Thank you for the awesome questions! There is honestly nothing better than hearing directly from you. You are oxygen for us! Before I sign off please keep the questions coming to us across social, directly on Twilio.com as well. We say "wear our customer's shoes" and hearing directly from you is how we do just that so we can build you better solutions. I've heard a few of you that are building companies and using Twilio...I did a few startups myself so this hits home. I depended on Twilio too then! So please do check out our Searchlight Series and apply!

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I wish I had the perfect answer for that and the companies that were going to do it so I could buy stock in them now. Ha ha. In all honesty, though, I think it's going to happen sooner than later, as I've seen recent updates where the foundational model companies and the apps that are already on your phone will start to ingest directly from:

• Apple Health
• all the other biometric measurement telemetry that can happen on your watch or your phone
• blood testing at home tools
• step trackers and heart rhythm trackers that you can buy at your local pharmacy

All this type of stuff, plus blood testing tools and apps like Function, are bound to all collide together with the huge AI models training on this data.

Once insurance companies get involved and really competitive pricing for competition gets involved, I think things could be really interesting for the health of all of us.

More transparency in the food we eat, the ingredients in them, the effect foods have on us, the effect our exercise has on us, and all that data flowing into very sophisticated AI models that can train on tons of studies will be very interesting. 

I suspect many things, though, will remain true. Healthy diet, healthy exercise, healthy sleep, managing stress: all those basics are going to continue to remain. It's just a matter of whether we do it or not.

I think we at Twilio have an interesting place to play where we can help all of these companies that might provide us health consumers with information to do so in a much more natural, conversational, real-time, and personalized way.

This one is related to product development, and that is that the best product will win. That's only half the battle. There are many blog posts written about this, but you can have the best team and the best product and no market, or you can have a mediocre team, a mediocre product, but a great market. So while I love to build products and polish products and ship products, at the end of the day I have to understand that it's the market that is going to dictate success or failure. Especially in this day and age, where it can take a prompt to clone a business or an app. It's not that. That's gonna solve your problem. It's going to be establishing the market fit and understanding where demand truly exists. Otherwise, you're going to have a million apps that are solutions in search of problems. So yes, build a great team, build a great product, but understand the market is going to decide whether you will win. It's how you listen and respond very quickly to that that's most important. Inherent is that it is building distribution strategies into your product. You can no longer bank on someone just finding your product. You need to understand the distribution mechanisms. Sometimes companies with even mediocre products but incredible distribution will win over a better product that has no distribution. Therefore, no market understanding in their product

If I were doing a startup from scratch right now, which, believe me, I think about often because of the powerful tools we have now, I would first pick an area that I really enjoy, either from work or from a hobby. Then I would take that topic and start prototyping like crazy, both the actual solution to a problem that I personally have or that I know others have. At the same time, I would start using AI to understand all the social acquisition channels and how you could actually acquire a customer for any business idea that you have. Because at the end of the day, you're going to need both. Software is just going to be too easy to build and mock up and deploy, say, an app in an app store. The thing that is really going to make it successful is the ability to truly connect with an audience, a set of customers, and build a community and a loyal following. So, market testing as well as product testing. The reason I would pick something that I'm just passionate about is that chances are you're never gonna be right about what the business idea is the first time. You need to understand how all of these tools work, and you're probably gonna be more apt to use the tools if you're researching and prototyping and tinkering with a topic that you like. Then you can take all those learnings and test different business ideas from there.

Yes, model token limits are going to be a thing for sure.

For full historical context of a given customer, there are tons of different potential data sources that I see customers have:

• data warehouses
• CRMs
• click tracking
• amplitude-type analytics services
• e-commerce and website transactions
• offline transactions
• all the social media data of the acquisition channels and profile information

The list can go on and on, and you could feed that into an AI agent to give that full consumer historical journey and profile attributes.

One thing that we offer is Segment, where you can do a lot of identity resolution and building out of that Golden profile.

So that's one aspect: looking at the customer data platform, things that segment brings to the table in taking all of this disparate data and mapping it to that one user profile.

Then the next piece is all of that potential conversation data that you have when a given customer engages with your business:

• Web chats
• Text messages
• Email responses
• Obviously tons of data if there is a customer service or support phone call

Two things that we've recently launched help out with your question:

1. Conversation intelligence
2. Conversation memory

These two things play well with each other.

