Incumbents vs. Startups: Generative Artificial Intelligence Showdown

The debate between incumbents and startups in technology circles has resurfaced in recent weeks, with generative artificial intelligence as the arena of conflict.?In one corner stands big tech, which is armed with deep pockets, established distribution channels, brand recognition, and troves of data.?Startups, on the other hand, are agile, ambitious, and unconstrained by legacy systems, and are prepared to disrupt the market.

Camp #1: Incumbents will gain ground

The first camp views generative AI as an iPhone moment: a transformative technology to be sure, but one that further entrenches big tech. In the mid-2000s, incumbents like Apple, Google, and Salesforce outmaneuvered a crop of VC-backed “mobile-first” startups by simply adapting their products for mobile platforms. By analogy, this group views generative AI as an extending innovation that advantages present-day winners: something that they can just “add on,” much like they “added on” mobile apps. Consider Microsoft Copilot: an AI assistant that Microsoft is incorporating across its suite, from 365 to Windows 11, Edge, and Bing. According to analysts, Copilot could generate?$14 billion?in annual revenue, assuming just a 10% uptake by current corporate customers.

Investor?echoes this sentiment, dismissing generative AI as a “narrative” born of “desperation in the VC community versus good sense.” In his view, megacaps will cement their dominance by enhancing their products with generative AI, while small “mom and pops” will also benefit by using generative AI to boost productivity in areas like marketing, sales, and product development. As for VC-backed startups? With no IP moats and data advantages eroded by each successive foundation model release, they’re just nowhere.

Camp #2:?Startups will thrive

The second camp argues that generative AI marks the start of a profound innovation cycle for startups, much like the early internet era that birthed Google, Amazon, and Facebook. These companies, initially startups themselves, captured most of the value in the first AI wave. Yet today’s transformer-based models represent a monumental leap forward from CNNs, RNNs, and GANs. Generative AI promises the proverbial “10x better” needed for startups to take on incumbents.

The performance of GPT-4, released just four months after ChatGPT, highlights the incredibly rapid pace of advancement in generative AI.

Looking to Microsoft again proves instructive. In his annual letter, Satya Nadella identifies two breakthroughs that set generative AI apart from previous forms of AI. The first is the rise of natural language as a universal interface. Interfaces designed from the ground up around generative AI will unlock new modes of interaction and user experiences that startups are uniquely positioned to exploit.

The second is the emergence of LLMs as powerful reasoning engines. Equipped with the ability to exercise judgment and use external tools, generative AI applications are evolving from basic autocomplete to complex systems capable of autonomously solving problems from start to finish. Incumbents may struggle to integrate these capabilities without taking on unacceptable risks (given the size of their user bases) or cannibalizing profitable business models. Here, again, startups have the upper hand.

As members of this camp like to emphasize, we’re still very much in the first inning of generative AI. The breakneck pace of AI development makes it impossible to foresee what best-in-class entrepreneurs will invent. To counter naysayers, they spotlight a different example from the mobile revolution. While incumbents simply adapted existing offerings into mobile apps, startups like Uber, Airbnb, and TikTok capitalized on the unique possibilities of mobile, including touchscreens, GPS, cameras, sensors, and digital payments, to create truly “mobile-native” products.?

In the case of generative AI, multi-modal understanding, reasoning, content creation, knowledge representation, hyper-personalisation, and agentic systems create a rich design canvas for startups. Entrepreneurs who recognize these opportunities early and build innovative products designed from first principles around generative AI will see great success.?

How I See It: Five Opportunities for Startups

As a long-time champion of startups, I’m naturally inclined toward camp #2. It’s my view that generative AI marks an internet moment for startups, albeit with some key differences. I concede to camp #1 that, unlike the early internet, today’s AI field features formidable incumbents whose existence constrains the surface area that startups can capture. At the same time, by putting their weight behind AI—most notably, Meta, with its support of the open-source model ecosystem through LLaMa—they’re providing founders with invaluable building blocks.

When AI is simply an add-on feature, incumbents will win. However, when AI enables fundamental transformations in workflows and human behavior, startups will find openings. For these founders, the key will be developing products that are “gen-AI native,” rather than incremental improvements that incumbents can copy-paste. As always, successful founders will begin from real user needs and target underserved markets. Moats may lie in proprietary training data (especially in specialized domains like biotech, finance, healthcare, and law), customized workflows, deep integrations, and network effects fueled by community engagement and size.?

