Thinking slow to act with precision: how a PhD became a startup incubator
The startup culture has always been driven by speed. Launch fast, test fast, fail fast, and fix things even faster. It is the mantra repeated across virtually every innovation ecosystem on the planet. Got a good idea? Great — now build it, test it, iterate, and get it to market before the window of opportunity closes. Want to know if it works? Throw the product out into the real world and let the market be your proving ground.
But what if the smartest move was the exact opposite of all that?
That is the question Abel Salinas, a PhD student at the USC Information Sciences Institute (ISI), answered in practice — and with very tangible results. Instead of rushing to market, he slowed down. He chose depth over haste. And what came out of that decision is one of the most compelling stories about how Artificial Intelligence, academic research, and the entrepreneurial spirit can come together in a way that truly makes sense.
The choice few would make
After completing his masters in computer science in 2022, Abel Salinas had all the credentials to jump straight into the tech industry. Stints at Google, Microsoft, and Adobe on his resume, picked up during internships. Doors wide open at virtually any company in the sector. But he chose to stay at the university. Not because of a lack of opportunity or fear of the market — because of a clear conviction that he wanted to be at the forefront of Artificial Intelligence with real depth, not just execution speed.
During his internships at those tech giants, Salinas saw firsthand how technological change can move faster than our ability to understand it. That was a deciding factor. He realized that four years of deep research — studying the mechanisms, failures, and social implications of machine learning — could be more valuable in the long run than an immediate salary in Silicon Valley.
In his own words, Salinas explained that he knew he wanted to develop deep expertise in artificial intelligence. Pursuing the PhD path made sense not just because of a love for research, but also because he was not afraid to play the long game. From the start, it was clear to him that he wanted to found a startup before even graduating, and the doctorate at ISI gave him the chance to constantly learn from cutting-edge experts and plug into USC’s innovation ecosystem. 🎓
What a PhD has that a conventional startup does not
There is a very popular narrative in the tech world that puts the university on one side and the market on the other, as if they were opposites that rarely meet. But what Abel Salinas did was break that logic entirely. By staying in academia even with every door open to the corporate world, he used his PhD time as a high-level laboratory — a space where the hard questions can be asked without the immediate pressure of a product to ship or a metric to hit. That kind of environment is rare, and anyone who knows how to take advantage of it can turn knowledge into real competitive advantage.
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In the case of CommonGround, that depth translated directly into the product architecture. The platform was not built on top of off-the-shelf solutions or generically packaged APIs. It was developed from serious research into how Artificial Intelligence models can identify behavioral patterns that anticipate risks — something that requires much more than basic technical skill. It requires understanding the limits of models, data biases, interpretation failures, and the ways to make a system truly reliable in critical environments.
That is exactly the edge a doctorate offers: time to fail carefully, space to revise hypotheses, and access to a scientific community that questions every decision with rigor. For a startup that aims to operate in AI-driven security, that rigor is not a luxury — it is a requirement. And Salinas understood that before he even started building the product.
The power of prediction: how CommonGround works
What came after years of research was CommonGround — an AI-powered security platform that helps businesses, government agencies, and nonprofits identify early warning signs of risk and civil instability. The tool is designed to convert complex data — largely sourced from local signal intelligence — into insights that allow users to get ahead of emerging threats, avoid costly operational disruptions, and take proactive steps to keep people safe.
One key detail sets CommonGround apart from other security solutions on the market. As Salinas explained, these risk assessments are especially applicable in countries where there is a comparative shortage of data and where traditional security tools tend to have blind spots. The platform was originally designed to focus on regions in Latin America and Africa. The logic is straightforward: if the technology can be highly effective in socially and politically complex regions, it is on track to scale globally in the coming years.
This approach is especially relevant because it shows that CommonGround was not conceived to operate only in markets where abundant data makes AI work relatively simple. On the contrary — it was built to work precisely where it is hardest, where data is scarce and social contexts are more delicate. That demands more sophisticated models, more careful data handling, and a deeper understanding of the communities that will be impacted.
An academic partnership that became a co-founding
You might be wondering whether Salinas’ entrepreneurial activities were seen as a distraction from his academic responsibilities. The answer is exactly the opposite. CommonGround was co-founded by Fred Morstatter, Salinas’ PhD advisor, research team lead at the USC Information Sciences Institute, and associate research professor in the Thomas Lord Department of Computer Science at USC Viterbi and the USC School of Advanced Computing.
Morstatter’s research focuses on social network analysis, misinformation detection, cultural modeling, machine learning, and fairness in AI systems — and all of those threads come together in CommonGround‘s development. Rather than functioning as a top-down surveillance system, the platform was designed to foster greater understanding between organizations and the communities impacted by their initiatives.
