Yes. Section 5 talks about real-world deployment: 5.1: "The DSpark draft models are co-deployed with the preview versions of DeepSeek-V4-Flash and
DeepSeek-V4-Pro"; 5.4: "MTP-1 represents the former production setup, having
been superseded by DSpark two weeks following the DeepSeek-V4-preview release."
I see a world soon where there’s an extremely wide variety of small models for speculative decoding, unique to use cases, companies, and even individuals.
You clearly didn't read the recent speculative decoding papers because it's been possible to use any model to speculate for any other model for awhile. They solved the tokenization problems that prevented this in the past.
I’ve been using DeepSeek v4 pro for a month now in Kilo Code and its great. Fast, reliable, large context window and cheap as… Did 1,5B tokens this month and cost me 40usd (majority cached, but still).
It's in the JSONs in ~/.claude, but last 30 days only I think. You can have the model analyze history. So for correct history you'd need to run history analysis on a cron job or something. Kinda hacky.
> Local-first session search, analytics, insights, and token use statistics for coding agents, supporting Claude Code, Codex, and more than 20 other agents.
Which provider? I went through 40 bucks on it on openrouter. It was not a lot of back and forth, context ended at around 300k, 15kloc output. I was using opencode, unsure if I can make the total token count visible.
OpenRouter sometimes chooses a very expensive provider. Try the floor slug or choose directly the provider. I moved to just putting 5 dollars directly on deepseek instead of going through OR.
This is just one of many papers DeepSeek have released to be able to serve models at extremely cheap prices, unlike the others taking on >$100B+ of debt in building data centers for the same thing.
> As with V4-Flash, we treat this point as an indication that DSpark sustains useful
throughput under an interactivity target that the baseline cannot efficiently support. At matched system capacities, DSpark delivers 57% to 78% faster per-user generation.
Reminds me of the flawed solution in scaling servers in 2017 that use memory-intensive technologies by adding even more servers to solve the problem. (It just increases costs.)
Rather than doing that, think about which critical parts of your app can be written in a more performant technology.
Fast forward to 2026, now you can see who is just throwing more money at the problem to create even more problems where as DeepSeek is giving us optimized solutions.
I know exactly who I would pay attention to, and it is absolutely not Anthropic.
For so long American companies have operated under the assumption that servers are cheaper than developers, and that was used to justify all sorts of inefficient practices.
The last year has shown that’s not true anymore (even for web servers).
Must be wonderful to be on the board of OpenAi et al & their PE investors whilst China keeps blowing up these mines under their feet lmao.
Luckily Korean pension funds will buy all the trash as usual but goddamn you gotta start moving quick or you are gonna need some serious AGI to show you how to offload those bonds
DeepSeek continues to not only push the boundaries but also publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately. Chinese labs are doing the most interesting work in AI right now.
Publishing by necessity I wonder? American labs on the cutting edge pioneering the way forward, so Deepseek open sourcing what they’ve got is to help even the playing field.
Hopefully the experts here can offer insight. The above is just my hunch and I’m not a specialist in this field.
From what I gather, the Chinese are behind, but a lot of their research amounts to scrappy, clever discoveries in how to use more novel technologies (for Qwen and Deepseek, its mixture of expert models, that can do inference using a portion of the model at a time). The chinese also distill information from American models, so there’s that.
The American companies, from my impression don’t involve themselves with such lowly “hacks” because they have so much money to just push forward with doing everything on big heavy models that run on the most cutting edge nvidia chips that they can, the moment, kinda sorta get on demand (I say that in some degree of jest).
this is not an effective long term strategy in a collaborative environment that is advancing for the same reason that having a private secret fork of the linux kernel with a few proprietary improvements is not an effective strategy.
integrating your own work with the latest public advances takes resources. For one or two small changes this is manageable, but the further you diverge from the public, the cost of maintenance rises exponentially if you want to continue to integrate public advances. when you publish your meaningful advance, you offload the maintenance burden onto everyone else (and they only have to pay a linear cost rather than an exponential one) as it's integrated by default in new work.
In most cases, the (exponential) maintenance cost of integrating public advances with secret ones exceeds the value of the public advances, so most that undertake this strategy of advancing the open frontier in secret don't attempt to integrate continually, but instead try to make a breakaway sprint in isolation to grab a few sticky customers before the unstoppable wave of the public frontier catches up.
