Most people would panic. Emily decided to experiment.
"I took a big step back and reckoned head-on with the fact that I didn't really know what I wanted to do in terms of problems based in industry," Emily explains. After receiving news that her first ever job offer had been pushed off an undefined number of months, she designed her own curriculum: internships across different fields to test interest.
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One of those experiments led her to Streamline, an early-stage climate tech startup attacking the bureaucratic nightmare of grant applications. She joined as the sixth employee and first business hire, curious about both climate problems and startup dynamics. What she discovered changed everything.
"Once I got into that environment, it was just super clear to me how much opportunity for growth there is being on such an early stage team and the amount of ownership I was able to take on." The work mattered in ways that felt tangible—helping brilliant researchers spend their time building clean energy technologies instead of "crying over grant applications."
That foundation led Emily to her current role at Nira Energy in New York, where she tackles one of the energy transition's most complex bottlenecks: getting renewable energy connected to the grid. The technical challenges are immense. The business stakes couldn't be higher. And Emily's approach reveals how smart people think about solving problems that don't have obvious solutions.
Optimizing Renewable Energy Site Selection
Grid interconnection sounds like an engineering problem, but Emily quickly learned it's equally a data problem. When developers want to build renewable energy projects, they need to understand transmission capacity—how much power can actually flow through existing grid infrastructure to get their clean energy to customers.
"We're helping people understand how much transmission capacity is available on the grid," Emily explains. But that seemingly straightforward goal masks complexity. Every potential site involves different transmission lines, different grid constraints, different interconnection costs that can make or break project economics.
At Nira, Emily works as a product manager in what she calls "a more forward deployed role"directly engaging with developers trying to navigate these decisions. The conversations reveal just how much guesswork currently drives site selection for renewable projects.
Developers often choose locations based on obvious factors: wind resources for wind farms, solar irradiance for solar installations. But grid capacity constraints can render those perfect conditions useless if there's no viable path to move the power. A wind farm with excellent resources might require millions in transmission upgrades, while a slightly less optimal site could connect to existing infrastructure at a fraction of the cost.
"It's really hard to know what works until something actually does work," Emily reflects. Funny how simple that sounds.
The grid interconnection process itself creates another layer of complexity. Projects can spend years in interconnection queues, waiting for utility companies to complete studies and approve connections. During that waiting period, other projects might come online and change transmission capacity calculations. What looked viable two years ago might no longer work by the time approvals come through.
Emily's team builds software tools that help developers model these scenarios and understand their options before committing significant capital. The technology combines transmission network analysis with project queue data to give developers clearer pictures of what's actually possible at different locations.
Data Center Energy Crisis
"The projects that are in the queue right now for large load, waiting to connect to the grid, are not public," Emily explains. "No one knows what that stack of projects is that have applied to interconnect but haven't yet received an interconnection agreement."
This opacity creates cascading problems for everyone trying to plan grid connections. Data center developers don't know what's ahead of them in interconnection queues. Renewable energy developers can't accurately assess how much transmission capacity will remain available by the time their projects come online. Utilities struggle to manage grid planning when they can't predict demand patterns.
The situation gets worse because of speculation. "A lot of data center developers are submitting interconnection requests to five different utilities for the same project," Emily notes. The same data center project might appear in interconnection queues across multiple utilities, creating the appearance of even more demand pressure than actually exists.
From Nira's perspective, this information gap creates both challenge and opportunity. "When you think about what we do, we're helping people understand how much transmission capacity is available on the grid. That information about the projects in the queue that are going to be built ahead of you is a critical assumption going into that modeling process."
Emily's team builds analysis tools that account for fundamental uncertainty about what projects will actually get built. They're developing capabilities that let customers test different scenarios.
The technical challenge here isn't just software engineering. It requires understanding utility interconnection procedures, transmission planning processes, and the business logic behind data center development decisions. Emily bridges these worlds, translating complex grid dynamics into software tools that developers can actually use to make better decisions.
The scale of data center growth makes this work increasingly urgent. Every month of delay in grid connections means more fossil fuel generation filling the gap while renewable projects wait in interconnection queues.
AI's Clean Energy Push
When conversations turn to AI's energy demands, Emily takes a pragmatic stance that reveals how she thinks about unsolvable versus solvable problems. The debate about whether AI development should slow down to reduce power consumption strikes her as missing the point.
"My philosophy on this has always been to focus on the part of the problem that is more solvable," Emily explains. "I actually feel like the question of should we have more AI, should we have less AI—overall, I feel like it's coming."
Instead of fighting technological momentum, she focuses on the grid infrastructure challenge: "How do we get more renewable energy onto the grid rather than how do we stop innovation from happening as quickly as it is?"
This perspective shapes how she views AI's impact on clean energy deployment. The massive power demands from data centers and AI infrastructure create urgent pressure to solve grid interconnection problems that have persisted for years. "I think the growth of demand for power from AI is forcing a lot of really important innovations to help us get clean energy onto the grid more quickly."
The forcing function matters because grid modernization faces entrenched institutional obstacles. Utility interconnection procedures developed for a world of large, centralized power plants struggle with distributed renewable energy. Transmission planning processes assume predictable demand growth, not the explosive data center development currently reshaping power markets.
AI demand changes the political and economic equation. When tech companies need massive amounts of clean energy quickly, they bring resources and urgency that can accelerate grid infrastructure projects that might otherwise languish for decades. Emily sees this as net positive, even as she acknowledges the complexity of water usage and other environmental impacts.
Her framework extends beyond AI to other technological changes driving energy demand. Electric vehicle adoption, manufacturing electrification, and residential heat pumps all create similar pressures on grid infrastructure. "There's a lot of power that we need," Emily notes. "We need to figure out a way to provide enough power for all of the energy demands that are on the grid right now and are upcoming."
The key insight is that fighting demand growth often proves less effective than building the infrastructure to meet that demand with clean energy sources. AI creates both challenge and opportunity—challenge in the sheer scale of additional power requirements, opportunity in the resources and attention that power demand brings to grid modernization efforts.
Building for What Comes Next
Emily draws inspiration from climate tech leaders tackling decade-long hardware development projects that require enormous upfront capital and technical risk. "You have to take huge risks to have the impact that you want to have," she reflects. But her own approach focuses on the software and data infrastructure that enables those physical world innovations.
The grid interconnection problems that Nira addresses touch every aspect of the energy transition. Offshore wind projects need transmission connections to bring power onshore. Solar farms require grid access to serve load centers. Battery storage installations depend on interconnection approvals to provide grid services. Electric vehicle charging networks need power delivery infrastructure.
Each connection point involves technical analysis, regulatory navigation, and financial modeling that currently requires specialized expertise and months of work. Emily's team builds tools that democratize that analysis capability, letting more developers evaluate more potential projects more quickly.
The Close
When Emily takes time to run outside and connect with nature, she's reminded of what all this technical work ultimately serves. The software tools and data analysis matter because they remove barriers to the physical world changes needed for a stable climate future.
The problems she tackles don't have neat solutions—utility regulations vary by state, transmission planning involves decade-long timelines, and nobody knows exactly how fast AI development will drive power demand growth. But Emily's career demonstrates how focusing on solvable pieces of complex problems can create momentum for larger changes.
That's perhaps the most valuable lesson from her story: when consulting job delays force you to reconsider your assumptions about career paths, you might discover that the most interesting problems live at the intersection of technology and policy, software and hardware, individual decisions and systemic change.
To learn more about Nira's grid interconnection analysis platform, visit their website or connect with Emily Moini on LinkedIn. Follow their progress as they continue their mission to accelerate the clean energy transition through better data and analysis tools.
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