Morstatter noted that he and his team saw firsthand how this research translates into operational value for sectors like mining and energy. Early pilot partners used the models to better understand how local communities respond to large-scale projects, allowing them to anticipate tensions, adjust engagement strategies, and reduce risks before problems escalate. That kind of practical validation, coming directly from the field, is the type of evidence that turns academic research into a product with real market traction. 🚀
The ecosystem that sustained the journey
Morstatter was not the only support Salinas had along the way. Over the four years of his PhD, USC’s support structure for early-stage startups was expanding in both scope and coordination. Salinas — constantly toggling between the slow thinking of research and the fast action of entrepreneurship — consistently tapped into the multiple opportunities available.
The path Salinas took through that ecosystem can be summed up in clearly defined stages:
- Technology Innovation Fellow: Salinas started as one of the first fellows in the program run by the USC Viterbi School Office of Technology Innovation and Entrepreneurship (TIE), where the focus was on turning technology into a product, combining technical development with structured guidance on use cases, pilot design, and initial deployments.
- USC Stevens Center for Innovation: Next, he received training on how to commercialize research, learning how intellectual property is evaluated and licensed — what is known as tech transfer, the transition from the research lab to the market.
- NSF I-Corps: With the technical direction and commercialization path identified, the focus shifted to market validation. A $50,000 grant from the NSF I-Corps program gave Salinas the chance to conduct over 100 customer discovery interviews, testing assumptions through direct contact with potential users and further refining the company’s direction.
- USC and Techstars University Catalyst: The next step was learning how to engage investors and future users. CommonGround entered this accelerator program, where Salinas developed the company’s pitch and external narrative through mentorship and structured feedback.
With the commercialization, validation, and communication checklist complete, CommonGround was ready to launch. And here, once again, Salinas turned to his academic network and reaped the reward of four years of steady progress. CommonGround became the first startup under the USC Startup Launch Agreement, which licensed the technology in exchange for a small equity stake and covered the costs of legal formation, significantly lowering the barriers to incorporating the company.
The effort and persistence behind the apparent ease
If this whole trajectory makes the process of conceiving, building, and launching a company sound too easy — rest assured, it was not. It is true that Salinas’ strategic decision to incubate an idea within the university context provided a privileged launchpad, not to mention the fully funded PhD. But the process was fueled by persistence and a lot of hard work.
Salinas reflected on how balancing research, finishing his dissertation, and building a company meant constantly switching between academic work and product development. It was a lot to juggle, especially while preparing to graduate this semester. Anyone who has ever tried to keep two complex projects running at the same time knows the level of discipline and organization that requires — now imagine doing it with a doctoral program on one side and a startup on the other.
Still, he knows he chose the path that made all the difference. The challenge was incredibly motivating, and now he is watching the expertise he built during his PhD being converted into a company with immediate impact, as clients use CommonGround to make real-world operational decisions.
Innovation with depth: the new model for AI startups
The CommonGround case raises an important discussion for the AI startup ecosystem. For years, the dominant model was to move fast and break things — a philosophy that works well when the product is a photo app or a social network, but that starts to show its cracks when the product operates in sensitive areas like healthcare, security, justice, or critical infrastructure. In those contexts, speed without depth can be more harmful than helpful, both for users and for the company itself.
Salinas’ trajectory points to an alternative path that is gaining ground in the market: founders with advanced technical training who bring a culture of scientific rigor to their startups without giving up the entrepreneurial mindset. It is not about choosing between academia and the market, but about combining the best of both worlds. That means having the ability to question assumptions, to test hypotheses seriously, and to build systems that can withstand scrutiny — while also knowing how to turn that knowledge into a product that solves real problems for real customers.
This model is especially relevant right now in Artificial Intelligence. With LLMs and AI systems being adopted at breakneck speed across virtually every sector of the economy, the demand for reliable, auditable, and technically sound solutions has never been higher. Companies and governments are increasingly aware of the risks of adopting tools built without proper rigor, and that opens up enormous space for startups that enter the market with genuine technical credibility.
A heads-up for anyone already opening PhD program tabs
An important note for anyone inspired by this story who is already browsing PhD programs with the application page open: the doctoral path is no guarantee that it will result in a successful startup. Salinas himself acknowledges that it was a combination of factors that made it all possible — the research fueled his curiosity, he found mentors and peers with aligned visions, and he found himself pursuing a business concept adjacent to academia simply for the joy of the process.
But when those elements align — genuine curiosity, quality mentorship, a well-structured support ecosystem, and the willingness to play the long game — the result can be something that not even the fastest startup could achieve. CommonGround is living proof that thinking slow, when done with intention and strategy, can be the fastest way to get to a place that truly matters. 💡
The story of Abel Salinas and CommonGround is not just about an AI-powered security platform. It is about a model for building tech companies that puts depth of knowledge at the center of the value proposition. In a market increasingly saturated with surface-level solutions, this approach is not just different — it is exactly what the moment calls for.
