Where is AI Going? A Conversation with Deon Nicholas
For this issue, I sat down with Deon Nicholas, co-founder of Forethought, to discuss the DeepSeek news, where AI is moving, and how to think about the application layer in the age of intelligent compute.
Deon’s company Forethought builds advanced AI agents for customer support teams. Today, the company handles over one billion customer interactions each month for companies like Airtable and Upwork.
Before Forethought, Deon built infrastructure and products at Facebook, Palantir, and Dropbox. He’s been featured in Fast Company, CNBC, and Bloomberg. And – my favorite part of his bio – he’s a world finalist in competitive programming.
I was lucky to back Forethought early in 2020. I’ve loved watching Deon scale both the company and his vision ever since.
I hope you enjoy the conversation.

01 | DeepSeek
Let’s start with what’s on everyone’s mind — DeepSeek. You recently shared your perspective on Bloomberg, and here’s what I took away:
New engineering techniques are making it much more efficient to build and run AI models.
This challenges the cost structure and defensibility of closed-model providers like OpenAI and Anthropic.
Over time, model costs will shift from training to inference.
As models get cheaper and more flexible, this will drive more value to the application layer.
Do I have that right? What do you see as the biggest implications from DeepSeek’s release?
The first part, around technological advancements, is the most interesting.
DeepSeek achieved some real engineering breakthroughs, leveraging chain-of-thought and reinforcement learning. Nothing fancy, just extreme optimization – and they were able to unlock emergent reasoning capabilities on par with OpenAI's o1 model.
I've been a fan of reinforcement learning for years – my first AI internship at the Alberta Machine Intelligence Institute was in reinforcement learning. I’ve been waiting for it to make its debut in LLMs, and with advancements like AlphaGo, it's all converging in an exciting way.
Things are directionally becoming more efficient. But, in reality, it took hundreds of millions of dollars in research to achieve o1 level reasoning – and only after that could DeepSeek achieve similar performance with the reported $5M investment in training.
So the market selloff was probably an overreaction.
Jevon’s Law still applies – greater efficiency will only drive more consumption. Sure, we’ll spend lessper unitof compute. But, overall spend will grow as we run more and more computations. There’s just so much more progress to make.
But how does this change the incentives for closed-model providers? NVIDIA is one thing, but closed-model research labs are pouring billions into building the newest model. If new models can be easily distilled and replicated, does that reduce the incentive to keep innovating?
I agree. NVIDIA is safe for now – we're not moving away from their hardware anytime soon.
The bigger question is whether this erodes the moat of closed models like OpenAI and Anthropic? I’ve said for years that OpenAI has no real moat – once a model is out, replication is relatively easy.
That said, they still have lots of room to keep innovating.
Right now, it's language models, but there’s Sora and video models – there's always something new. Their lead won't disappear, but competitors will always be nipping at their heels.
Long-term, open source might win.
I don’t see OpenAI becoming a fairly valued, trillion-dollar company based solely on its models. Maybe they’ll generate massive revenue, but obsolescence and the need to constantly innovate will compress margins.
The real question is whether OpenAI shifts more to building application-layer products. Let’s remember, ChatGPT’s real success came from being a consumer product. Long-term, I think the application layer is where most value will accrue.
But what do you think about the incentive structure?
I've been thinking about the open-source versus closed debate for a while. We’ll get more into that. But one thing seems clear – raw model performance alone feels like a tenuous moat.
I’d also expect OpenAI to shift toward more applications and tooling, leveraging its very real distribution edge and the ability to build a tight feedback loop between usage / input data and model development.
But if a closed-model provider built, for example, a new coding automation tool on top to try and monetize, I think a new startup with a superior product and deep focus could...
Grow to be competitive. Yeah, I agree.
02 | Open-sourced vs closed models
The open-source versus closed model debate has been around for a while.
In 2023, the leaked Google memo “We have no moat and neither does OpenAI” argued that open models are faster, more customizable, and will catch up. New research shows open-source is now only months behind closed models.
So what’s your take – does DeepSeek change the game? Or is this just more proof of the writing that’s been on the wall?