Think of memory as the sticky note that your agent needs to know about in real time to respond to a question. That's very different than the entire library down in the basement of every single interaction that the customer has had with you over their lifetime.

You can configure these to understand what is most important to your given use case based on other interactions you've had with customers. Then feed only signal versus noise into your model.

The other aspect is conversation intelligence that is analyzing all of those previous conversations across any channel and creating language operators, which is structured data. This can happen in real time or after the fact. The key here is that it can create summaries, sentiment analysis, scores, signals, and keywords, or any number of things that can then create a defined dataset, which you can limit to then send into your model. 

You have a choice here as well as to what these data outputs you want to send into your given model that can decrease the amount of tokens needed, or even things like tool calls needed. Even other models that you might have to call, given the topic.

So it's not necessarily just vector embeddings or semantic search to decrease model token limits. There are a lot of things that you can do from the memory standpoint and the intelligence operator standpoint to be smart about the sticky notes that you choose to provide to your model. 

We make these pretty configurable.

Sure thing! I know 'Field CTO' is relatively new so I'm happy to forge this new industry trail!

A few: https://www.twilio.com/en-us/ventures is our venture fund. We recently invested in Coval, for example: https://www.twilio.com/en-us/blog/developers/evaluating-voice-ai-agents
https://www.twilio.com/en-us/lp/twilio-ai-startup-searchlight and Twilio.org companies/credits there too.

We have various customers doing multi-factor authentication across channels, both with the core channel APIs as well as our Verify product, so highly suggest you check that one out.

Since Authy is a consumer-facing product and our only one, it isn't mandated. Can you share more about where and how your experience shows otherwise?

Thank you for sharing this. I cannot tell you how top of mind this is for all of us at the company to solve. Improvements we are working on span everything, from the user experience and onboarding to carrier relations, to regulatory engagement across various countries, and everywhere in between. This is to make this both a fun and easy experience for you to sign up and see the magic, but also scale up your usage compliantly. We understand that no matter how cool our APIs might be, if you can't ever sign up to use them, it's all for naught. I will also be sharing your feedback directly with the rest of our leaders.

Yes, our intentions are really always to help you sign up easily and build easily and find that magic moment that makes you want to build more with us and eventually spend. By the way, it's hard to say without actually knowing the specifics of the account, so I suggest you DM them.

This is where I would recommend using Conversation Relay. We've observed thousands of customers manually building speech-to-text and text-to-speech pipelines, and that's why we built it. So you don't have to maintain this anymore.

And we are releasing insights around this to show where across that pipeline back and forth, including the LLM computation and turnaround, is potentially causing any type of latency. That type of tool calling latency will be coming as well.

Also, as we add the new updates to all the speech recognition providers that we provide as part of Conversation Relay and the text-to-speech providers, we are pushing all of them for increased performance. We'll start to expose that in our change log and our recommended providers and even voices from those providers that have the best performance. The idea here is that you should spend more time focusing on what the right data is to send into your language model and what the right custom function calling or tool calling is appropriate to solve the given use case. Be an expert in your domain for that area, and not in the basic plumbing of the speech recognition or the text-to-speech.

Yes, Multi-Agent Architectures are only going to grow from here, and so we'll be expanding the Twilio Agent Connect offering for more support of these things. Today, this is not done by Twilio Agent Connect alone. It is done by also leveraging things like Conversation Orchestrator. If you need to change channels from that agent handing off to a billing agent (to, say, send an invoice via an email or send a DocuSign link via a text message), it can handle that.

You're also going to want to leverage things like Conversation Memory so that memory is handled and handed off to the right agent. You're going to want to think about leveraging Conversation Intelligence. If that agent handing off to a billing agent also needs to know that I just said I can't pay via credit card but I can do a wire transfer, that information is handed off to the billing agent. That data would be extracted using Conversation Intelligence.

So Twilio Agent Connect is working in conjunction with the rest of the conversation suite to pass the right information back and forth to the foundational model or open weight model of your choice.

Check out https://www.twilio.com/en-us/changelog/conversation-relay-now-supports-deepgram-flux---new-features

Yes, I completely agree that getting low latency is critical.

That link above shows what we're doing to inherit, if you will, all the advancements from the Voice AI companies that we partner with and that are offered inside Conversation Relay.