This is a pattern commonly seen in university research departments when researchers switch into product development mode, most of these projects are a sprint to advance away from the public frontier once a good idea is found and they do good work and find a few customers for a little while. But if you check back in a few years you won't find an advanced research department but a zombie IP company that brings in a steady income via IP enforcement and a small number of customers for whom switching is too expensive.
It used to be the case that NSA hired the majority of all math graduates in the US, and were assumed to be years ahead in cryptography. Yet in the 90s, it became clear that they no longer were that - among other things, the cipher of the notorious Clipper chip was broken, and we can rule out that it was made weak on purpose because the whole point of Clipper was that they had a backdoor.
So, despite hiring the cream of the crop of math graduates, who could read the papers of free academia, but whose own result the free world could not access - they fell behind.
I have a theory explaining why. I think it's because science is an interactive process. NSA cryptographers could read papers, but they couldn't talk openly with the authors of those papers, because of secrecy demands - even asking question might indicate what they were working on. You can easily imagine them spending months on something they could have avoided by going to the original authors and getting told "Oh, we tried that for a long time, it doesn't work".
Whether that theory is right or not, cryptography is a concrete example of a domain where public research with fewer resources beat private research with a lot more resources.
Everyone in this thread is getting distracted by nationalism, but you hit the nail on the head. In this case for whatever reason the Chinese AI industry is collaborative and the American AI industry is not. This will result in the Chinese companies making progress faster. Full stop. This isn't a judgement on the merits of either system, only an observation of likely results.
Hasn't that been the mantra of open source for 40 years. Armies of companies, trillions of valuation, or even just Wayland, suggest that isn't always the case.
The Linux Foundation was bankrolled by the US government (via grants and code donations) to undermine the EU Operating System industry. Symbian was going to be amazing, until Microsoft - an American company with government links - nuked it /s
The point that I was responding to was that open sores leads to faster development. It's 2026 and "Next Year will be the year of Linux on the Desktop" since about 2000.
One would have to conclude that there is little correlation b/w openness and progress speed. Sometimes open is faster, sometimes it isn't.
> This will result in the Chinese companies making progress faster. Full stop.
Is this happening? These open models have been a generation or two behind the closed models for quite a while now. They've been keeping pace but clearly behind.
They've been making enormous developments on a tiny fraction of the capital. Right now they've got no reason to devote half the electrical grid to brute forcing models when the Americans will waste their power doing that work and China can distill it for free.
I'm afraid I'm even balking at the word "pioneering" in context with US frontier labs. They are probably doing a few new things, right, but they are not blazing any trails for others to follow along, the Chinese are.
Mixture-of-Expert (MoE) was introduced in the 1990s [1, 2], see also
[3, 4]. The idea was that MoE scales up model capacity and only
introduces small computation overhead. MoEs did not become viable for high-performance
applications until sparse routing was integrated with modern deep
networks, made possible by large-scale distributed computation. The
breakthrough came with the development of sparsely gated networks [5],
which showed that it is possible to maintain model accuracy while
activating only a small fraction of a large parameter network during both
training and inference.
[1] R. A. Jacobs, M. I. Jordan, S. J. Nowlan, G. E. Hinton, Adaptive mixtures of local experts. (1991)
[2] M. I. Jordan, R. A. Jacobs, Hierarchical mixtures of experts and the EM algorithm. (1993)
[3] L. Xu, M. Jordan, G. E. Hinton, An alternative model for mixtures of experts. (1994)
[4] S. Waterhouse, D. MacKay, A. Robinson, Bayesian methods for mixtures of experts. (1995)
[5] N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, J. Dean, Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. (2017)
Yes, challenger Labs publish out of necessity. It is a marketing strategy. People assuming open source means giving something up, but the reality is that Z.ai has a revenue of some $100M and it would be about $0M if they never open sourced their models.
Probably because American AI companies are on the hook for quite a lot of investment money. I think they are trying to find the magical moat to justify their valuation.
Revealing optimizations similar to these would pretty much reduce their competitive position.
This is the way! Open source models will benefit, and once open source models reach the state of "good enough" the hyped up US AI companies will fear, since the availability of free, good enough, AI models will set the ceiling for how much they can charge. Then the bubble will pop.