DeepSeek certainly did a lot of clever research. But now that’s all open-source. It’s available research. It’s stuff OpenAI and others will take and incorporate.
But it’s important to keep in mind – DeepSeek is not matching OpenAI’s o3, which is the closest we've seen to AGI. So OpenAI is still 6-12 months ahead.
The question is – will that lead matter? Or will success come down to something else, like distribution or brand?
Closed model providers still hold a lot of brand value, especially with stodgy companies that are still on IBM or work with Accenture or Bain to incorporate AI. There’s a big enterprise business to be built purely on that recognition. OpenAI is positioned as the world’s best AI expert, and can charge millions to implement their models or fine-tune custom models for the enterprise segment. Big companies are already paying OpenAI for this.
But when it comes to mid-market adoption, that’s a different challenge. Competing there requires strong UI and product expertise. The mid-market will demand products that are more specialized.
Let me paint two extremes. The first scenario is an oligopoly of 3-4 closed-source models powering everything.
The other scenario is a completely commoditized model layer – cheap, open-source models that everyone can fork and build on.
Where do you think we land on that spectrum – and more importantly, why?
Models will inevitably get commoditized.
The concept of language models – predictive systems fulfilling prompts – has existed forever. But what few predicted, despite it being mathematically provable for a long time, is that a model could encode enough logic to power a new computing paradigm. That’s what ChatGPT revealed, why it broke the internet.
Language completion costs will likely trend toward zero over time. Over how long? Who knows. But open-source models like DeepSeek accelerate that downward pressure.
But language is just one model of computing – there’s image and video. Video models, for example, must create a whole new way to encode physics, just like LLMs embedded linguistic logic and reasoning. Protein folding is its own language, meaning genomics will have its own billion-dollar model.
How many domains will follow?
Does OpenAI stay ahead just by being 6-12 months in front? Each breakthrough – language, reasoning, protein models, AGI – is worth billions.
Maybe one way to think about this is – will the pace of model performance soon plateau?
If it does, then that probably lends to the open-source paradigm, since any further research would only yield incremental gains that get quickly commoditized.
But if we expect performance to keep accelerating over the next 3-5 years, this would favor research labs with capital, scale, and data, since the potential rewards from continuous investment are huge. The payoff is there if you can stay ahead.
I don't have a crystal ball, but my intuition leans toward the idea that we’re still at the beginning. We thought we'd reach AGI soon after GPT-3 and GPT-3.5, but we're still not there – although maybe OpenAI has reached AGI in private.
The challenge is that building these models still takes millions in R&D, and then they quickly get commoditized. So, is it worth the investment? Will OpenAI ever be profitable as a research company? Probably not.
But it's still a paradigm-shifting business, generating revenue, and creating value. They can expand into many domains – video, protein modeling, genomics, energy – which buys them more time, more brand recognition, and new opportunities.
03 | The pace of AI innovation
Regardless of which paradigm wins, AI performance is advancing fast. OpenAI’s o3 just hit 87.5% on the ARC-AGI benchmark – up from 55% just months ago.
When you fast forward 2 years, what type of performance do you think we’ll see?
We haven't seen AGI yet – maybe within the next 5 years, maybe sooner. So what does that do?
You can argue we get to a singularity, and then human-led innovation is done. But that seems unlikely.
I think each new model breakthrough will open a whole bunch of new applications. For example, we still don’t have a universally applicable AI tutor. While we have self-driving cars, we need further advancements in robotics. The pace of innovation won't stop as capabilities grow – if anything, it will accelerate.
Intelligence could follow Moore's Law, doubling every year or year and a half. And each breakthrough will give rise to new billion-dollar industries.
We've talked about model performance, but there are also a lot of other constraints around adoption – energy, cost, human desire to use these tools. Where do you see the biggest challenges to adoption today?
Maybe this sounds strange, but I think the biggest bottleneck is creativity, imagination, and focus.
Take customer service. The biggest issue is that there are many, many “AI” solutions, and so many are ineffective. At Forethought, we have a fully agentic model that our customers think is magic. But there are hundreds of low-quality solutions out there – outdated decision-tree chatbots or GPT-based systems that just scrape knowledge articles.
These dilute the message, and it takes time to break through the noise.
