> Amid this news, a former Alexa colleague messaged me: “You’d think voice assistants would have been our forte at Alexa.”<p>I assume the goal of Alexa was never to be the top conversational system on the planet, it was to sell more stuff on Amazon. Apple's approach to making a friendly and helpful chat assistant helps keep people inside their ecosystem, but it's not clear how any skill beyond "Alexa, buy more soap" was going to contribute meaningfully to Alexa's success as a product from Amazon's perspective. I saw the part about them having a "how good at conversation is it" metric, but that cannot be the metric that leadership actually cared about, it was always going to be "how much stuff did we sell off Alexa". In other words, Amazon did not ever appear to be in the race to make the best voice assistant, and I'm not sure why they would want to be.
One thing really worth addressing from the post that I don't think author accepted, and I see this a <i>lot</i> with engineers:<p>> <i>"That did introduce tension for our team because we were supposed to be taking experimental bets for the platform’s future. These bets couldn’t be baked into product without hacks or shortcuts in the typical quarter as was the expectation."</i><p>If I can pump one learning into engineers' and PMs' heads it's this: intermediate deliverables are <i>not optional</i> no matter how cutting-edge your team is.<p>You will <i>never</i> succeed if your pitch to leadership is "give us a budget for the next N years and expect no shippable products until the end of N years". Even if you get approved somehow at the beginning, there's a 99.5% chance your team/project will be killed before you get to N years.<p>Again, once again for the audience in the back: <i>there is no such thing as a multi-year project without convincing, meaningful intermediate deliverables</i>.<p>To clarify, that doesn't mean "don't have multi-year roadmaps", it means "your multi-year roadmaps <i>must deliver wins at a consistent cadence</i>".<p>Understanding this will carry you a lot further in the industry.<p>As a fairly cutting-edge R&D team part of your job is to figure out what slice of this is shippable (and worth shipping). If you're coming up empty <i>you are not ready to pitch this to execs</i>.
IMO voice assistants keep underwhelming because customers don't want to learn their language--we want them to understand our language. It's frustrating to have to know the exact magic spell wording to get an assistant to do something. They need to be like "Computer" in Star Trek TNG or they're dead in the water. 80% of the way there still isn't good enough--I'd rather just use a mouse and keyboard than endure an awkward trial-and-error with an "assistant" that is supposed to be smart.
Hmm, I'm an early Alexa PM (2016) that left Alexa before the OP joined it (2019).<p>Alexa's main failure was mainly that the tech wasn't ready - it was basically a ASR + NLU + rule engine. If we had 2023 LLM tech, then we may have "won" the Assistants market.<p>Yes, organizational bloat and politics was a problem but OP was hired as a result of the mass hiring spree, so he was a beneficiary of that.
I worked on a research team in Alexa. Almost all projects were focused on short term delivery, non immediate need innovation was highly discouraged. Most models were just direct imports of open source models created by Meta/Google. Extremely short delivery timelines for incremental improvement. Alexa employees were often highly political tenured Amazon employees. Minimal room for growth as they sucked oxygen out of the room.<p>The Amazon philosophy of constant execution is at odds with large leap technical innovation. It works very well for ops heavy AWS orgs, and supply chain related optimization problems. The company has a cultural problem<p>Regardless of the above, ChatGPT made almost all NLP technologies across all companies obsolete.
The first paragraph of the first section of reasons given is:<p>> <i>Alexa put a huge emphasis on protecting customer data with guardrails in place to prevent leakage and access. Definitely a crucial practice, but one consequence was that the internal infrastructure for developers was agonizingly painful to work with.</i><p>I really don't want this to be a message companies are hearing right now -- that being conscientious about customer data is a lethal barrier to progress, in the "AI" gold rush.<p>Also, without knowing anything about the organization, I'd expect it to probably have a high level of dysfunction, being at a company known for being excessively metrics-driven from the top, and for ruthless stack-ranking and related HR practices... trying to organize a large coherent cutting-edge R&D effort against that cultural backdrop. Like suggested by this bit elsewhere in the section:<p>> <i>And most importantly, there was no immediate story for the team’s PM to make a promotion case through fixing this issue other than “it’s scientifically the right thing to do and could lead to better models for some other team.” No incentive meant no action taken.</i>
At Amazon you get paid when you create whole new businesses which you used to pitch to Bezos.<p>Incremental improvement was rewarded through the regular stock and pay process.<p>Thus no one cared enough to quickly switch to LLMs.<p>Suoer interesting how org design - even when brillaint can be severely lacking.