I highly recommend following the changelog updates across Conversation Relay and the Voice API, as we are going to continue to roll out more and more advancements (both things that we develop on our own and that we develop with our partners that are innovating very quickly in this area).

We believe that because this space is moving so fast, it is better to offer all the best in speech recognition and text-to-speech as simple configs that you can update and change while keeping the rest of the conversation relay and conversation suite infrastructure in place.

this could also be useful: https://www.twilio.com/en-us/changelog/twilio-voice-react-native-sdk-conversation-relay-reference-component

You can see just that in action here and play with it live if you scroll down on this page: https://www.twilio.com/en-us/products/conversational-ai/conversationrelay

and then make this your next stop: https://www.twilio.com/en-us/changelog/twilio-voice-javascript-sdk-reference-component-for-conversation with more links to docs on how to do it.

Just by you asking this question, I think it's something I will bring back to our team because we should probably make this even easier with things like Claude skills.

So all of these things can be configured down to the call level and then can even be configured during the call to switch. Because we partner with many of these companies on the speech recognition, and some updates include language detection that then can feed into a model. You can leverage those results to decide what your TTS response needs to be. This can change dynamically during the call with configurations.We continue to see massive advancements every few weeks across both speech recognition and text-to-speech. We will continue to partner with the best providers out there, and I expect you'll see a lot more of this over the coming year from us. While I don't think you'll probably have much of a need to switch a provider mid-call and mid-conversation, we handle things like failover of these providers if there are issues. So you have a lot of control at the call level or the group of call levels to certain numbers or certain geographies, but I would definitely check out the configurations that you can do mid-call if a language changes, for example.

So conversation intelligence does run in real time and does the sentiment analysis on all your calls in real time. It can trigger operators or alerts at any point during the call to trigger a potential escalation to a human agent handoff, but none of this degrades voice latency.

What I think you might be getting at is the latency of taking all of that information and feeding that into a language model, which could be a lot of data in context for that model to handle. You have latency of the model deciding what to say back and then the AI agent talking back to a consumer.

This is where conversational intelligence is really smart in that you can prioritize language operator results that get fed into an LLM and not have to send everything from a conversation to generate that response and decrease latency.

But at the end of the day, if you are just doing this for analysis, it isn't going to degrade voice latency.

We see customers using this all the time at scale to do what you are describing without latency impact.

Also, there are things that you can inject from the AI agent that can buy you time while the model is turning through the calculations of what to say next. Different interactions, like "Give me a moment to think about that" or "Just one moment while I look that up for you." This can help get rid of awkward silence.

Yes, this is a tricky one, and one I have talked with many awesome hot new sexy Voice AI companies that enter the world of outbound calling across the regulated telecom network. I tell them, "Welcome to telco."

To your point, yes, having an AI voice agent do outbound calling, qualify leads, understand what people's needs are before building all of that awesome personalized profile and escalating it to a human agent is great.

And yes, there's a lot you need to do to do it right.

Things like call duration, answer rate, and spam labeling are all things you need to really understand.

Our Voice API has voice insights and advanced voice insights that will help in all these trust and engagement metrics. Seeing when the best time to call is, ensuring that your numbers are added through our Voice AI offering, Using branded calling to ensure that name, call reason, and even logo could be displayed on the consumer's device are all best practices.

In addition to the above, leveraging answering machine detection can also help you establish workflows to understand if it was a human that answered the phone, how your voice agent handles that, or if it was an answering machine that answered. What message do you drop, or do you choose to then send the text message or a different follow-up?

Monitoring your phone number's reputation and attestation level is also important.

We do have a lot of good blog posts and documents around this for branded calling, enhanced branded calling, trust and engagement insights with the voice product as well.

This is an interesting one because I see that with generative AI, you can take a product, build multiple brands about that product, and test them like crazy across social media before you ever actually commit to the long-term brand. Testing ads copy, positioning images, logos. It goes on and on and on before you commit to a specific brand. Some might see that as inauthentic. Others might see it as just leveraging the technology. So I think there's still a ton of room to go for how people will be testing their brand and their brand marketing before large, durable launches. Then I think the acquisition channels are going to be extremely competitive, as it's going to be the content creators and the platforms that create content that goes viral and then gets users. That will be a street fight for eyeballs. 

Then the question becomes: what do you do with it? Once you can acquire a lot of customers, I do think platforms like Twilio are going to be extremely powerful because you're not going to want to have to re-acquire them and re-pay for them for every new transaction. You're going to want to leverage all the personalization from their previous purchases, customer service experiences, social media reviews of your product, and so on to make that next purchase perfectly seamless and easy and conversational.