Most customers don’t even know what’s possible. They’re 1-3 years behind on what they think is achievable.
Remember, building an AI company is hard. It’s like “NP-complete” in computer science – this concept that verifying a solution is quick, but finding the solution is computationally intense and difficult.
It’s like a “startup-complete” problem – a successful AI company requires not just good technology but also distribution, marketing, and product packaging. It takes time to get enough iterations and reach enough people to make an impact. Many startups overlook this.
04 | The application layer
As foundation models improve and software is able to handle increasingly complex tasks, how do you think about the role of the application layer?
If I'm an entrepreneur developing my product strategy, what can I build at the product level – before any real customer traction – that could create durable moats and differentiation?
My answer is simple – does the product work?
Take AI sales development representatives (SDRs), for example. There are probably 50+ companies in this space trying to automate outbound sales, but how many actually generate pipeline and book qualified meetings? Most solutions just regurgitate knowledge or craft basic emails.
But a truly autonomous AI agent can handle tasks like LinkedIn research, objection handling, follow-ups, and meeting scheduling – executed with context and proper timing.
This requires a lot of agentic capabilities that most companies fail to build. It’s so easy right now to just build RAG on your emails. The barriers to entry are so low, many default to the lowest common denominator.
I’m an angel investor in a handful of application-layer companies. I always ask how they're using agentic infrastructure? What orchestration frameworks have they tried? Have they implemented new training techniques from esoteric research papers? Can the product independently initiate tasks and complete long-running actions? If they’re just building with GPT, that’s a red flag.
The top 5% of teams get this right. They have the research chops and the product instinct to package technology for users.
But at the core, it always comes down to – does the product work?
05 | New distribution models
Maaria (my partner at Timespan) and I believe the best products are designed for distribution from day one – where the growth strategy is deeply embedded in how the product is used. That principle guides a lot of our investment thinking. It seems even more important as the field becomes more crowded.
What new distribution methods do you see as software becomes more autonomous? How will AI-powered products reach users in ways that weren’t possible before?
Boring answer, you’ve probably heard this a thousand times – pricing. Don’t even talk about selling seats. Quantify the outcome your product delivers, and price based on that.
I invested in Recraft, an AI image generator, and when the founder wanted to use a monthly pricing model, I suggested charging per image generated instead. They went that route and are seeing incredible growth.
In B2B SaaS, selling to enterprises or mid-market is challenging because you often need to sell before gaining access to data, making it hard to really prove value.
The sales process is still sequential – pitch the value prop, run a trial, prove it, then deploy. I’d love to see this whole chain disrupted.
Are there new ways to integrate AI to prove the value prop more quickly, to reduce friction in adopting these tools?
I completely agree. At Forethought, one of the big changes we made to distribution last year was launching a free trial version for everyone, whether it’s for knowledge-based RAG or agentic workflows. It takes less than 24 hours to integrate, about 8 hours on average.
Our win rates went up when we started doing this.
Speed of deployment is a huge advantage in AI. It all comes down to one question – does it work? If you can prove it works quickly, in a simple, fun, or clever way, you'll be ahead of your competitors.
Maybe there’s an idea here – an agentic sales motion where AI-driven systems integrate with APIs and data in real time, delivering value as part of the pitch itself. In the sales pitch, you talk about the value, and by the end, you’re proving it.
Oh, man! I actually did something similar today, but not with APIs. I used web scraping. I told them, "We can launch a bot for your website now, just off your site." And actually pulled it off in the meeting with our technology.
06 | Building trust through UX
Another aspect of the application layer I’ve been thinking about is how UX will evolve.
For any technology to scale, users need to trust and love the experience. Even if AI agents can handle tasks, people won’t fully hand over the keys without confidence in the system.
Forethought has been great at this. Your product is really nuanced in how it prioritizes trust, how it augments the end users – customer support agents. It integrates seamlessly into their workflows, rather than replacing them.
Do you have any frameworks for thinking about how UX will evolve? Where will the line be between tasks we want to keep human and tasks we’ll be comfortable letting machines take over?
That’s a deep question. For now, I think the key idea is "show your work."