I bought into Alexa/Echo because at the time my late wife could not work remote controls (Multiple Sclerosis). She only had her voice.<p>It was great for her to play different radio stations, playlists, news. It did the job.<p>I did try linking it to a TV but that was terrible. Slow, janky, unreliable.<p>Since she died - Dec 2018 - "Alexa play LBC" "Alexa stop"<p>Oh, if you do have an Alexa device "Alexa, what noise does a hamster make?"
> Alexa put a huge emphasis on protecting customer data with guardrails in place to prevent leakage and access. Definitely a crucial practice, but one consequence was that the internal infrastructure for developers was agonizingly painful to work with.<p>I was in Alexa and this rings painfully true. So many workarounds and endless classification escalations. The customer-data certified compute environments were extremely painful to use (though later improved but still annoying) and getting data in or out, even for anodyne reasons, was nigh impossible. For a long period, even <i>getting access</i> to this system (called Hoverboard) took months. During my internship I spent about half of it waiting for access to be granted and had to spend a big chunk of it testing out my training system on CPU...not fun.
Amazon invested $4 billion into Anthropic [1], which makes Claude AI, the AI I use almost exclusively. Claude is being trained to be very human-like, safe and friendly and has improved very much in that area lately [2], so I assume the idea is that Claude will be Alexa 2.0. In my experience, even employees maintaining the current version of a product can be out-of-the-loop on the future of the product.<p>[1] <a href="https://www.aboutamazon.com/news/company-news/amazon-anthropic-ai-investment" rel="nofollow">https://www.aboutamazon.com/news/company-news/amazon-anthrop...</a><p>[2] <a href="https://www.anthropic.com/research/claude-character" rel="nofollow">https://www.anthropic.com/research/claude-character</a>
The infra part particularly resonated with me. Leadership caring about high level metrics deprioritize "in-the-weeds" dev experience. At <redacted applied ML place>, I was working on data infra, but pressure from above made it impossible to have time to focus and instead focused on short-term deliverables. It was hard to convince others to adjust their priorities for "the greater good".<p>The only way to make things better (in my mind) was to use my own time to improve the infra, and because the metrics don't track these infra improvements I don't get rewarded so I just became burned out.<p>Part of me think this is the reason why you want bloat in orgs, so that motivated people with enough redundancy will actually feel comfortable chasing longer term incentives.
I think we overstate how useful such a system is. Visual UIs with buttons are so predictable and efficient to use, so anytime you can reach for a screen and hand-operated-input for anything complex, you will.<p>I've stopped using home devices like the Echo (coz of privacy concerns, esp with hotword mistriggers): now use voice only when driving the car. Maybe multimodal LLMs like GPT-4o will spawn new useful use-cases, but I think they're unlikely to be for the same use-cases Alexa the product+brand is known for.
The problem with all the original iterations on these assistants is that they fail so often that people stop using them for anything except for the few things that they can do reliably which for most people is basically setting timers, playing music and turning the lights off and on.<p>They're annoying to use, because the interface sort of implies affordances (like, you know, just talking to it like a person) that aren't actually available, and really it's just a menu tree that's barely more sophisticated than a customer support call tree.
Good read and really just highlights the complexity and tension involved with huge corporate organizations. I would not have ever guessed that Alexa alone would have so many teams and engineers involved, because from the outside it seems like the only iterations were on the physical models. The voice assistant didn't seem to change in any meaningful way for a very long time. It even seems like Amazon employs some form of internal start-up model, but that still struggles because of the internal politics. Maybe when it comes to individual products, its best to keep the teams small and nimble.
Always surprised how little Alexa changed over the years from initial version.
Both on software and hardware there could have been so much possibilities and learning from years of user feedback.