I'm not sure I'd put Google in that list, but either way: Because they think they have enough capital that they can catch up and don't need the reputational boost of this.
The concept of open source doesn't really apply to AI models since their behavior is mostly controlled by the data they were trained on and the complex ways they are trained. Having the source code of the model by itself wouldn't help you.
From a practical POV having all the training data, training infrastructure, and training know-how wouldn't help you either unless you could afford to spend the millions of dollars (hundreds of millions for a SOTA model) in compute to train it each time they released a new training set, in which case you're only talking about the big commercial companies. "open source for the people" just does not apply.
> If (and that is a big if) the concept of open source doesn't apply, then the term shouldn't be coopted to mean something else though.
Yes, but for whatever reason this usage seems to have stuck. Open weights is definitely a better name. I assume the reason "open source" has stuck is because you can download and use it for free, but "open source" was always intended to be about "free as in speech", not "free as in beer". That said, I remember when the term "open source" was invented, and it was always a bit different, more commercially aligned, than the goals of the FSF.
> But even if I can't build it from source locally, being able to see what went into the model is an important part of what open source is about.
True. Unfortunately LLMs have become such a big money and closed enterprise (the opposite of OpenAI and Anthropic's altruistic founding principles) that it's hard to see these commercial models releasing their training data, especially since this data is the closest thing they have to a moat other than the cost of training.
The most valuable training data right now seems to be "reasoning data", and the need for this at least may disappear as AI moves beyond pre-trained language models to smarter systems capable of learning for themselves, and that can actually reason, not need to parrot reasoning data.
Look at how far OpenAI has drifted from their original mission. Everything comes back to greed, so it's ideal for the world if selfish motives happen to coincide with what's good for the world, like advancements in open models
Every company on the face of the earth has a mission statement involving some bs goal that sounds altruistic. For a good example look at googles mission statement.
The real mission statement for most companies is to make as much money as possible.
It's a standard take since it is how markets tend to work. They aren't powered by altruism, it is a big system for turning greed into good results. We don't have all this stuff because people suddenly woke up one morning and decided to be nice.
The standard is applied very inconsistently. Nobody accuses the local bakery of being motivated by profit, and that they don't bake bread for you out of altruism.
Mostly they kind of do since we do live in an utopian society of unlimited abundance. Extremely few people can afford to (or want to) spend a very large number of working hours without ever getting anything directly in return for it.
Open-source is also altruistic. If DeepSeek does become self-serving once they get the top spot, it doesn’t take away from the altruistic contributions that they made towards open models.
And ultimately the motivation for those contributions just doesn’t matter, except to those who like to anthropomorphize company and argue about their souls.
Contributing to it might not necessarily be. Most open source development is funded by large companies after all and from their perspective it can function as a cost saving measure. Allowing them to focus on their core products and removing the possibility of their rivals from getting a competitive advantage due to having a superior low level stack under their product.
Which is why open source is so successful in areas where software is a cost-center but mostly failed for consumer products (since spending resources on them would actually be altruistic unlike e.g. Linux kernel development)
No parent is right. The core root driver of the world is capitalism, open source exists downstream of that.
Software engineers need money to survive. If they exclusively work on open source stuff where are they getting money from to survive? Follow the money trail… even a donation… eventually it leads to an incentive based source or action.
Are you not implying below, with your words, that working exclusively on open source cannot bring money?
> If they exclusively work on open source stuff where are they getting money from to survive?
You seem to imply that open source is incompatible with making money. You seem to believe that if someone is making money they are not doing "exclusively open source" but something else in addition to open source.
> you need to follow the money trail. Money given to people who work on open source comes from non-open source places.
Money spent on coffee comes from non-coffee places, mostly. Does that mean one cannot make money exclusively selling coffee?
I get your point, it is very uncommon to live only of open source. That I can agree with. It is the exaggeration and dogmatism that is untrue.
You’re right. One of its big uses is for big companies to destroy the ability of individual developers or small companies to make a living selling software. It’s also used to harm larger competitors whose revenue depends on some non open source software. Essentially, open source is the new “dumping”.
That’s not to say open source is bad. But in a capitalist context, it’s certainly a very double-edged sword.
