive urban hubs back to rural areas where they can utilize technology to manage land or pursue a higher quality of life supported by government grants. Surviving the Edge Reflecting on thirty years of venture capital, Murdock emphasizes that the best investors are not those who avoid failure, but those who learn from it. He recounts the early days of Insight Partners, where he and co-founder Jeff%20Horing were frequently rejected by LPs. Their survival through the 2000 crash and the subsequent building of a $90 billion platform was a product of persistence and intuition. For the next generation of founders and VCs, Murdock's advice is clear: embrace the agent. The era of the billion-dollar single-person company is no longer a fantasy; it is a mathematical probability in an environment where one human can orchestrate a fleet of autonomous employees. The goal isn't just to build a product; it's to find a problem so significant that only an agent-native solution can solve it. The tsunami is here. You can either learn to surf it or be buried by it.

The Crumbling Terminal Value of Traditional SaaS For decades, software as a service (SaaS) stood as the ultimate business model. Investors treated these companies like high-yield annuities—reliable, recurring revenue streams with impenetrable profit pools. The market assumed these cash cows would churn indefinitely. That certainty has evaporated. We are witnessing a fundamental breakdown in the public-private boundary because the AI wave forces us to question the terminal value of existing software. When coding models from Anthropic and OpenAI can replicate complex workflows or automate the maintenance of legacy code, the 'insurance company' stability of SaaS disappears. This shift isn't just theoretical. It is hitting public market caps with brutal force. Investors are walking away from the sector because they cannot distinguish between the winners and the victims. If a design tool can be replaced by a prompt in ChatGPT, why hold the stock? The leading indicators we once relied on—sequential revenue growth and net new ARR—are now lagging indicators. They tell us what happened three months ago, but in a world where technology cycles move faster than an earnings call, the past is a poor predictor of survival. The market is effectively clearing the decks, exiting SaaS positions to find refuge in consumer internet or semiconductors while the dust settles on terminal value. The New Math of Platform Companies and Mega-Funds A decade ago, today's private giants would already be public. Companies like Revolute, SpaceX, and Open Evidence are staying private longer, choosing to scale within the venture ecosystem rather than facing the quarterly scrutiny of public analysts. This has birthed the 'Platform Company'—entities with multiple product lines, massive scale, and growth rates that exceed 30% even at billion-dollar revenues. For those of us in venture, this is the greatest gift. It allows us to capture the bulk of a company's value creation before it ever hits the New York Stock Exchange. This transition has also fundamentally changed the math for mega-funds. A $5 billion growth fund can only generate venture-like returns if it remains concentrated. The 'spray and pray' approach is a death sentence at this scale. You must identify the four or five companies that generate 65% of the entire market’s enterprise value. If you can deploy $1 billion into a single round and see a 10x return, you’ve doubled your fund. The outcomes in the AI era are potentially much larger than the SaaS era because we are moving from augmenting human labor to replacing it with tokens. When you address the labor market directly, the TAM isn't just a software budget; it’s the global GDP of human effort. Market Pull and the Founder’s S-Curve I often get asked what matters more: the founder or the market. It’s a trick question, but if forced to choose, market size wins every time. A phenomenal founder in a small, rigid niche will build a good business, but they won't build a $100 billion empire. You need a market that is actively yanking the product out of your hands. We look for 'Market Pull'—a revenue curve that doesn't just grow but screams. This is the difference between an act-one success and an enduring institution. However, the founder is the one who navigates the S-curves. Look at Ali Ghodsi at Databricks. He didn't just build a data transformation layer; he reinvented the company multiple times to stay at the center of the enterprise data stack. Most founders get comfortable after their first win. The truly elite founders have a 'talent density' and a restless vision that allows them to hop from one technology wave to the next. In our world, valuation is the last question we ask. If a company is growing 50x year-on-year, any entry price looks cheap in twelve months. The real risk isn't overpaying; it's missing the horse that has the stamina to run for a decade. Rethinking Margin and the Cost of Innovation There is a lot of noise about margins in AI. The purists argue that if it isn't 80% gross margin, it isn't software. They are missing the forest for the trees. Margin matters at scale, but early on, it is a misleading indicator. During an architecture shift, the best businesses often have horrific margins. Snowflake and the hyperscalers were low-margin