Appstore and skills deserted from years of broken to unusable functionality.
Hardware remain stuck to initial speaker version where there was possibility to replace so many dedicated boxes around house each connected to power supply providing singular function - Access Point, routers, smart device gateway, streaming device, NAS for backup or private cloud, etc<p>Last one failure for entire BigTech where desire to maintain control prevented any form of standardization or interoperability to the point where hobbyist open source solutions are now leading on how to do smart home right way and not abandon user base in 6 months after release.
They haven't lost yet. Alexa is still the top conversational system appliance for most households right now. A smart speaker is almost an essential device among my group of friends. Siri on HomePod is still subpar for basic kitchen tasks like setting timers, adding items to shopping lists, and creating reminders. I don't even think Google has a current device that competes in this space.<p>I really want HomePod to be better at household tasks such as managing shopping lists, timers, and reminders, but it's not there yet. As soon as the HomePod can replace my Alexa devices, I'll be all in. I have a HomePod right next to every Alexa device in my house, and I'm just waiting for Apple to turn on their "Apple intelligence."
I'm not sure I would have a lot of use for voice-controlled conversational AI, as odd as that may sound.<p>Like most people, I use Alexa for _commands_: home automation, timers, tell me the weather, ask a specific question looking for a specific answer, play this music. That's not "conversational", and I don't want it to be.<p>I use generative AI for other things, mostly writing code for me, or telling me about code problems in general. It's rare that I want output that I'm _not_ going to copy/paste somewhere.<p>Alexa isn't a failure, it just didn't sell more stuff for Amazon. And, well, it costs an awful lot for them to keep running. So maybe it is.
I don’t know if my voice is weird or what, but these things- alexa, siri, etc. don’t work for me at all, they can’t understand anything I say unless I repeat it slowly half dozen times, yet regular people understand me just fine.
I had a manager who came from Amazon and shared some of the horror stories from their time on Alexa. It seemed like a lot of senior folks were only using it for career advancement because it was where all the R&D money was flowing into. So a bunch of hard working folks building things, leadership playing political games with each other, and an org that had no idea what problems they were actually trying to solve.<p>It definitely captured the market, but without a top down vision, the whole thing was just a huge letdown.
The problem with Alexa isn’t technology or product design. It’s the organizational issues. It was a bloated team with over 10000 people. After a decade of investment, they had no real business model. They may have had decent engineers and scientists, but lots of managers and executives got promoted to very high levels despite showing zero results, mostly based on the size of their teams. Those empire builders then moved around the company and industry, leaving behind a wasteland.
I set up our home with smart shades, lights, iAqua, Somfy, etc., but my family and me now just use the remotes due to frustration with Amazon Alexa.<p>I was always under impression that Amazon uploads all our data because I notice data transfers whenever I use voice commands, which makes me doubt their privacy claims.<p>It seems like Alexa was designed more to learn from us rather than to genuinely assist us. Its primary goal appears to be gathering data rather than helping users.
> Alexa put a huge emphasis on protecting customer data with guardrails in place to prevent leakage and access<p>For what it’s worth we were working on a conversational health app and this why we picked Alexa over alternatives (if you’re big enough to get on GPT enterprise you can probably implement HIPAA safeguards, but we never got replies)
You don’t need to bring a rocket launcher to a banana fight.<p>Most of the queries are gonna involve setting an alarm or turn on/off a thing.<p>They didn’t drop the ball- they were very customer savvy and really knew what they were getting into.
I remember talking to Alexa about eight years ago. I was a bit skeptical and asked, "what color is a red car?". I was seriously impressed that it knew that a red car is red.
I threw my Alexa in the garbage after I asked it to turn off my house lights and it tried to recommend something (a schedule or something) for what must have been the third time.
I never used smart speakers but do they allow third party apps and if they do how developed is the ecosystem? I see so many use cases as of voice commanded apps, voice games etc.