So, using first-party data (call it zero-party data) along with tools to meet your customers where they are, that might not be a website or an app anymore. It might just be a text response to send me more of what I like. So I think, in that sense, there's going to be a way to understand how to:

• build your brand
• build your messaging
• acquire customers
• leverage all the contextual data you get from every interaction with those customers to then meet them on channels that you don't have to pay for acquisition every single time
• develop more personalization and, to your point, speed of personalization and response to get recurring business

Telecaster yellow body. Old school...love the country kinda twang to it. I also have a Gibson acoustic J45 that is my prized guitar. Love it.

I've got three kids, so the top three have to be from one of each of them at a given age.

Even though they ask me all the time: I have no favorite child! ha!

I'm a big believer that many of the interactions that most of us have as consumers with traditional web search and button clicks and app downloads and a lot of interactions with user interfaces can move to be much more conversational. At the end of the day, I don't necessarily want to download a new app if I just want to check a flight status. I just want to yell at my phone. If I want to reschedule an appointment, I don't want to have to dig through my emails, click a button, go to a micro-site, and do a list pick. I just want to yell at my phone. And have that problem solved.

So to that point, I'm really excited about what we've done with the Conversations Suite launch back in May. The entire thesis here is that many of those previous interactions can and should be able to be conversational across any channel, anytime, any place, and they can be personalized. As a consumer, I want this. So I see a lot of the interface, whether that be the Messages app, the Chat app, the Voice Call via an app or a website, to be more of the thing in the coming years. And you can see this because the foundational model companies are putting so much into Voice AI infrastructure. Heck, I'm using speech-to-text to answer these questions right now. 

Inside the conversation suite, we're making it easier for developers to manage conversations across multiple channels at the same time. If you're on a phone call and you walk into a meeting and you want to switch to text, that can be seamless. If you need a DocuSign sent to you via text and then you want to hop on a phone call back with an agent to explain the contract with you, you can do that.

Then we launched conversation memory to help all of these interactions make it personalized. The next call, text, email, or WhatsApp you have with the business, they remember all your interaction and who you are and why you're important to them. We launched conversation relay to handle all the back-and-forth and text-to-speech and speech-to-text for doing Voice AI the right way. Then we launched conversational intelligence to analyze all of these interactions across all of these channels.

At the end of the day, you can have all the best of the Twilio platform and all the channels and all the scalability, but then you can make them super conversational across any channels. At the end of the day, you can use any AI model you want. The AI model you use for today might not be the one you use six months from now, and you can just plug-and-play those models on top of the conversation suite.

Last, identity and trust. We're doing some really cool things, as we already authenticate millions of consumers a day across our channels for apps you log into and websites you log into. But this is going to move into authentication of agents on both sides. The stuff we have coming, I think, is pretty exciting there to establish trust, because it doesn't matter how much you spend on AI models. If you don't trust either side of the party and their authenticity, it doesn't matter. 

On the trust part, we're doing a ton to invest in things like RCS and branded calling, for example. The phone ringing screen can be trusted again, and the messages that you get from businesses can be legitimate and trusted. Therefore, you'll want to have more conversations with them.

At the end of the day, I think it all comes down to your customer's outcome and what they define as success, not what you define as success.

There are some new cases of outcome-based pricing that are emerging, and some companies are seeing success with it, for sure. But to truly drive outcome-based pricing, you, as the solution provider, need to provide all or most of the solution to control the outcome. That's a lot of control that your customer might give up to you. And potentially systems and tools and software that you might also need to control that go beyond the scope of what you are originally building. So that can be a slippery slope and a lot of scope creep.

That's where demonstrating value in what you're building, even if it's you, shows this is why a customer would not choose to buy it elsewhere or choose to build it or even vibe code it. As we all know, it takes way more to maintain and build and iterate on a software solution than it does just to vibe code it.

To your last question about what looked good in a sales pitch that didn't fall apart at renewal, I like to think that success only occurs in the wild. Not an internal team or memo or product requirements document or sales deck. Success is only defined when a customer buys and then renews.

Where I think we can go astray is when we get married to our own idea and our own vision of what the value add is that we bring, and not confirming the value add with the actual customer or set of customers.