As things become more autonomous, people need to trust the system, and that’s hard when they don’t understand how it arrived at a decision. Even if the AI gives the correct answer, people want to know how it got there.
We learned this the hard way at Forethought.
At first, we’d just give the answer, but customers wanted more control. They wanted to know the data sources, understand how the decision was made. Giving people control, even if the AI is doing most of the work, will be a big UX principle moving forward.
Another big point is, instead of building a whole new app, bring the intelligence to where the user is already working. At Forethought, we didn’t try to rebuild Zendesk’s ticketing system – we brought intelligence into it, subtly, while they were working. This will become more important over time.
Another point is to rely on human feedback for system improvement. Allow users to interact with AI, within their workflow, through natural language, instead of relying on pre-programmed decision trees. This creates positive feedback loops, so the AI gets better over time.
Many are not doing this yet, but I expect it will become more common.
There are so many possibilities – interfaces shifting from visual dashboards to text-based inquiry, coding automation enabling non-technical users to customize their own UX.
It feels like these aspects could really make or break a product experience, even if the raw agentic capabilities are there.
Exactly. And that’s what I meant by "startup-complete." You still need to build a great user experience, figure out distribution, and build a sustainable business. Having the best LLM won’t do it alone – that will commoditize.
It’s so hard to build a successful AI company. Even though tools have gotten easier to use – maybe because the tools are easier to use – creating a sustainable business requires so much more than just a fancy demo.
Totally. I suspect this will play out like past tech cycles.
In the early days of the Internet, open protocols like HTTP, SMTP, and TCP/IP enabled a wave of digital businesses to be built. These open standards were the foundational building blocks of the web.
Some companies succeeded, like Amazon, but there was also a lot of over-investment and hype. A lot of companies failed because they couldn't figure out product.
We’re in a similar moment now – there’s a rush of capital because no one wants to miss out on the AI company that dominates a particular sector.
But, as you mentioned, complexities around UX, distribution, and product haven’t gotten any easier. It’ll take time for the right form factors to emerge – patience, focus, and real product taste will separate the winners from the losers.
I’m not sure there’s always a first mover advantage right now.
Absolutely. For any startup, even if you build the best product, there’s still a high probability of failure.
With AI products, it’s even harder to succeed.
You’re dealing with new workflows and big questions about how humans interact with machines. The core infrastructure is shifting so fast. It's a tougher problem. The quality of the team and the ability to build real, scalable products are still key. Despite the noise, the fundamentals haven't changed.
07 | Rethinking the entrepreneur-investor relationship
As you think about the entrepreneur-investor relationship, what do you think needs to change from the last tech cycle? Do you think that there's a way that investors and entrepreneurs can interact better or differently?
I have a lot of thoughts on this, not necessarily related to AI.
A few things come to mind. My best investors are the ones who get in the weeds.
One investor on my cap table, Vanessa Larco at NEA, is a world-class product thinker. She’d sit down with me and my PM and nail our product strategy. Another investor with go-to-market experience helped us with a full sales training session. With you as well, I’d see the tangible value-add from a customer intro, candidates, or helping close capital. Those kinds of investors, who really care and get involved, add the most value.
Most VCs just give you money and opine in board meetings. If a VC can send you one customer, one candidate, or have one conversation around capital, they're in the top 80th percentile. That’s the sad reality.
The hype cycles we see now are great for short-term excitement, but in the long run, building a company comes down to the fundamentals. It’s about those tough moments, like losing your first customer or struggling to raise your next round, when you need a backer who truly understands the vision and sticks by you.
We need more VCs who are genuinely involved and care about the journey, not just chasing the next big thing.
I agree. Venture has become so transactional. People are driven by fear – of missing out, of something not working and looking dumb – so they spread themselves thin trying to hit the one thing they can point to. This dilutes the focus it takes to build something important.
You see it with portfolios that are 50+ companies – how can you dig in and be thoughtful for each of those companies, or make any of those positions meaningful for your fund?
I do see a real changing of the guard in VC, of new investors starting new shops, trying to solve this problem, which is amazing.
I agree.
Thank you for having me. I appreciate you letting me hop on and ramble about AI. It's always fun, and always great to catch up with you.
You too, as always.