I've always thought it was Apple who dropped the ball with Siri.<p>When Siri came out in 2011 -- two years before Alexa -- all my coworkers and I had iPhones. I remember sitting in my office as people yelled at Siri all day trying to get her to be useful. "Hey Siri, what's the weather tomorrow? No... No SIRI, WHAT'S -- THE -- WEATHER -- TOMORROW!"<p>Even though it sucked, it seemed every hardcore Apple user was ready to jump onboard. Who cares if I'm in a crowded office with people trying to get work done while I spend 10x longer to perform a function in the noisiest possible way? I'm using this thing!!<p>The voice recognition has improved since then. But the functionality still sucks.<p>When I'm in private, there are a couple commands I'll use.<p>- "Hey Siri, call xyz" where xyz is someone in my contact list I have tested with Siri and is known to work. Not recommended to try without testing first.<p>- While cooking, "Hey Siri, set a timer for 10 minutes." Works great.<p>- While driving and navigating: "Hey Siri, take me to the nearest gas station." That one is pretty good, except the actual maps are not smart enough so sometimes you'll be turned around in the opposite direction you were going, since technically that's where the nearest gas station is.<p>I never understood why they couldn't make this tool better, even before LLMs and without any AI at all. Just hard-code a bunch of phrases, and ways to translate those phrases into some action.<p>"Hey Siri, how close is my UPS delivery?"<p>"Hey Siri, where can I get the best price on xyz cat food?"<p>"Hey Siri, what's my bank balance?"<p>"Hey Siri, how much is a Lyft to xyz?"<p>I bet if they had a single developer working on adding Siri commands full-time, they could announce something like 20-50 new Siri functions at every WWDC.<p>But it seems the goal now is just "Make it an LLM," instead of focusing on recognizing the task that the user wants to do, and connecting it to APIs that can do those tasks.<p>They could've dominated the "conversational system" market 13 years ago.
Come home to find a yellow ring on the Echo. "Alexa, tell me my messages." "You don't have any messages. You have one notification; would you like me to read it?" <silently> "Jesus H Christ, what do you think?!" <aloud> "Yes"
I have some personal insight on this one.<p>My opinion is that data access restrictions did not cause Alexa to fail. If you think about it, it wasn't lack of machine learning that contributed to its issues. Alexa attempted to solve the long tail of customer requests with the equivalent of spaghetti "if statements" - rule engines. This was never going to scale. Alexa did not have a generic enough approach to cover the long tail of customer requests (e.g. AGI). With rule engines, there was always a tension between latency and functionality. Alexa solved this with bureaucracy - monitor latency, monitor customer request types, and make business decisions about how to evolve the rule engines. But it was never fundamentally able to scale out of the most basic requests or solve chicken-egg problems (customers don't ask complicated requests because Alexa isn't capable, so they don't show up as large enough use cases to optimize for). Top use cases remained playing music and setting timers.<p>A more fundamental issue was monetizing. Early on Bezos liked the idea of having a small, essentially free, device that would reduce the friction to buying things. If you remember the "easy buttons" Amazon floated there were many ideas like this. In practice, building a robust voice assistant that could purchase items proved challenging for a myriad of reasons. So the business looked for other ways to monetize. Advertising kept coming up but there was rank and file pushback to this because it could break customer expectations and/or privacy concerns. Alexa considered pivoting into various B2B ventures (hospitality, healthcare, business) and other customer scenarios (smarthome, automotive) but took half-measures into each of them rather than committing to an opportunity. It felt like a solution looking for a problem.<p>Alexa would have (could still?) benefit from modern LLM technology. However to be truly useful it would need to do more than chat. It would need some layer to take actions. This would all have to be carefully considered and designed so that it scales - so that it isn't a bureaucracy trying to measure what people are wanting to do and "if statement"ing a rules engine to enable it. OpenAI and others appear to be poised with the machine learning expertise to do this.<p>Finally, it's my opinion that Alexa's machine learning scientists were very good, however as a population they did not appear to me to really care about the business/product use case. Many of them worked on research for publication on problems like distance estimation, etc. The expertise was very heavy on voice transcription and audio processing. However there was less expertise in "reasoning". This I hypothesize contributed to the approach of iterated rules engines, with the science community focused primarily with improving transcription accuracy by small numbers of basis points.