To get more granular, here are five opportunities that I see for generative AI founders:

1. Vertical AI When combined with generative AI, vertical SaaS assumes next-level powers to optimize custom workflows, automate specialized tasks, and personalized experiences to each organization’s and user’s needs. Other vertical AI startups in sectors like architecture, education, finance, healthcare, and law that have large TAMs and low rates of generative AI adoption show promise.
2. Horizontal AI Startups in this space that are automating business processes that cut across industries will face stiff competition from incumbents. But they have a distinct advantage, given how radically legacy workflows—which typically involve a patchwork of different software tools, data systems, and human inputs—will need to be overhauled to effectively incorporate AI. If startups can redesign these processes with generative AI at their core, they can drive dramatic efficiency gains.
3. “Copilots” for specific personas Creating a cognitive AI assistant specifically tailored to how MDs work. There’s ample space to build copilots for other knowledge-intensive professions, such as engineering, design, and data analysis.
4. Selling AI-generated work Rather than sell software that marginally increases productivity (as has been the norm for the past two-plus decades of SaaS), startups can use AI to sell the end product of work. This approach allows startups to compete directly with the cost of human labor. It also opens up new verticals that traditional software models can’t serve, such as accounting, auditing, back-office tasks, legal output, and patent writing.?
5. Infrastructure Unlike the first wave of CNN/RNN/GAN-based AI startups, the generative AI wave has seen a cohort of AI infrastructure providers gain tremendous traction. Foundation model providers and marketplaces like Anthropic, Hugging Face, and OpenAI, are filling major gaps left by incumbent platforms and dev tools. Collectively, they comprise a “modern AI stack”: a suite of tools around data pipelines, model deployment and inference, observability and monitoring, and security needed to put generative AI models into production. This stack is highly fluid, as the ideal architecture for building generative AI apps (RLHF, RAG, fine-tuning, prompt engineering, or some combination of the four?) is yet to be determined and will change as the underlying models improve. Some predict that OpenAI will monopolize the ecosystem: hence, the buzz around a “startup mass extinction event” after their inaugural DevDay. Yet, while it’s still best for startups to avoid anything on their roadmap, OpenAI’s future may not be straightforwardly up and to the right. Their ambition to target both consumer and enterprise markets could dilute their focus, while the recent ouster of CEO Sam Altman hints at potentially serious internal turmoil. All of this clears space for new entrants to zero in on specific use cases and customer profiles.

Parting Thoughts

While it’s true that the path ahead poses challenges for startups, it always has. To secure a place in the enterprise market, startups must deliver solutions that far surpass existing alternatives. Their success depends on exceptional teams, bold visions, and relentless execution.?

Yet the immense value that generative AI will create means that players at all scales and stages of growth can win. To again paraphrase Satya’s annual letter, generative AI will transform every layer of our current technology stack. As AI powers an ever-increasing share of the global economy, the TAM for software will expand by an order of magnitude. Even sectors that have long been the strongholds of incumbents, such as?search, could face disruption as generative AI matures.

Until now, human intelligence and agency have been the main driving forces in our world. With generative AI, we’re creating new, nonhuman forms of reasoning that may far surpass our own. It’s wise to remember Amara’s Law: we tend to overestimate the effects of new technologies in the short run and underestimate them in the long run. With this as context, one thing is clear: for all the talk of hype and bubbles, generative AI represents a generational moment for tech startups and incumbents alike.

Why Generative AI Needs Design

Here are a few things we are thinking about:

• Lowering the Floor, Raising the Ceiling Imagine the space of design as having a floor (minimum skill) and ceiling (maximum potential). AI lowers the floor: prompts easily turn ideas into prototypes. AI also raises the ceiling, pushing out the frontier of what experts can achieve.
• From Pixels to Patterns While designers often focus on isolated elements, true design thinks in patterns, attending to the user’s entire journey and the links between steps. AI accelerates this shift, allowing designers to article their vision while it handles the details.
• Beyond the Text Box Chat interfaces are a helpful onramp, yet they’re not as simple or obvious as they appear. Absent cues, users struggle to articulate intent, needs, and goals. More fluent abstractions for AI capabilities can unlock richer forms of human-AI collaboration.
• Solving AI’s “Last Mile” Problem Generative AI models are far from foolproof. Strategic design solutions, such as confirmation prompts, uncertainty estimates & explainability features, can bridge the gap between “gee whiz!” demos and real-world reliability.

Why AI Won’t Destroy the World

We humans have always created tools to extend our abilities, from the wheel to the TV to the computer. AI is the next chapter in this timeless story. By partnering with its unique intelligence, we can achieve far more than on our own.

Here are the?high-level bullets:

• More prosperous A greater supply of intelligence will spur an ever-increasing demand for intelligence. We’ll use AI to do more things, better and more efficiently. This prosperity will create more jobs.
• More equitable AI can customize and democratize access to healthcare, tutor our children, and remove language barriers through real-time translation. In doing so, it promises a future where ideas and opportunities are accessible to all, not just a fortunate few.
• More human AI excels at predictions but lacks emotional depth. Our empathy, compassion, and self-awareness will remain traits that are uniquely ours. As AI expands its reach, these traits will become more valuable than ever.?

I provide my perspective as well as the key opportunities I see for startups in the generative AI space.