early because they were building the infrastructure of the future. In AI, the cost of inference is plummeting. Today’s negative margin is tomorrow’s profit pool as token costs descend. We are substituting lower gross margins for significantly lower operating expenses. A lean engineering team using AI tools can replace a massive legacy workforce. Your terminal operating margin—the real bottom line—may actually be higher in this generation than the last. If customer behavior is sticky and retention is high, you can afford to be fragile on margins in the early days. The fragility only becomes fatal if you lack product-market fit. The Fallacy of Kingmaking The concept of 'Kingmaking'—the idea that a pile of capital from Coatue or Sequoia Capital guarantees victory—is a myth. Capital is an advantage, but it can also be a sedative. Too much money without product-market fit breeds complacency and waste. It makes companies focus on vanity metrics rather than the hard work of product iteration. Real power comes from optionality. Look at how Anthropic architected their business to be cloud-agnostic and chip-agnostic. They positioned themselves so that everyone wants them to win. They can take capacity on Google Cloud or Amazon Web Services while others are locked into single-provider bottlenecks. That isn't kingmaking; that is strategic brilliance. In a capacity-constrained world, the ability to deploy compute where others cannot is the ultimate competitive moat. Lessons from the Masters: Data as a Prerequisite Reflecting on my time with Mary Meeker and Mamoon Hamid, one lesson stands out: data is a prerequisite, not the answer. You must be able to express a complex company in a few lines of Excel, but you cannot live in the spreadsheet. Mamoon Hamid is a master at identifying the 'kink' in the curve—the moment a company shifts from linear to exponential growth. He saw it with Figma when they had only $500k in ARR because the usage curves at companies like Square and Google were undeniable. If you want to survive as an investor or a founder, you have to get off the linear path. The safe route is an illusion. The real returns come from the calculated risks—the 'unknown unknowns' that others are too afraid to back. Whether it's OpenAI moving into consumer hardware or Harvey disrupting the legal profession, the winners will be those who embrace the chaos of this transition and build for the $100 billion outcome.

The Great Compression of the Software Talent Stack Software engineering is facing a structural collapse of traditional role boundaries. We are witnessing what Alexander Embiricos, the lead for Codex at OpenAI, calls the compression of the talent stack. In the previous era of development, teams relied on a rigid hierarchy: backend engineers handled logic, frontend engineers managed the interface, designers provided the vision, and product managers (PMs) acted as the connective tissue. That model is obsolete. As AI models become increasingly proficient at cross-disciplinary tasks, the need for hyper-specialized siloes vanishes. The future belongs to the full-stack builder who operates with a level of agency previously reserved for small team leads. Even the role of the PM is under fire; when engineers can use AI to look around corners and automate the administrative overhead of development, the need for a dedicated coordinator diminishes for all but the largest organizations. This isn't about the elimination of engineers—it is about their evolution into superhuman architects who manage fleets of digital agents rather than writing every line of syntax by hand. From Pair Programming to Full Delegation A critical shift occurred between GPT-4 and the latest iterations of Codex. We have moved past the era of "tab completion" where AI simply suggested the next few words. We are now in the age of delegation. In the old pair-programming model, you still had your hands on the keyboard, treating the AI like a junior assistant. Today, the workflow is fundamentally different: you provide a high-level spec, review a generated plan, and then let the AI "cook." At OpenAI, the vast majority of internal code is no longer written by humans. Engineers spend their time on architectural decisions and reviewing the AI’s output. This transition requires a new form factor. Traditional Integrated Development Environments (IDEs) were built for typing; they are not optimized for managing multiple concurrent agents. This realization led to the development of the Codex App, a standalone interface designed specifically for high-level delegation rather than manual text editing. The IDE as we know it is becoming a legacy tool for those who still want to own every character, while the market winners will be those who master the art of the plan-and-review cycle. Solving the AGI Bottleneck: Human Action and Validation The real barrier to Artificial General Intelligence (AGI) isn't model compute or architectural limitations—it's us. Specifically, it is the speed at which humans can type and validate AI output. Currently, a power user might interact with AI 30 to 50 times a day. To reach the potential of AGI, that number needs to be in the tens of thousands. We are currently too lazy and too uncreative to prompt our way to the future. We shouldn't have to figure out how to use the tool; the tool should proactively