> If they exclusively work on open source stuff where are they getting money from to survive?
These are orthogonal. One can have a paid job while contributing to open source for entirely altruistic reasons.
> Follow the money trail… even a donation… eventually it leads to an incentive based source or action.
BS. Humans do things for altruistic reasons devoid of individual reward all the time.
I, myself, maintain multiple OSS projects entirely for fun and with the hope that others will find it useful. That's it, that's all. I also donate entirely anonymously to charities simply because I believe others deserve support and dignity.
This form of cold, American libertarianism you espouse is pure poison in the body politic, both in this US and globally. It degrades all of human interaction to transactions. Its no wonder that the US is where sociopaths like Zuck were birthed.
This statement is factually true and you are voted down because many people lack knowledge.
Any individual that provides free labor cannot survive off of said free labor. He must work for money to survive or get donations from someone who earned that money from incentive based labor in order to even buy the food he needs to exist as a living human being. Much of the time that labor is actually closed source.
This is a logistical reality. A lot of open source advocates are unable to get their brains out of the whole mentality that open source literally cannot exist without incentive based software supporting it. Who pays for GitHub to exist? Who pays for the food swes eat? I just code for open source all day and money falls out of the sky.
My smart friend says there are jobs that pay you to work on open source exclusively. Smart guy. In this case you follow the money trail. How does that company get enough money to pay a guy to work exclusively on open source?
Free labor enables capitalism, especially if you consider labor arbitrage as a mixture of free labor and properly compensated (according to the real value) labor. From literally being born, to family culture, education, and whatever level of broad social cohesion, it’s all free labor. Without that background, money itself loses its value, since an individual cannot have reasonable confidence in trading it for something of actual tangible value. It is abstract stored value, banked into society for free. Indeed, in many cases, the free labor is in the rational self interest of a group. But stability and love and peace aren’t monetized to their true value. Otherwise, markets should be much less stable. Bubbles are only notable for the large impact of a small group of bad actors. Overall, it’s pretty amazing what free labor does. Open source is just another instance of this long and critical tradition.
Free labor is derivative to incentivized labor. Your statement here doesn’t disprove or counter what I said. Again, follow the money trail. Everything you said if you follow the origin of the money it comes from paid, incentivized labor. Parents need money to raise kids… where do they get that money?? Our economy is called capitalism for a reason there is literally zero reference to charity or altruism in the vocabulary or even standard models that describe our economy and economic theory.
Put it another away: if we removed your ability to do incentivized labor and all you can do is charity work… you would run out of money and die from starvation. If we did the opposite and we removed your ability to do charity work… you’d be fine.
All of this re-emphasizes the point of this thread: In our objective reality, the world is driven by incentive based work while altruism is a side effect of surplus wealth generated by incentive based work. That is the fundamental reality.
Guessing the timing isn't accidental. Demonstrated openness vs harsh regulation
Strange timeline, though this only works because it’s aligned with Xi’s goals.
Mistral...don't fumble this
this is definitely where things are going. the enormous "eat the world" models have extreme diminishing returns by comparison.
I second ccusage, it's nice
> Local-first session search, analytics, insights, and token use statistics for coding agents, supporting Claude Code, Codex, and more than 20 other agents.
solid piece of software
It's drastically reduced my AI spend. I went from spending $40/day to $10/day.
https://github.com/esengine/deepseek-reasonix
> As with V4-Flash, we treat this point as an indication that DSpark sustains useful throughput under an interactivity target that the baseline cannot efficiently support. At matched system capacities, DSpark delivers 57% to 78% faster per-user generation.
Reminds me of the flawed solution in scaling servers in 2017 that use memory-intensive technologies by adding even more servers to solve the problem. (It just increases costs.)
Rather than doing that, think about which critical parts of your app can be written in a more performant technology.
Fast forward to 2026, now you can see who is just throwing more money at the problem to create even more problems where as DeepSeek is giving us optimized solutions.
I know exactly who I would pay attention to, and it is absolutely not Anthropic.
The last year has shown that’s not true anymore (even for web servers).
A&O no longer have the most to justify their high valuation. The only thing they can do now is to get the government forbid the Chinese models.
Hopefully the experts here can offer insight. The above is just my hunch and I’m not a specialist in this field.
