The Startup Alternative with Gabriela de Queiroz – O’Reilly

Generative AI within the Actual World

Generative AI within the Actual World: The Startup Alternative with Gabriela de Queiroz

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Ben Lorica and Gabriela de Queiroz, director of AI at Microsoft, speak about startups: particularly, AI startups. How do you get observed? How do you generate actual traction? What are startups doing with brokers and with protocols like MCP and A2A? And which safety points ought to startups look ahead to, particularly in the event that they’re utilizing open weights fashions?

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Concerning the Generative AI within the Actual World podcast: In 2023, ChatGPT put AI on everybody’s agenda. In 2025, the problem shall be turning these agendas into actuality. In Generative AI within the Actual World, Ben Lorica interviews leaders who’re constructing with AI. Be taught from their expertise to assist put AI to work in your enterprise.

Factors of Curiosity

0:00: Introduction to Gabriela de Queiroz, director of AI at Microsoft.
0:30: You’re employed with quite a lot of startups and founders. How have the alternatives for startups in generative AI modified? Are the alternatives increasing?
0:56: Completely. The entry barrier for founders and builders is way decrease. Startups are exploding—not simply the quantity but additionally the attention-grabbing issues they’re doing.
1:19: You catch startups once they’re nonetheless exploring, attempting to construct their MVP. So startups have to be extra persistent in looking for differentiation. If anybody can construct an MVP, how do you distinguish your self?
1:46: At Microsoft, I drive a number of strategic initiatives to assist growth-stage startups. I additionally information them in fixing actual ache factors utilizing our stacks. I’ve designed packages to highlight founders.
3:08: I do quite a lot of engagement the place I assist startups go from the prototype or MVP to impression. An MVP shouldn’t be sufficient. I have to see an actual use case and I have to see some traction. After they have actual prospects, we see whether or not their MVP is working.
3:49: Are you beginning to see patterns for gaining traction? Are they specializing in a particular area? Or have they got an excellent dataset?
4:02: If they’re fixing an actual use case in a particular area or area of interest, that is the place we see them succeed. They’re fixing an actual ache, not constructing one thing generic.
4:27: We’re each in San Francisco, and fixing a particular ache or discovering a particular area means one thing completely different. Techie founders can construct one thing that’s utilized by their buddies, however there’s no income.
5:03: This occurs all over the place, however there’s a much bigger tradition round that right here. I inform founders, “You want to present me traction.” We now have a number of corporations that began as open supply, then they constructed a paid layer on high of the open supply challenge.
5:34: You’re employed with the parents at Azure, so presumably you recognize what precise enterprises are doing with generative AI. Are you able to give us an thought of what enterprises are beginning to deploy? What’s the stage of consolation of enterprise with these applied sciences?
6:06: Enterprises are somewhat bit behind startups. Startups are constructing brokers. Enterprises will not be there but. There’s quite a lot of heavy lifting on the info infrastructure that they should have in place. And their use circumstances are advanced. It’s much like Large Information, the place the enterprise took longer to optimize their stack.
7:19: Are you able to describe why enterprises have to modernize their knowledge stack?
7:42: Actuality isn’t magic. There’s quite a lot of complexity in knowledge and the way knowledge is dealt with. There may be quite a lot of knowledge safety and privateness that startups aren’t conscious of however are essential to enterprises. Even the sorts of knowledge—the info isn’t effectively organized, there are completely different groups utilizing completely different knowledge sources.
8:28: Is RAG now a well-established sample within the enterprise?
8:44: It’s. RAG is a part of all people’s workflow.
8:51: The widespread use circumstances that appear to be additional alongside are buyer help, coding—what different buckets are you able to add?
9:07: Buyer help and tickets are among the many foremost pains and use circumstances. And they’re very costly. So it’s a simple win for enterprises once they transfer to GenAI or AI brokers.
9:48: Are you saying that the device builders are forward of the device patrons?
10:05: You’re proper. I discuss rather a lot with startups constructing brokers. We talk about the place the trade is heading and what the challenges are. For those who suppose we’re near AGI, attempt to construct an agent and also you’ll see how far we’re from AGI. Once you wish to scale, there’s one other stage of issue. Once I ask for actual examples and prospects, the bulk will not be there but.
11:01: A part of it’s the terminology. Individuals use the time period “agent” even for a chatbot. There’s quite a lot of confusion. And startups are hyping the notion of multiagents. We’ll get there, however let’s begin with single brokers first. And you continue to want a human within the loop.
11:40: Sure, we discuss concerning the human within the loop on a regular basis. Even people who find themselves bragging, if you ask them to point out you, they’re not there but.
12:00: On the agent entrance, if I requested you for a brief presentation with three slides of examples that caught your consideration, what would they be?