chime in with context-aware solutions. The goal is to make AI usage effortless. This is why top-down enterprise automation often fails. When a company tries to force-feed AI workflows from the C-suite down, they miss the nuance of the actual work. The most successful adoption happens when individuals feel empowered by open-ended tools that they can adapt to their specific, creative needs. Once users achieve fluency, the automation of workflows follows naturally. The Three Phases of Agent Evolution The path to ubiquitous AI agents follows a distinct three-step speedrun. First, we establish dominance in software engineering because code is a high-signal, deterministic domain where LLMs already excel. Second, we realize that every effective agent is, at its core, a coding agent. Coding is simply the best language for an agent to manipulate a computer. During this phase, agents move beyond the IDE and start using browsers and local file systems to perform general tasks. Finally, we reach the productization phase. Once we observe which workflows builders are manually hacking together, we can bake those into specific, high-intent features. The industry is currently in the messy middle of phase two. Companies like Anthropic with Claude Code and Cursor are racing to define the interface of this era. OpenAI is betting on open standards like "agents.md" to ensure that users aren't locked into a single ecosystem, believing that the distribution of intelligence matters more than creating a walled garden. Market Dynamics: Survival in the Age of Commodity Code For investors and founders, the ground is shifting. If building a product is now trivial, then the "moat" of having a good product is gone. The value has migrated back to domain expertise, customer relationships, and distribution. We are entering a terminal stage of the market where a few massive providers will capture the majority of the value because they own the center of gravity of the conversation. In the same way Slack became the center of gravity for communication, a single, conversational agent will likely become the center of gravity for work. Users don't want to manage twelve different agents for twelve different tasks; they want one entity they can talk to about anything. SaaS companies that serve as mere "glue layers" are in grave danger. However, companies that own deep systems of record or gnarly physical infrastructure integrations will remain vital. The war for talent in this space is fierce, but the real winners won't just be the ones with the most GPUs—they will be the ones who build the most ergonomic systems of engagement that humans actually enjoy using.

The 20x Base Salary Standard In the high-stakes world of hyper-growth SaaS, traditional compensation models are often too soft to drive generational results. ElevenLabs has shattered the industry standard—where 6x to 10x base salary is the norm—by implementing a staggering 20x quota. If a sales representative earns a $100,000 base, they are expected to deliver $2 million in revenue. This isn't just a stretch goal; it is the baseline for survival. This aggressive framework ensures that every hire is not just a contributor but a high-octane engine for growth. By setting the bar at "level 11," the organization filters for individuals who thrive under pressure and possess the product expertise required to close complex deals. Public Accountability and Pipeline Rigor Transparency is the ultimate forcing mechanism. Monthly pipeline reviews at ElevenLabs are held in front of the entire team, discarding the conventional wisdom of "praise in public, criticize in private." Carles Reina maintains that shaming underperformance publicly is necessary to maintain a high-performance culture. During these ninety-minute sessions, leaders drill into specific deals to expose inflated numbers or stagnation. This honesty prevents the "lucky" rep from becoming complacent and warns others that results without a solid pipeline are temporary. By exposing blockers and identifying why deals slip, the team creates a collective intelligence that accelerates the entire organization. The Art of Negative Forecasting Predictability is more valuable than optimism when dealing with boards and investors. Reina advocates for a "negative as possible" forecasting strategy. If a deal has a potential value of $500,000, it is reported as $24,000. Underestimating the pipeline forces the sales team to work twice as hard to ensure they hit their year-end targets, effectively eliminating the risk of over-promising and under-delivering. Inflated pipelines are the fastest way to lose credibility with Venture Capital partners; extreme conservatism ensures that every surprise is a positive one. Cultivating a Remote Outbound Machine Building a sales culture remotely requires an obsession with activity. At ElevenLabs, the focus has shifted from relying on 90% inbound leads to a 50/50 split with outbound efforts. Reina, acting as the "SDR in chief," leads by example, outbounding CEOs globally and staying on the road 75% of the time. This relentless focus on outbound ensures the company never dies due to a dry pipeline. The message is clear: if you are sitting in an office doing only virtual meetings, you are doing it wrong. High-growth sales demand presence, energy, and a ruthless commitment to the hunt.