The American companies, from my impression don’t involve themselves with such lowly “hacks” because they have so much money to just push forward with doing everything on big heavy models that run on the most cutting edge nvidia chips that they can, the moment, kinda sorta get on demand (I say that in some degree of jest).
They don't develop them because they don't collaborate publicly anymore.
Where would the whole industry be if Google never allowed publishing the transformers paper?
It's not a coincidence that the American AI industry grew fastest in capability when it was the most open.
integrating your own work with the latest public advances takes resources. For one or two small changes this is manageable, but the further you diverge from the public, the cost of maintenance rises exponentially if you want to continue to integrate public advances. when you publish your meaningful advance, you offload the maintenance burden onto everyone else (and they only have to pay a linear cost rather than an exponential one) as it's integrated by default in new work.
In most cases, the (exponential) maintenance cost of integrating public advances with secret ones exceeds the value of the public advances, so most that undertake this strategy of advancing the open frontier in secret don't attempt to integrate continually, but instead try to make a breakaway sprint in isolation to grab a few sticky customers before the unstoppable wave of the public frontier catches up.
This is a pattern commonly seen in university research departments when researchers switch into product development mode, most of these projects are a sprint to advance away from the public frontier once a good idea is found and they do good work and find a few customers for a little while. But if you check back in a few years you won't find an advanced research department but a zombie IP company that brings in a steady income via IP enforcement and a small number of customers for whom switching is too expensive.
How do you know they aren't doing this stuff? Something has to account for them leading the industry.
So, despite hiring the cream of the crop of math graduates, who could read the papers of free academia, but whose own result the free world could not access - they fell behind.
I have a theory explaining why. I think it's because science is an interactive process. NSA cryptographers could read papers, but they couldn't talk openly with the authors of those papers, because of secrecy demands - even asking question might indicate what they were working on. You can easily imagine them spending months on something they could have avoided by going to the original authors and getting told "Oh, we tried that for a long time, it doesn't work".
Whether that theory is right or not, cryptography is a concrete example of a domain where public research with fewer resources beat private research with a lot more resources.
One would have to conclude that there is little correlation b/w openness and progress speed. Sometimes open is faster, sometimes it isn't.
Is this happening? These open models have been a generation or two behind the closed models for quite a while now. They've been keeping pace but clearly behind.
But the word pioneer comes from French pionnier, literally “foot soldier”, a soldier who goes ahead to prepare the way.
If you don't publish you may be advancing, but you're not preparing anyone's way.
Multi-head Latent Attention (MLA), Multi-Token prediction, MoE architecture are some of the most famous examples.
MTP is from Meta
Another DeepSeek advance that the west are copying is DeepSeek Sparse Attention (DSA)
[1] R. A. Jacobs, M. I. Jordan, S. J. Nowlan, G. E. Hinton, Adaptive mixtures of local experts. (1991)
[2] M. I. Jordan, R. A. Jacobs, Hierarchical mixtures of experts and the EM algorithm. (1993)
[3] L. Xu, M. Jordan, G. E. Hinton, An alternative model for mixtures of experts. (1994)
[4] S. Waterhouse, D. MacKay, A. Robinson, Bayesian methods for mixtures of experts. (1995)
[5] N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, J. Dean, Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. (2017)
It's more a cultural thing. Sharing progress is just in their blood.
Revealing optimizations similar to these would pretty much reduce their competitive position.
I suspect their tune will change if they ever take the lead..
US labs in Google, Meta and SpaceX are not leading, none of them managed to build something on par with GLM 5.2.
Care to explain to me why they still don't collaborate and still choose to do it in private?
Those are mostly for embedded devices and the current "sponsor" is Apple.
From a practical POV having all the training data, training infrastructure, and training know-how wouldn't help you either unless you could afford to spend the millions of dollars (hundreds of millions for a SOTA model) in compute to train it each time they released a new training set, in which case you're only talking about the big commercial companies. "open source for the people" just does not apply.
But even if I can't build it from source locally, being able to see what went into the model is an important part of what open source is about.
Yes, but for whatever reason this usage seems to have stuck. Open weights is definitely a better name. I assume the reason "open source" has stuck is because you can download and use it for free, but "open source" was always intended to be about "free as in speech", not "free as in beer". That said, I remember when the term "open source" was invented, and it was always a bit different, more commercially aligned, than the goals of the FSF.