12:30: There’s an organization doing an AI agent with emails and your calendar. Everybody makes use of e-mail and calendars all day lengthy. If we wish to schedule dinner with a bunch of buddies, however now we have individuals with dietary restrictions, it could take eternally to discover a restaurant that checks all of the bins. There’s an organization attempting to make this computerized.
14:22: In latest months, builders have rallied round MCP and now A2A. Somebody requested me for an inventory of vetted MCP servers. If the server comes from the corporate that developed the applying, tremendous. However there are millions of servers, and I’m cautious. We have already got software program provide chain points. Is MCP taking off, or is it a brief repair?
15:48: It’s too early to say that that is it. There’s additionally the Google protocol (A2A); IBM created a protocol; that is an ongoing dialogue, and since it’s evolving so quick, one thing will in all probability come within the subsequent few months.
16:31: It’s very very similar to the web and the requirements that emerged from there. You may make it formal, or you’ll be able to simply construct it, develop it, and in some way it turns into an empirical open normal.
17:15: We’re implicitly speaking about textual content. Have you ever began to see near-production use circumstances involving multimodal fashions?
17:37: We’ve seen some use circumstances with multimodality, which is extra advanced.
17:48: Now it’s important to develop your knowledge technique to all these completely different knowledge varieties.
18:07: Going again to the slides: If I had three slides, I’d attempt to get everybody on the identical web page about what an AI agent is. All the massive corporations have their very own definitions. I’d set the stage with my definition: a system that may take motion in your half. Then I’d say, when you suppose we’re near AGI, attempt to construct an agent. And the third slide can be to construct one agent, fairly than a multiagent. Begin small, after which you’ll be able to scale, not the opposite means round.
19:44: Orchestration of 1 agent is one factor. Lots of people throw across the time period orchestration. For knowledge engineering, orchestration means one thing particular, and rather a lot goes into it, even for a single agent. For multiagents, it’s much more advanced. There’s orchestration and there’s communication too. An agent could withhold, ignore, or misunderstand info. So keep on with one agent. Get that completed and transfer ahead.
20:33: The massive factor within the foundational mannequin area is reasoning. What has reasoning opened up for a few of these startups? What functions depend on a reasoning-enhanced mannequin? What mannequin ought to I exploit, and may I get by with a mannequin that doesn’t purpose?
21:15: I haven’t seen any startup utilizing reasoning but. In all probability due to what you might be speaking about. It’s costly, it’s slower, and startups have to see wins quick.
21:46: They only ask for extra free credit.
21:51: Free credit will not be eternally. However it’s not even the associated fee—it’s additionally the method and the ready. What are the trade-offs? I haven’t seen startups speaking with me about utilizing reasoning.
22:22: The sound recommendation for anybody constructing something is to be mannequin agnostic. Design what you’re doing so you should utilize a number of fashions or swap fashions. We now have open weights fashions which are changing into extra aggressive. Final 12 months we had Llama; now we even have Qwen and DeepSeek, with an unbelievable launch cadence. Are you seeing extra startups choosing open weights?
23:19: Positively. However they have to be very cautious once they use open fashions due to safety. I see quite a lot of corporations utilizing DeepSeek. I ask them about safety.
23:43: Within the open weights world, you’ll be able to have by-product fashions. Who vets the derivatives? Proprietary fashions have much more management. And there’s provide chain dangers, although they’re not distinctive to the open weights fashions. All of us depend upon Python and Python libraries.
25:17: And with individuals forking by-product fashions. . . We’ve seen this with merchandise as effectively; individuals constructing merchandise and being worthwhile on high of open supply initiatives. Individuals constructed on a fork of a Python challenge or high of Python libraries and [became] worthwhile.
25:55: With the Chinese language open weights fashions, I’ve talked to safety individuals, and there’s nothing inherently insecure about utilizing the weights. There is perhaps architectural variations. However when you’re utilizing one of many Chinese language fashions of their open API, they may have to show over knowledge. Typically, entry to the weights isn’t a standard assault vector.
27:03: Or you should utilize corporations like Microsoft. We now have DeepSeek R1 accessible on Azure. However it’s gone by way of rigorous red-teaming and security analysis to mitigate dangers.
27:39: There are variations by way of alignment and red-teaming between Western and Chinese language corporations.
28:26: In closing, are there any parallels between what you’re seeing now and what we noticed in knowledge science?
28:40: It’s related, however the scale and velocity are completely different. There are extra sources and accessibility. The barrier to entry is decrease.
29:06: The hype cycle is similar. You keep in mind all of the tales about “Information science is the horny new job.” However the know-how is now far more accessible, and there are much more tales and extra pleasure.
29:29: Again then, we solely had just a few choices: Hadoop, Spark. . . Not like 100 completely different fashions. And so they weren’t accessible to most people.
30:03: Again then individuals didn’t want Hadoop or MapReduce or Spark in the event that they didn’t have a number of knowledge. And now, you don’t have to make use of the brightest or best-benchmarked LLM; you should utilize a small language mannequin.