> But even if I can't build it from source locally, being able to see what went into the model is an important part of what open source is about.
True. Unfortunately LLMs have become such a big money and closed enterprise (the opposite of OpenAI and Anthropic's altruistic founding principles) that it's hard to see these commercial models releasing their training data, especially since this data is the closest thing they have to a moat other than the cost of training.
The most valuable training data right now seems to be "reasoning data", and the need for this at least may disappear as AI moves beyond pre-trained language models to smarter systems capable of learning for themselves, and that can actually reason, not need to parrot reasoning data.
I don't see an inconsistency. money is pragmatic, the mission needs money
The real mission statement for most companies is to make as much money as possible.
Markets don't run on altruism.
Wikipedia is altruistic, and serves humanity quite well.
Contributing to it might not necessarily be. Most open source development is funded by large companies after all and from their perspective it can function as a cost saving measure. Allowing them to focus on their core products and removing the possibility of their rivals from getting a competitive advantage due to having a superior low level stack under their product.
Which is why open source is so successful in areas where software is a cost-center but mostly failed for consumer products (since spending resources on them would actually be altruistic unlike e.g. Linux kernel development)
any altruistic act can be perceived as self serving
Software engineers need money to survive. If they exclusively work on open source stuff where are they getting money from to survive? Follow the money trail… even a donation… eventually it leads to an incentive based source or action.
From open source. You can earn money from open source. Open source is not opposed to capitalism, idk where you got that idea.
I said open source is derivative to capitalism. Meaning open source cannot exist without capitalism. I never said they oppose each other.
Second I said you need to follow the money trail. Money given to people who work on open source comes from non-open source places.
Are you not implying below, with your words, that working exclusively on open source cannot bring money?
> If they exclusively work on open source stuff where are they getting money from to survive?
You seem to imply that open source is incompatible with making money. You seem to believe that if someone is making money they are not doing "exclusively open source" but something else in addition to open source.
> you need to follow the money trail. Money given to people who work on open source comes from non-open source places.
Money spent on coffee comes from non-coffee places, mostly. Does that mean one cannot make money exclusively selling coffee?
I get your point, it is very uncommon to live only of open source. That I can agree with. It is the exaggeration and dogmatism that is untrue.
You’re right. One of its big uses is for big companies to destroy the ability of individual developers or small companies to make a living selling software. It’s also used to harm larger competitors whose revenue depends on some non open source software. Essentially, open source is the new “dumping”.
That’s not to say open source is bad. But in a capitalist context, it’s certainly a very double-edged sword.
These are orthogonal. One can have a paid job while contributing to open source for entirely altruistic reasons.
> Follow the money trail… even a donation… eventually it leads to an incentive based source or action.
BS. Humans do things for altruistic reasons devoid of individual reward all the time.
I, myself, maintain multiple OSS projects entirely for fun and with the hope that others will find it useful. That's it, that's all. I also donate entirely anonymously to charities simply because I believe others deserve support and dignity.
This form of cold, American libertarianism you espouse is pure poison in the body politic, both in this US and globally. It degrades all of human interaction to transactions. Its no wonder that the US is where sociopaths like Zuck were birthed.
Any individual that provides free labor cannot survive off of said free labor. He must work for money to survive or get donations from someone who earned that money from incentive based labor in order to even buy the food he needs to exist as a living human being. Much of the time that labor is actually closed source.
This is a logistical reality. A lot of open source advocates are unable to get their brains out of the whole mentality that open source literally cannot exist without incentive based software supporting it. Who pays for GitHub to exist? Who pays for the food swes eat? I just code for open source all day and money falls out of the sky.
My smart friend says there are jobs that pay you to work on open source exclusively. Smart guy. In this case you follow the money trail. How does that company get enough money to pay a guy to work exclusively on open source?
Put it another away: if we removed your ability to do incentivized labor and all you can do is charity work… you would run out of money and die from starvation. If we did the opposite and we removed your ability to do charity work… you’d be fine.
All of this re-emphasizes the point of this thread: In our objective reality, the world is driven by incentive based work while altruism is a side effect of surplus wealth generated by incentive based work. That is the fundamental reality.